nils-herrmann/clean_scirepeval huggingface dataset:
Large Margin Multi Modal Multi Task Feature Extraction for Image Classification
Venue: IEEE Transactions on Image Processing

The features used in many image analysis based applications are frequently of very high dimension. Feature extraction offers several advantages in high dimensional cases, and many recent studies have used multi task feature extraction approaches, which often outperform single task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi modal multi task feature extraction (LM3FE) framework for handling multi modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g. classification) An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real world image data sets demonstrate the effectiveness and superiority of the proposed method.
Ophthalmology, 3rd Edition

able quick reference. Differential lists are often then reinforced by the illustrations. It is certainly difficult for the novice to attain a comprehensive picture of neuroophthalmology. So much material must be absorbed and integrated. But a picture is worth a thousand words. The authors are much better teachers by eliminating tens of thousands of words and replacing them with illustrations. All the important topics are addressed. Subtle details may be left out, but the es.sential is here. I could imagine a resident, deciphering an OKAP question, and bringing page and illustration from this book to mind and acing the answer. This is a book not only for students, but also teachers. I plan on frequently pulling this book out in clinic and using the simple approach of this book and its illustrations to put specific patients in perspective. The authors use three reviewer quotes in their preface to this second edition. I was certainly skeptical of this dramatic, perhaps over the top, self endorsement by the selection bias. But I am now a believer. The authors can now count me as one of those gushing reviewers. This is an exceptional and superlative educational tool for all.
Seismic imaging of incomplete data and simultaneous source data using least squares reverse time migration with shaping regularization

Simultaneous source acquisition improves the efficiency of the seismic data acquisition process. However, direct imaging of simultaneous source data may introduce crosstalk artifacts in the final image. Likewise, direct imaging of incomplete data avoids the step of data reconstruction, but it can suffer from migration artifacts. We have proposed to incorporate shaping regularization into least squares reverse time migration (LSRTM) and use it for suppressing interference noise caused by simultaneous source data or migration artifacts caused by incomplete data. To implement LSRTM, we have applied lowrank one step reverse time migration and its adjoint iteratively in the conjugate gradient algorithm to minimize the data misfit. A shaping operator imposing structure constraints on the estimated model was applied at each iteration. We constructed the shaping operator as a structure enhancing filtering to attenuate migration artifacts and crosstalk noise while preserving structural information. We have carried out numerical tests on synthetic models in which the proposed method exhibited a fast convergence rate and was effective in attenuating migration artifacts and crosstalk noise.
UnitBox: An Advanced Object Detection Network
Venue: ACM Multimedia

In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the l2 loss separately. Such an oversimplified assumption is contrary to the well received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersection over Union (IoU) loss function for bounding box prediction, which regresses the four bounds of a predicted box as a whole unit. By taking the advantages of IoU loss and deep fully convolutional networks, the UnitBox is introduced, which performs accurate and efficient localization, shows robust to objects of varied shapes and scales, and converges fast. We apply UnitBox on face detection task and achieve the best performance among all published methods on the FDDB benchmark.
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimation can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is about 10 times faster, to speedup the patch wise convolutions in the deep model. With this more efficient model, we are able to design deeper networks to pursue better performance. Our proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed form solutions exist. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state of the art depth estimation approaches.
Study of Glass Fibre Reinforced Concrete

Plain concrete possess very low tensile strength, limited ductility and little resistance to cracking. Internal micro cracks are inherently present in concrete and its poor tensile strength is due to propagation of such micro cracks. Fibers when added in certain percentage in the concrete improve the strain properties well as crack resistance, ductility, as flexure strength and toughness. Mainly the studies and research in fiber reinforced concrete has been devoted to steel fibers. In recent times, glass fibers have also become available, which are free from corrosion problem associated with steel fibers. The present paper outlines the experimental investigation conducts on the use of glass fibers with structural concrete. CEM FILL anti crack, high dispersion, alkali resistance glass fiber of diameter 14 micron, having an aspect ratio 857 was employed in percentages varying from 0.33 to1 percentage by weight in concrete and the properties of this FRC (fiber reinforced concrete) like compressive strength, flexure strength, toughness, modulus of elasticity were studied.
Paradise Lost: The Neurobiological and Clinical Consequences of Child Abuse and Neglect
Venue: Neuron

In the past two decades, much evidence has accumulated unequivocally demonstrating that child abuse and neglect is associated with a marked increase in risk for major psychiatric disorders (major depression, bipolar disorder, post traumatic stress disorder [PTSD] substance and alcohol abuse, and others) and medical disorders (cardiovascular disease, diabetes, irritable bowel syndrome, asthma, and others) Moreover, the course of psychiatric disorders in individuals exposed to childhood maltreatment is more severe. Recently, the biological substrates underlying this diathesis to medical and psychiatric morbidity have been studied. This Review summarizes many of the persistent biological alterations associated with childhood maltreatment including changes in neuroendocrine and neurotransmitter systems and pro inflammatory cytokines in addition to specific alterations in brain areas associated with mood regulation. Finally, I discuss several candidate gene polymorphisms that interact with childhood maltreatment to modulate vulnerability to major depression and PTSD and epigenetic mechanisms thought to transduce environmental stressors into disease vulnerability.
Comparative evaluation of calcium silicate based dentin substitute (Biodentine(r) and calcium hydroxide (pulpdent) in the formation of reactive dentin bridge in regenerative pulpotomy of vital primary teeth: Triple blind, randomized clinical trial
Venue: Contemporary clinical dentistry

Considering the biological concerns of calcium hydroxide (CH) as a pulpotomy agent, an alternative silicate based dentin substitute i.e. Biodentine (Ca3SiO5) was evaluated clinically and radiographically.  To evaluate the effectiveness of dentin substitute (Biodentine) in regenerative pulpotomy of vital primary teeth that would giv a biological base to its use in forming reactive dentin bridge and overcoming the drawbacks of calcium hydroxide.  Randomised clinical trial on 40 bilateral carious primary molars in 20 participant children (aged 5 10 years) was carried out by same operator using Ca3SiO5(group 1) and CH (group 2) as vital pulpotomy agents. Blinded clinical and radiographic outcomes were observed at 3, 6 and 12 months interval.  Clinical outcomes of both protocols were analysed using Pearson's chi square test applied at P 0.05. Descriptive statistics were expressed as mean increase in dentin bridge formation in mms from two reference points in standardized radiographs using paired 't' test at baseline and 12 months and found to be statistically significant (P 0.05) in group 1 when compared with group 2.  Group 1 revealed statistically favourable regenerative potential along with clinical success compared to group 2 thereby sharing both indications and mode of action with CH, but without its drawbacks of physical and clinical properties.
A Greedy Approach for Optimizing the Problems of Task Scheduling and Allocation of Cloud Resources in Cloud Environment

Cloud computing is a computing technique which is an on demand mechanism having pay per usage, and service based technique where elastic and scalable IT functionalities are delivered to several user ends using world wide web (internet) as a communication medium. Distributed computing strategy, utility based approach and virtualization also play a major role to provide an efficient and efficient base for the creation of the new concept of cloud computing. It provides a critical virtual pool of associated computing resources which are accessed and used by the end users over the medium i.e. internet. After looking the revolutionary benefits and advantages of cloud computing approach, the market is going to shift from previous traditional technologies to new cloud computing based approaches and technologies. Resource allocation is a prim concern which plays an important role in many computational fields, such as in data center management and in operating systems. In cloud based systems, resource allocation part can be said as a mechanism that looks to ensure that the computing requirements of the applications are properly, completely and correctly fulfilled by the server's infrastructure. The servers which provides the services of cloud computing, keep this most important aspect in their notice that the resources are to be utilized efficiently so that they may generate maximum profit. This outputs the allocation of resources and the scheduling of tasks as a prim and core challenging issue in cloud computing. This paper comes with some effective and efficient task scheduling and resource allocation algorithms which output the optimum results while implemented in cloud computing.
Multi Criteria Decision Analysis Approaches for Selecting and Evaluating Digital Learning Objects

The wide spread of internet resources has emerged new paradigms of learning and knowledge delivery to facilitate and enhance learning particularly in e learning systems. The important milestones of these new paradigms are known as digital learning objects (DLO) which are smallest packed bits for learning. The abundance of these DLOs raises an important question on how to select effectively high quality reusable learning objects. There exist many soft computing approaches such as; fuzzy computing, neural networks, evolutionary computing, support vector machines, machine learning and probabilistic reasoning. In this paper, fuzzy multi criteria decision making methods in choosing suitable DLOs were reviewed. This paper discusses recent variety of soft computing methods used in selecting and evaluating digital learning objects through multi criteria decision analysis approaches used; for selecting metrics like basic topical, course similarity, internal topical, basic and user similarity personal, and context similarity situational relevance and for evaluation; scalarization method, employing triangular, trapezoidal and distance based similarity (adapted from TOPSIS techniques) As DLOs continue to evolve it is inevitable to utilize multi criteria techniques for selecting and or improving quality. The abundance of DLOs has increased the need for applying practical soft computing techniques to retrieve high quality, reusable DLOs.
Evaluating Digital Health Interventions

From the Research Department of Primary Care and Population Health, University College London, London, United Kingdom; Designing Health Lab, School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona; Department of Behavioural Sciences and Learning, Linkoping University, Linkoping, Sweden; Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; The Methodology Center and Department of Human Development and Family Studies, The Pennsylvania State University, State College, Pennsylvania; MRC Clinical Trial Service Unit Hub, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; NIHR MindTech HTC, University of Nottingham, Nottingham, United Kingdom; School for the Engineering of Matter, Transport, and Energy, Ira A. Fulton Schools of Engineering, Arizona State University, Phoenix, Arizona; Research Department of Epidemiology and Public Health, University College London, London, United Kingdom; and Wessex Institute, University of Southampton, Southampton, United Kingdom
Distributed acoustic monitoring to secure transport infrastructure against natural hazards requirements and new developments

The Austrian Federal Railways (OEBB) rail network operate several trans European connections, thereof approximately 20% are exposed to natural hazards. For trains, obstacles in the tracks, damage to the track bed and direct train hits are the most relevant hazard scenarios. Classical permanent protections structures are sometimes hard to realize due to technical reasons and the limited time windows available for construction work. Therefore detection technologies gain more importance as an alternative to classical protection measures. Distributed acoustic sensing (DAS) with fiber optic cables is in use worldwide for monitoring pipelines or e.g. securing industrial and military facilities. This study evaluates if reliable detection of hazardous processes in the track area is possible by using existing fiber optic communication cables. We show that processes such as rock fall or tree throws can be detected well, but due to the high sensitivity of the monitoring system, interfering signals still produce a relative high number of false alarms.
A 5 year Follow up of a Controlled Mobile Core Lumbar Disc Prosthesis Part 1: Clinical Results

Total disc replacement is an alternative to arthrodesis in degenerative disc disease in young patients suffering from persistent chronic back pain refractory to conservative treatments. The aim of this study is to assess the efficacy and safety of a mobile core lumbar prosthesis. This study reports both clinical (Part 1) and radiological (Part 2) assessments.  Four hundred and eleven patients were included in a prospective and multicenter study with 5 year follow up. The clinical assessment investigated ODI, VAS (lumbar and radicular) score, quality of life (SF 36) medication consumption, professional status, and complication occurrence after surgery.  ODI significantly decreased at 5 years after surgery with an average improvement of 27.3% Low back pain and radicular pain significantly decreased. SF 36 significantly increased for both the mental and physical components. Medication consumption also significantly decreased at 5 years. Professional status was improved with an increase in the proportion of working patients and a decrease in sick leave up to 5 years after surgery. All complications were reported with an overall reoperation rate of 14.6% (regardless the cause and the level of the lumbar spine)  This study showed satisfactory clinical results and patient satisfaction at 5 years' follow up and confirmed the safety and efficacy of this lumbar total disc prosthesis with specific controlled mobility core.  Joel Delecrin, Department of Orthopaedics, Hotel Dieu Hospital, Service de Chirurgie Orthopedique, 1 place Alexis Ricordeau 44093, Nantes France, Tel: +33 (0)2 40 08 48 45; Fax: +33 (0)22 40 08 49 08; E mail: joel.delecrin@chu nantes.fr Received November 10, 2016;  2016;   Delecrin J, Beaurain J, Dufour T, Huppert J, Allain J, et al. (2016) A 5 year Follow up of a Controlled Mobile Core Lumbar Disc Prosthesis: Clinical Results.  344.
A 5 year Follow up of a Controlled Mobile Core Lumbar Disc Prosthesis: Clinical Results

Total disc replacement is an alternative to arthrodesis in degenerative disc disease in young patients suffering from persistent chronic back pain refractory to conservative treatments. The aim of this study is to assess the efficacy and safety of a mobile core lumbar prosthesis. This study reports both clinical (Part 1) and radiological (Part 2) assessments.  Four hundred and eleven patients were included in a prospective and multicenter study with 5 year follow up. The clinical assessment investigated ODI, VAS (lumbar and radicular) score, quality of life (SF 36) medication consumption, professional status, and complication occurrence after surgery.  ODI significantly decreased at 5 years after surgery with an average improvement of 27.3% Low back pain and radicular pain significantly decreased. SF 36 significantly increased for both the mental and physical components. Medication consumption also significantly decreased at 5 years. Professional status was improved with an increase in the proportion of working patients and a decrease in sick leave up to 5 years after surgery. All complications were reported with an overall reoperation rate of 14.6% (regardless the cause and the level of the lumbar spine)  This study showed satisfactory clinical results and patient satisfaction at 5 yearsa follow up and confirmed the safety and efficacy of this lumbar total disc prosthesis with specific controlled mobility core.
A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology
Venue: Comput. Ind.

We propose a new paradigm for next generation enterprise information systems for the continuous alignment of business and IT for the agile enterprise.The metamodelling approach supports both human interpretable enterprise architecture models and machine interpretable enterprise ontologies.Semantic lifting transforms metamodels for the enterprise architectures into machine interpretable enterprise ontologies.Semantic metamodels express the semantics of all modelling concepts by an ontology. The ontology is extended by a metamodel, which defines the notation and syntax of the graphical modelling language.Examples of next generation enterprise information systems are described, which embed modelling tools and algorithms for model analysis, identification of adaptation needs, and risk assessment. The paper deals with Next Generation Enterprise Information Systems in the context of Enterprise Engineering. The continuous alignment of business and IT in a rapidly changing environment is a grand challenge for today's enterprises. The ability to react timeously to continuous and unexpected change is called agility and is an essential quality of the modern enterprise. Being agile has consequences for the engineering of enterprises and enterprise information systems. In this paper a new paradigm for next generation enterprise information systems is proposed, which shifts the development approach of model driven engineering to continuous alignment of business and IT for the agile enterprise. It is based on a metamodelling approach, which supports both human interpretable graphical enterprise architecture and machine interpretable enterprise ontologies. Furthermore, next generation enterprise information systems are described, which embed modelling tools and algorithms for model analysis.
BORO as a Foundation to Enterprise Ontology
Venue: J. Inf. Syst.

Modern business organizations experience increasing challenges in the development and evolution of their enterprise systems. Typical problems include legacy re engineering, systems integration/interoperability, and the architecting of the enterprise. At the heart of all these problems is enterprise modeling. Many enterprise modeling approaches have been proposed in the literature with some based on ontology. Few however adopt a foundational ontology to underpin a range of enterprise models in a consistent and coherent manner. Fewer still take data driven re engineering as their natural starting point for modeling. This is the approach taken by Business Object Reference Ontology (BORO) It has two closely intertwined components: a foundational ontology and a re engineering methodology. These were originally developed for the re engineering of enterprise systems and subsequently evolved into approaches to enterprise architecture and systems integration. Together these components are used to system.