nils-herrmann/clean_scirepeval huggingface dataset:
Improving biomedical information retrieval by linear combinations of different query expansion techniques
Venue: BMC Bioinformatics
Year: 2016

BackgroundBiomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user's needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user's information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries.ResultsThe retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 even it outperforms a previous study by 7.12 .ConclusionsWe propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher quality search results.
Brain Medical Image Fusion Based on Dual Branch CNNs in NSST Domain
Venue: BioMed research international
Year: 2020

Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a research challenge. In this paper, a new framework for medical image fusion is proposed which combines convolutional neural networks (CNNs) and non subsampled shearlet transform (NSST) to simultaneously cover the advantages of them both. This method effectively retains the functional information of the CT image and reduces the loss of brain structure information and spatial distortion of the MRI image. In our fusion framework, the initial weights integrate the pixel activity information from two source images that is generated by a dual branch convolutional network and is decomposed by NSST. Firstly, the NSST is performed on the source images and the initial weights to obtain their low frequency and high frequency coefficients. Then, the first component of the low frequency coefficients is fused by a novel fusion strategy, which simultaneously copes with two key issues in the fusion processing which are named energy conservation and detail extraction. The second component of the low frequency coefficients is fused by the strategy that is designed according to the spatial frequency of the weight map. Moreover, the high frequency coefficients are fused by the high frequency components of the initial weight. Finally, the final image is reconstructed by the inverse NSST. The effectiveness of the proposed method is verified using pairs of multimodality images, and the sufficient experiments indicate that our method performs well especially for medical image fusion.
The BIAS map: behaviors from intergroup affect and stereotypes.
Venue: Journal of personality and social psychology
Year: 2007

In the present research, consisting of 2 correlational studies (N 616) including a representative U.S. sample and 2 experiments (N 350) the authors investigated how stereotypes and emotions shape behavioral tendencies toward groups, offering convergent support for the behaviors from intergroup affect and stereotypes (BIAS) map framework. Warmth stereotypes determine active behavioral tendencies, attenuating active harm (harassing) and eliciting active facilitation (helping) Competence stereotypes determine passive behavioral tendencies, attenuating passive harm (neglecting) and eliciting passive facilitation (associating) Admired groups (warm, competent) elicit both facilitation tendencies; hated groups (cold, incompetent) elicit both harm tendencies. Envied groups (competent, cold) elicit passive facilitation but active harm; pitied groups (warm, incompetent) elicit active facilitation but passive harm. Emotions predict behavioral tendencies more strongly than stereotypes do and usually mediate stereotype to behavioral tendency links.
From paper to electronic dictionaries: Evolving dictionary skills
Year: 2013

Successful dictionary use depends on two factors: (1) user friendliness of dictionaries and (2) good dictionary reference skills of their users. As the world moves from paper to electronic dictionaries, we need to realize that the skills needed to use modern digital dictionaries are not necessarily identical to those for traditional print dictionaries. Some print dictionary skills are transferable to the context of digital products, whereas some other skills are no longer relevant, often because electronic dictionaries may now be able to do part of what used to be the user's job (such as automatically reducing an inflected form to a citation form) On the other hand, using electronic dictionaries may require new types of reference skills, not known from paper dictionaries. The most salient group of new skills is related to searching for information, and may be subsumed under the more general concept of digital literacy. In the present contribution I consider which paper dictionary skills are still relevant for digital dictionaries, which are obsolete, and what types of skills are new to electronic dictionaries.
Morphology before phonology: A case study of Turoyo (Neo Aramaic)
Year: 2020

Some models of the morphology phonology interface take (certain aspects of) morphology and phonology to be computed in the same component of the grammar, simultaneously, including many instantiations of Optimality Theory (McCarthy and Prince 1993a,b, Kager 1996, Hyman and Inkelas 1997, Mascaro 2007, Wolf 2008, i.a. On the other hand are models that separate morphology from phonology, including Distributed Morphology (Halle and Marantz 1993, 1994) and related models (e.g. Trommer 2001, Bye and Svenonius 2012, Dawson 2017, Rolle 2020) as well as "subcategorization" based approaches (Paster 2006, 2009, Yu 2007, i.a. I undertake a careful study of the order of operations needed to derive the form of finite verbs in the Neo Aramaic language Turoyo (Jastrow 1993) Two morphophonological phenomena found in Turoyo verbs provide evidence for a separation of morphology from phonology: (i) phonologically conditioned suppletive allomorphy that is anti optimizing and surface opaque (reaffirming the findings of Paster 2006) and (ii) phonological displacement of an affix (a la infixation) that is also anti optimizing and surface opaque, and even more surprisingly, counterbleeds morphological operations in the verbal complex but feeds/bleeds phonological ones. The main conclusion from Turoyo is that exponent choice precedes, and is oblivious to, the regular phonology of the language and considerations of phonological optimization. Turoyo also provides a more general window into a number of issues at the morphology phonology interface, including cyclicity, the timing of infixation, and constraints on allomorphy.
Elevated ACE 2 expression in the olfactory neuroepithelium: implications for anosmia and upper respiratory SARS CoV 2 entry and replication
Venue: European Respiratory Journal
Year: 2020

The ongoing outbreak of coronavirus disease 2019 (COVID 19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) has become a major threat to global health  The mechanism of cellular entry by SARS CoV 2 is through binding to angiotensin converting enzyme 2 (ACE 2) [2, 3] a metalloproteinase ectoenzyme that primarily functions in the regulation of angiotensin II, but also has non catalytic roles such as intestinal neutral amino acid transport. The level of ACE 2 protein and its subcellular localisation in the respiratory tract may be a key determinant of susceptibility to infection, symptoms and outcomes in COVID 19. In humans, ACE 2 protein is broadly expressed in the lung, kidney and small intestine  Pathological analysis of COVID 19 post mortem samples shows substantial damage in the lung [5] suggesting that the airway is the principal entry and target of SARS CoV 2. However, analysis of multiple single cell RNA seq datasets reveal overall low ACE 2 RNA transcription in nasal airway epithelium, with further reduced expression in lower airway club cells and rare expression in alveolar epithelial cells  This pattern of ACE 2 expression provides evidence that the upper, rather than the lower, airway is the initial site of SARS CoV 2 infection. ACE2 protein is expressed at high levels in the human olfactory epithelium relative to upper airway epithelial cells. This may explain COVID 19 associated olfactory dysfunction, and suggests a SARS CoV 2 reservoir site and potential intranasal therapy.
Promotion Tenure Reform Workgroup Report
Year: 2017

The remit of the Open Scholarship Initiative A 2017 Promotion Tenure Reform workgroup clearly connected researchersaEUR(tm) personal publishing choices to the oft traditionalist system of promotion and tenure in the United States, wherein researchers feel compelled to publish in toll access journals or monographs if they wish to achieve tenure, win grants, receive awards, or otherwise advance professionally. Other professional advancement systems worldwide, such as university hiring, contract renewals and government and foundation grantmaking processes similarly reinforce the primacy of toll access research formats. Hiring practices were of concern for our workgroup, given the increasing aEURoeadjunctificationaEUR and precarious state of tenured university posts in the United States. Due to these parallels, the Reform working group expanded our charge to consider hiring, grants, and other professional advancement scenarios common to researchersaEUR(tm) concerns worldwide.In this report, we unpack how professional advancement practicesaEUR"including and beyond promotion and tenure review standardsaEUR"can be realigned to encourage researchersaEUR(tm) adoption of open access (OA) open research, and open educational practices.Here, we set the scope of the current problem, discuss the reasons why professional advancement scenarios should be realigned to reward open research practices, identify challenges to reforming professional advancement scenarios wholesale and worldwide, recommend concrete actions for beginning the reformation process, and share resources related to professional advancement and open access.
Privacy Preserving Ranked Multi Keyword Context Sensitive Fuzzy Search Over Encrypted Cloud Data
Year: 2018

The privacy preserving search feature is very useful for a cloud user to retrieve the desired encrypted documents easily, securely and cost effectively in the cloud. However, a search query issued by the user may sometimes have mis typos i.e. wrongly typed words. The mis typos could occur because of the addition or drop of letter(s) from the word or by swapping of characters in a word. At times such mis typos may have many spelling suggestions against them within a given threshold, of which only a few makes sense as per the context of the query. Also the mistyped word may sometimes result in another valid word from the dictionary or the word list and hence the mis spelt word may go unnoticed. Recent works in cloud address the issue of fuzzy search. However, these approaches do not suggest a word suitable as per the context and co occurrence with other words of the query for such mis typos. This paper presents a privacy preserving scheme 'Context Sensitive Fuzzy Search' (CSFS) in a cloud computing environment that address these issues. CSFS uses Levenshtein distance and neighbor co occurrence statistics computed from encrypted query click logs and suggest(s) word(s) from the generated suggestions and set distance as per their co occurring frequency with other words of the query. The results achieved show that the spelling suggestions listed are as per the context of the query and have high recall value.
Position and Vector Detection of Blind Spot motion with the Horn Schunck Optical Flow
Venue: ArXiv
Year: 2016

The proposed method uses live image footage which, based on calculations of pixel motion, decides whether or not an object is in the blind spot. If found, the driver is notified by a sensory light or noise built into the vehicle's CPU. The new technology incorporates optical vectors and flow fields rather than expensive radar waves, creating cheaper detection systems that retain the needed accuracy while adapting to the current processor speeds.
Bags of Tricks and A Strong Baseline for Deep Person Re identification.
Year: 2019

This paper explores a simple and efficient baseline for person re identification (ReID) Person re identification (ReID) with deep neural networks has made progress and achieved high performance in recent years. However, many state of the arts methods design complex network structure and concatenate multi branch features. In the literature, some effective training tricks are briefly appeared in several papers or source codes. This paper will collect and evaluate these effective training tricks in person ReID. By combining these tricks together, the model achieves 94.5% rank 1 and 85.9% mAP on Market1501 with only using global features. Our codes and models are available in Github.
PGPR Isolates from the Rhizosphere of Vegetable Crop Momordica charantia: Characterization and Application as Biofertilizer
Year: 2017

Plant Growth Promoting Rhizobacteria (PGPR) are a group of naturally occurring soil bacteria that aggressively colonize plant roots and increase plant growth and yield (Wu et al. 2005) They prevent the colonization of deleterious rhizospheric microorganisms (DRMOs) at micro niches and can promote plant growth directly or indirectly. It is interesting to study the diverse bacterial taxa for their PGPR characteristics (Lucy et al. 2004) The numbers of PGPR species has increased due to studies on a wide range of plant species, advancement in bacterial taxonomy and progress in understanding the different mechanisms of action of PGPR. The increased number of PGPR for various crops and vegetables has been commercialize as a biofertilizer, recently includes Pseudomonas (Loper et al. 2007) Azospirillum (Cassan and Garcia Salamone, 2008) Bacillus (Jacobson et al. 2004) Stenotrophomonas (Ryan et al. 2009) Rhizobium (Long, 2001) Serratia (De Vleeschauwer and Hofte, 2007) Azotobacter, Klebsiella, Enterobacter, Alcaligens, Arthrobacter, Burkholderia (Joseph et al. 2007) and Streptomyces (Schrey and Tarkka. International Journal of Current Microbiology and Applied  (2017)  http:/www.ijcmas.com
Comprar Paediatric symptom sorter El Radhi, A. Sahib 9781846194740 Radcliffe Publishing
Year: 2011

Tienda online donde Comprar Paediatric symptom sorter al precio 62,68 EUR de El Radhi, A. Sahib, tienda de Libros de Medicina, Libros de Enfermeria Enfermeria en pediatria
Knowledge Graph Embedding Based Question Answering
Venue: WSDM
Year: 2019

Question answering over knowledge graph (QA KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective Knowledge Embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e. simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely adopted benchmark demonstrate that the proposed KEQA outperforms the state of the art QA KG methods.
Model based hierarchical reinforcement learning and human action control
Venue: Philosophical Transactions of the Royal Society B: Biological Sciences
Year: 2014

Recent work has reawakened interest in goal directed or 'model based' choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model based mechanisms might play a special and pivotal role in human decision making, dramatically extending the scope and complexity of human behaviour.
Skin integrated wireless haptic interfaces for virtual and augmented reality
Venue: Nature
Year: 2019

Traditional technologies for virtual reality (VR) and augmented reality (AR) create human experiences through visual and auditory stimuli that replicate sensations associated with the physical world. The most widespread VR and AR systems use head mounted displays, accelerometers and loudspeakers as the basis for three dimensional, computer generated environments that can exist in isolation or as overlays on actual scenery. In comparison to the eyes and the ears, the skin is a relatively underexplored sensory interface for VR and AR technology that could, nevertheless, greatly enhance experiences at a qualitative level, with direct relevance in areas such as communications, entertainment and medicine1,2. Here we present a wireless, battery free platform of electronic systems and haptic (that is, touch based) interfaces capable of softly laminating onto the curved surfaces of the skin to communicate information via spatio temporally programmable patterns of localized mechanical vibrations. We describe the materials, device structures, power delivery strategies and communication schemes that serve as the foundations for such platforms. The resulting technology creates many opportunities for use where the skin provides an electronically programmable communication and sensory input channel to the body, as demonstrated through applications in social media and personal engagement, prosthetic control and feedback, and gaming and entertainment. Interfaces for epidermal virtual reality technology are demonstrated that can communicate by programmable patterns of localized mechanical vibrations.
Automated Self Driving Car Following Lane with Emergency Braking System
Venue: ICCA
Year: 2020

Technological development has created huge paradigm shift over the years. For ensuring maximum productivity, machines are being used. As part of the process, this paper presents a model of self driving car which follows lane while driving and modified with Advance Braking System. This car is capable of taking autonomous decision while driving in the road so that it does not clash with front object and drives following the lane properly. To implement this, raspberry pi 3 model B+ pi camera version 2.1, Arduino Uno, Bluetooth receiver, sonar sensor, 2 wheel, 2 motor and 1 motor driver are used for hardware related work. Open CV, python, Arduino are used for software related work. The car has used image processing RGB to HSV conversion technique for operating. In the experimental evolution, the model has successfully operated while driving.
Development of a BTB /TBA+ ion paired dye based CO2 indicator and its application in a multilayered intelligent packaging system
Venue: Sensors and Actuators B: Chemical
Year: 2019

Many visual indicators have been developed to provide product quality information through their incorporation into intelligent food packaging systems. In particular, the colorimetric changes undergone by the pH sensitive dyes that are present in food packages can be used to monitor food quality without destroying the package. However, some difficulties remain in incorporating these dyes into commercial packaging materials because the dyes are often strongly hydrophilic, which leads to the migration of these dyes into food with high water contents through simple contact; additionally, these dyes do not easily mix with the hydrophobic polymers of packaging materials. To overcome these problems, we prepared a bromothymol blue (BTB /tetrabutylammonium (TBA+ ion paired dye and used it to produce a CO2 sensitive intelligent packaging film. Branched polyethylenimine was added to the indicator solution to improve the film's sensitivity to CO2. The formation of ion pairs was confirmed through Fourier transform infrared spectroscopy, and film characteristics, such as color changes in the presence of CO2 and thermal properties, were examined. Finally, we manufactured multilayered packaging films using the BTB /TBA+ ion paired dye and used these to measure CO2 changes in kimchi packaging during the fermentation process. CO2 mediated color changes were easily visible to the naked eye, demonstrating the applicability of our sensor in commercial food packaging films.
Source localization in underwater waveguides using machine learning
Year: 2016

The problem of underwater acoustic source localization is solved using an artificial neural network (ANN) under the machine learning framework. Source localization in a waveguide is posed as a classification problem by training a feed forward neural network (FFN) with one hidden layer. In this paper, the acoustic pressure signals received by a vertical linear array are preprocessed by constructing the normalized cross spectral density matrices (CSDM) which are used as input of the FFN. Each unit of the output layer represents one possible range (Here, for simplicity, source range is the only parameter that needs to be determined) Different from model based localization methods such as matched field processing (MFP) ANN as a data driven method can learn features from real acoustic data, thereby bypassing the sound propagation modeling step completely. Simulations show that FFN achieves a good performance in determining the source ranges even with deficient training data samples and low signal to noise r.