Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of polarization diversity using an electromagnetic spatio-temporal channel model

A spatio-temporal channel model based on EM scattering is presented where the properties of the transmit and receive antennas are included. The direction and the amplitude of the electromagnetic field are also included in the model instead of using just an amplitude. These directional properties are then used to analyze the polarization diversity in a propagation scenario with scattering objects of simple shapes. A vector RAKE receiver for this scenario is employed and the resulting BER is related to physical parameters of the propagation environment. It is found that a substantial improvement in BER is possible by exploiting polarization diversity and that the result is insensitive to the orientation of the base station antennas.
A Simplified Model for the Evaluation of the Rheological Properties of a Suspension of Solids in a Power-Law Fluid

Abstract An effort is made to determine theoretically the new rheological properties of a rheologically defined non-Newtonian fluid as a result of the addition of particles in the original fluid. A theoretical model is proposed to determine the rheological properties of a dilute suspension of infinite-length round particles for a Power-Law fluid, which is treated as a homogeneous fluid with new rheological properties. The equations of a two-dimensional, dilatational, creeping, steady-state flow of a near-Newtonian fluid around a solid circular particle are developed and solved. The solution is obtained by computation of the dissipation relation and is based on the change of the shear rate in a shear flow of the suspension. The model is solved numerically. The resulting from the particle addition fluid is found to be more shear-thinning in comparison to the original. Experimental evidence from the literature supports the soundness of the present findings.
“Late dividends of the British Empire”: Language ideologies and the native/non-native question in online newspaper comments

Abstract As the use of English as a lingua franca increases in a range of contexts, one question that has received recent media attention is that of whether native or non-native speakers are more effective communicators in these contexts. The native/non-native question resists a straightforward answer, but taking account of the views of people in the business world is a necessary step towards understanding the underlying issues. This article investigates the nature and origin of these views by analysing online newspaper comments written in response to a column in the Financial Times. It first identifies several topics related to the native/non-native question, including perceived differences between and within the two categories. It then discusses these topics from a language-ideological perspective, aiming to identify the patterns of beliefs and assumptions that inform the comments. Although this perspective involves a critical evaluation of the binary “native/non-native” opposition, the article identifies several important effects of the native speaker concept, ranging from outright discrimination to feelings of frustration and inhibition. It portrays the comments as both reflecting and questioning the ideological premises of the native speaker concept, and it considers the implications of the approach for ELF research and for the wider study of international communication.
Is intraabdominal pressure a predictive factor for mortality and morbidity in abdominal trauma?

Abstract The clinical signs and symptoms of an acute in increase intraabdominal pressure (IAP) are subtle, especially in the conditions of the polytraumatized patient. Thus, abdominal compartment syndrome (ACS) can brutally occur and can have a major impact on the body’s main organs and systems. The purpose of our research was to identify the influence of intraabdominal pressure, intra-abdominal hypertension, and abdominal compartment syndrome, in the evolution of polytraumatized patients. Our study analyzed the patients admitted in the IIIrd Department of General Surgery of University Emergency Hospital in Bucharest between 1st of January 2010 and 31st of December 2018. The value of intraabdominal pressure, on admission, correlated with the risk of IAH/ ACS in patients with abdominal trauma - being major causes of morbidity and mortality. IAP monitoring should become a mandatory part of the management plan for patients with abdominal trauma.
Teaching for Transfer in the Statistics Classroom

 Cognitive Transfer – Foundational issue to Western education paradigm – pertains to " how knowledge in one situation applies (or fails to apply) in other situations " (Singley & Anderson, 1989) – Knowledge from one context reaching out to enhance another (Perkins & Salomon, 1988)  Many analogous terms and topics are studied throughout learning and psychology literature – expertise, synthesis, understanding, analogical reasoning – statistical thinking (e. Motivation  " Undergraduate programs in statistics should equip students with problem solving skills they can effectively apply, build on, and extend over time " – ASA Guidelines for Undergraduate Programs (November, 2014)  " Many introductory courses contain too much material, and students end up with a collection of ideas that are understood only at surface level, are not well integrated, and are quickly forgotten. " – GAISE College Report (2012)  Even students that do well in introductory statistics are unable to transfer that learning to novel applications (Ben-to Transfer  " Transfer does not take care of itself " (Perkins & Salomon, 1988) even when contexts are remarkably similar  Regardless of the distance or direction of transfer intended, successful outcomes require intentional effort  Garfield (2002) explained that statistics instructors often present concepts and procedures expecting students to develop statistical reasoning or thinking through opportunities to apply content with software and data sets, but it seems this is simply not enough  Develop rich, interconnected schema – Students arrive with an existing schema network – Work toward abstraction and flexibility of cognitive elements – Context is important  Metacognition – Sub-goals help with strategic thinking and organization – Worked examples & self-explanation promote high-road transfer – Again, context is important  Manage cognitive load – Intrinsic & extrinsic components to cognitive load (Sweller, 1994) – Automation of cognitive processes reduces burden (Sternberg, 1998)
Strategic orientations of the organization - entrepreneurial, market and organizational learning

Summary The underlying assumption of this study is that it is possible to define some principles of strategic management. These principles, called strategic orientations, influence and direct the activities of an organisation and are intended to ensure its viability and superior performance. So far, studies into strategic orientations that set out to explain different levels of organisational performance prove that each type of strategic orientation can have a positive impact on business performance. The purpose of this study is to discuss the key aspects of the three strategic orientations - entrepreneurial, market and organizational learning, outlining the relationship between them and the consequences of their choice on the development of the organization. In addressing entrepreneurial, market and learning orientations, the study has described some of the relationships between orientations and their effect on organisational performance. This has allowed us to identify areas of future studies on strategic orientations – that is to say, their integration, the effect of their configurations on organisational performance in different environments, and internal factors underlying strategic orientations.
The Searching of Relevant Query

Many internet users are increasingly search complex task-oriented goals on the internet, for example making travel arrangements and administrate finances or planning purchases. They are usually breaking down the tasks into a some codependent steps and issue multiple queries around these steps repeatedly after long time. To give a good support users in their long-term information quests on the internet, for searching purpose search engines keep track of their queries and clicks while searching online on internet. We study the problem of organizing a user past queries into groups in a dynamic and automated fashion by using some methods. Self-moving identify the query groups is helpful for a number of completely unlike search engine components and applications, query alterations, result ranking, collaborative search and sessionization. To cone nearer in that we go beyond near to that rely on like textual or time boundary value, and then we propose a more robust approach that leverages search query logs. The query reformulation and click graphs contain useful information on user behavior when searching online. In this paper, we show how such information can be used effectively for the task of organizing user search histories into the groups of query. We have to combine the two graph in query fusion graph i.e. query reformulation graph and query click graph. We specify that to show our approach that is based on probabilistic random walks over the query fusion graph outperforms time-based and keyword similarity based approaches. We also find value in combining our method with relevance keyword matching-based methods, exceptionally when there is insufficient usage information as per our queries. As per our project scope and it's future work, we have to intend investigate the usefulness of the knowledge gained from these query groups in various applications such as providing query suggestions and biasing the ranking of search
Storage Stability Of Bitumen Modified By The Addition Of Ground Rubber, Swollen SBS And Polymeric Short Fibers

Abstract Bitumen is used as binder for asphalted roads worldwide. However the service life of asphalt roads is limited due to the viscoelastic properties of bitumen. The lack of yield stress and the flow behavior at high temperatures as well as the stiffness of bitumen at lower temperatures results in the main failure sources of asphalt roads. Many polymers have been used in industry to improve the rheological behavior of bitumen and consequently, service life of roads. The polymers are commonly added to hot bitumen under stirring in order to achieve a stable dispersion. However, most polymers show little to no miscibility in bitumen, which requires long dispersion times and may lead to oxidation of bitumen and degradation of the polymers. Poor miscibility of the dispersed polymer and the bitumen matrix can result in phase separation during transport of molten bitumen, leading to a heterogeneous binder and further failure of the paved road, which is a common problem in the paving industry. Rubber pre-treating leads to a faster mixing process without compromising dispersion quality and stability. Bitumen was modified with SBS, ground rubber and chopped fibers of polymers. These fibers, which showed good stability, can be considered for the future as bitumen modifiers. To evaluate the stability of the modified bitumen, we propose a stability index.
The epidemiology and influence of osteoarthritis on clinical, laboratory and ultrasound parameters of patients with early rheumatoid arthritis

Abstract Background: Osteoarthritis (OA) and rheumatoid arthritis (RA) can overlap and the presence of OA can interfere with the evaluation of patients with RA. Objectives: The aim of this study was to evaluate the possible impact of OA on the clinical, laboratory and ultrasound parameters currently evaluated in patients with early RA (ERA). Methods: We have evaluated the data obtained from patients with ERA referred to our Early Arthritis Research Center (EARC). Only data from patients who fulfilled EULAR/ ACR 2010 criteria for RA and had symptom duration of less than 12 months were analyzed. All patients underwent clinical examination, laboratory tests and ultrasound (US) examination. Results: There was a clear predominance of women (62.8%). The mean age was 55.47±13.71 years. At baseline, 21 patients (48.8%) were diagnosed with OA. Hand OA did not influence the values of any of the parameters assessed (p>0.05). For patients with knee OA, significantly higher values were observed only for DAS28 at baseline (p=0.018) as well as after 12 months of observation (p=0.031). Conclusions: Significantly higher values of DAS28 were observed in patients with ERA who associated knee OA, while the values of SDAI were not influenced, suggesting that SDAI may be superior to DAS28 in evaluating patients with ERA and knee OA. The values of patient’s VAS were not influenced by the presence of hand or knee OA suggesting that these types of OA do not influence the patients’ perception of the disease activity. Moreover, the values of ultrasound scores were not influenced by the presence of OA.
Impact of Aspect Ratio of Carbon Nanotubes on Shear and Extensional Rheology of Polyethylene Nanocomposites

Abstract This paper reports the results of studies on the effect of aspect ratio of multiwall carbon nanotubes (CNT) on the shear and extensional rheological behavior of low density polyethylene (LDPE) nanocomposites. Up to a CNT loading of 2wt%, as used in the present study, the shear rheological data suggest no network formation in the nanocomposites, irrespective of the aspect ratio of the nano filler. Dynamic shear viscosity ή increases with increase in loading and aspect ratio of CNT. However, at low CNT loadings (0.1 wt%) and with CNT of high aspect ratio, ή for the nanocomposites is found to be lower than that of neat polymer. Steady shear rheology results show negative values for the normal stress for the high aspect ratio CNT which is believed to be due to the tumbling of CNT with high aspect ratio. Results of extensional viscosity measurements show that extent of strain hardening is dependent on the CNT aspect ratio and follows the order, high aspect ratio >medium aspect ratio > short aspect ratio, while the time of break follows the reverse order. The effect of aspect ratio on critical extensional stress becomes prominent only at the high aspect ratio, but the stress increases with the increase in CNT loading, irrespective of the aspect ratio.
Segmentation of EM Images of Neuronal Structures Using CellProfiler

CellProfiler is a flexible, open-source tool designed to analyze microscopy images. CellProfiler's primary focus is on analysis of fluorescently labeled cells, but it can be adapted to other uses. This poster describes the analysis of the ISBI 2012 EM challenge images using CellProfiler in conjunction with custom software written for the challenge. The images are first scored with a custom pixel-based classifier trained on the ground truth. This scoring is then used as the input for CellProfiler. I first identify potential centers within tissue surrounded by membrane, and then grow these using a seeded watershed. Finally, I use a module that was custom-designed for the challenge to clean up artifacts in the resulting segmentation. The CellProfiler EM Challenge entry demonstrates techniques for analyzing neuronal structures. It also demonstrates how CellProfiler can be used as an algorithm development platform. CellProfiler can run ImageJ and ImageJ 2.0 plugins written in Java as well as native modules written in Python. An analysis method can be distributed as a CellProfiler pipeline, contributing to the reproducibility and documentation of the method and allowing researchers to easily integrate both their algorithms and others to arrive at a production-quality and scale solution.
Proceedings of the 1st ACM workshop on Patent information retrieval

We welcome you to PaIR 08, the 1st workshop on patent information retrieval, organized by the Information Retrieval Facility (IRF) and Matrixware Information Services. Patent Information Retrieval is an active and challenging field both for researchers and for professional information specialists. Patents play a key role not only in protecting intellectual property but also as a strategic business factor in all modern economies. Despite the enormous advances in Information Retrieval techniques in the past few years, advanced search tools for patent professionals are still in their infancy, so the research in patent retrieval represents an important opportunity for research. Patent search is a particular challenge to information retrieval and access systems. Amongst the challenges successful patent search of search system of the future will have to face are; very large numbers of highly complex structured documents; highly heterogeneous document collections (scientific papers, legal public disclosure as well as patents); multiple languages; ambiguous and conflicting jargon; complex technological concepts; sophisticated legal jargon; ranges and other complex query forms tracking temporal issues like publication data tabular and graphical information mixed into text; and so on. The objective of the workshop is to provide a forum for Information Retrieval and Knowledge Management scientists as well as Patent Retrieval experts from industry to study the next generation of patent search tools. The workshop presents a balanced mixture of IP professionals, presenting their special information needs and IR&KM researchers, presenting relevant technical ideas, for example for high recall search in prior art searching or new approaches to patent and technology landscape searching. We hope the workshop will be a springboard for many future events and lead to the recognition of patent searching as one of the central areas of research in Information Retrieval and Knowledge Management. The number of submitted papers, their quality and the variety of topics they explored show that Patent Information Retrieval is a multidisciplinary field. The program committee accepted seven full papers that cover topics like large-scale patent annotations, chemical IR, patent search strategies or ideas on measuring the progress of system performance for retrieval tasks in the intellectual property domain. Because we had many more high quality papers submitted than could be presented orally we decided to also give a number of other authors the opportunity to present their work as posters.
Tracking system of Mine Patrol Robot for Low Illumination Environment

Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, tracking in low-illumination environments is a challenging problem, not yet successfully addressed in the literature. In this work, we tackle the problems by introducing Low-Illumination Long-term Correlation Tracker (LLCT). First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard PCA to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, update memory templates with interval updates, then re-match existing and initial templates every few frames to maintain template accuracy. The extensive experiments on popular Object Tracking Benchmark OTB-50 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance. In addition, the proposed approach can be integrated easily in robot system and the running speed performed well. The experimental results show that the novel tracker performance in low-illumination environment is better than that of general trackers.
Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks

The objective is to test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction or generalization of independently varying active and passive states. We use deep learning to investigate the generalizable content of two-dimensional (2D) US muscle images. US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle, were recorded from 32 healthy participants (seven female; ages: 27.5, 19–65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, drift-free components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous independent variation of passive (joint angle) and active (electromyography) inputs. For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography and joint moment were estimated to accuracy 55 ± 8%, 57 ± 11% and 46 ± 9%, respectively. With 2D US imaging, deep neural networks can encode, in generalizable form, the activity–length–tension state relationship of these muscles. Observation-only, low-power 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalized muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing.
High-Throughput Machine Learning from Electronic Health Records

The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models have previously been constructed for a few important diseases, such as breast cancer and myocardial infarction, we currently know very little about how accurately the risk for most diseases or events can be predicted, and how far in advance. Machine learning algorithms use training data rather than preprogrammed rules to make predictions and are well suited for the complex task of disease prediction. Although there are thousands of conditions and illnesses patients can encounter, no prior research simultaneously predicts risks for thousands of diagnosis codes and thereby establishes a comprehensive patient risk profile. Here we show that such pandiagnostic prediction is possible with a high level of performance across diagnosis codes. For the tasks of predicting diagnosis risks both 1 and 6 months in advance, we achieve average areas under the receiver operating characteristic curve (AUCs) of 0.803 and 0.758, respectively, across thousands of prediction tasks. Finally, our research contributes a new clinical prediction dataset in which researchers can explore how well a diagnosis can be predicted and what health factors are most useful for prediction. For the first time, we can get a much more complete picture of how well risks for thousands of different diagnosis codes can be predicted.
Polarimetric study of scattering from dry snow cover in alpine areas

This paper introduces a qualitative characterization of dry snow scattering behaviour in Alpine areas. An electromagnetic scattering model is developed, based on the vector radiative transfer equation, to simulate the fully polarimetric response of a snow cover. Multi-temporal and multi-frequency polarimetric SAR data acquired over Alpine test sites are analyzed by comparing polarimetric indicators to simulated ones with various sets of snow cover characteristics.