nils-herrmann/clean_scirepeval huggingface dataset:
Market integration and market structure in the European soft drinks industry : always Coca-Cola ?

Market Integration and Market Structure in the European Soft Drinks Industry: Always Coca-Cola? by Catherine Matraves* This paper focuses on the question of European integration, considering whether the geographic level at which competition takes place differs across the two major segments of the soft drinks industry: carbonated soft drinks and mineral water. Our evidence shows firms are competing at the European level in both segments. Interestingly, the European market is being integrated through corporate strategy, defined as increased multinationality, rather than increased trade flows. To interpret these results, this paper uses the new theory of market structure where the essential notion is that in endogenous sunk cost industries such as soft drinks, the traditional inverse structure-size relation may break down, due to the escalation of overhead expenditures.
Semantic Complex Event Processing for Social Media Monitoring-A Survey

Semantic Complex Event Processing (CEP) is a promising approach for analysing streams of social media data in crisis situations. Traditional CEP approaches lack the capability to semantically interpret and analyse data, which Semantic CEP attempts to address, but current approaches have a number of limitations. In this paper we survey four semantic stream processing engines, and discuss them with the specific requirements of CEP for social media monitoring in mind. Current approaches assume well-structured data, known streams and vocabularies, and mainly static event patterns and ontologies, neither of which are realistic assumptions in our scenario. Additionally, the languages commonly used for event pattern detection, i.e., SPARQL extensions, lack several important features that would facilitate more advanced statistical and textual analyses, as well as adequate support for temporal and spatial reasoning. Being able to utilize external tools for processing specific tasks would also be of great value in processing social data streams.
Brain Tumour Extraction from MRI Images Using MATLAB

Medical image processing is the most challenging and emerging field now a days. Processing of MRI images is one of the part of this field. This paper describes the proposed strategy to detect & extraction of brain tumour from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumour from MRI scan images of the brain is done by using MATLAB software.
MAD Skills: New Analysis Practices for Big Data

As massive data acquisition and storage becomes increasingly a↵ordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world’s largest advertising networks at Fox Audience Network, using the Greenplum parallel database system. We describe database design methodologies that support the agile working style of analysts in these settings. We present dataparallel algorithms for sophisticated statistical techniques, with a focus on density methods. Finally, we reflect on database system features that enable agile design and flexible algorithm development using both SQL and MapReduce interfaces over a variety of storage mechanisms.
Study Cases : Ride Augmentation Bone by Grafting and PRP / Preparation for Dental Implants Placement

To clarify surgery dental implants keys necessary. From classification bone, principles of the maxillary sinus lift and use biomaterials. We’ll see, as well as Dental implants and increase the length and width split bone. Ring bone to ensure adequate supply to the process of dental implants and bone ring technique to increase bone length. A dental implant is fast becoming the treatment of choice in replacing a missing tooth. However, the success of an implant is dependent, amongst others, on the availability of adequate alveolar bone to house the implant, something which is not always there. With such demand, the need for augmenting the bone has risen and along with that, the enhancement of the bone grafting. Plateletrich plasma was one such technique introduced twenty years ago as a method to improve the healing of bone grafts. Dental implants were placed in subjects divided into two groups and four subgroups on basis of implant treatment with PRP and implant surface topography used. A total of 30 implants were placed, 15 in each group. For PRP group, implants were placed after surface treatment with PRP. Temporization was done within two weeks and final prosthesis was given after three months. Implant stability was measured with Periotest at baseline, one month and three months. Bone height was measured on mesial and distal side on standardized x-rays. A statistically significant difference was noticed in implant stability with PRP at baseline. The effect of PRP on bone height changes was not statistically significant. A synergistic effect of PRP and square thread-form was observed on improved implant stability and bone levels; however, no such effect is seen with PRP and reverse buttress thread-form. I will present here about thirty operations surgery: 1. Maxillary sinus lift 2. Split and the ring chin technology. 3. The use of biomaterials as bone graft and platelet-rich plasma PRP
Age Constraints on Second-Language Acquisition

This study evaluated the critical period hypothesis for second language (L2) acquisition. The participants were 240 native speakers of Korean who differed according to age of arrival (AOA) in the United States (1 to 23 years), but were all experienced in English (mean length of residence 5 15 years). The native Korean participants’ pronunciation of English was evaluated by having listeners rate their sentences for overall degree of foreign accent; knowledge of English morphosyntax was evaluated using a 144-item grammaticality judgment test. As AOA increased, the foreign accents grew stronger, and the grammaticality judgment test scores decreased steadily. However, unlike the case for the foreign accent ratings, the effect of AOA on the grammaticality judgment test scores became nonsignificant when variables confounded with AOA were controlled. This suggested that the observed decrease in morphosyntax scores was not the result of passing a maturationally defined critical period. Additional analyses showed that the score for sentences testing knowledge of rule based, generalizable aspects of English morphosyntax varied as a function of how much education the Korean participants had received in the United States. The scores for sentences testing lexically based aspects of English morphosyntax, on the other hand, depended on how much the Koreans used English.
The Price of Robustness

A robust approach to solving linear optimization problems with uncertain data was proposed in the early 1970s and has recently been extensively studied and extended. Under this approach, we are willing to accept a suboptimal solution for the nominal values of the data in order to ensure that the solution remains feasible and near optimal when the data changes. A concern with such an approach is that it might be too conservative. In this paper, we propose an approach that attempts to make this trade-off more attractive; that is, we investigate ways to decrease what we call the price of robustness. In particular, we flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations. An attractive aspect of our method is that the new robust formulation is also a linear optimization problem. Thus we naturally extend our methods to discrete optimization problems in a tractable way. We report numerical results for a portfolio optimization problem, a knapsack problem, and a problem from the Net Lib library.
Investor protection and corporate governance q

Recent research has documented large di!erences among countries in ownership concentration in publicly traded "rms, in the breadth and depth of capital markets, in dividend policies, and in the access of "rms to external "nance. A common element to the explanations of these di!erences is how well investors, both shareholders and creditors, are protected by law from expropriation by the managers and controlling shareholders of "rms. We describe the di!erences in laws and the e!ectiveness of their enforcement across countries, discuss the possible origins of these di!erences, summarize their consequences, and assess potential strategies of corporate governance reform. We argue that the legal approach is a more fruitful way to understand corporate governance and its reform than the conventional distinction between bank-centered and market-centered "nancial systems.
Asset Pricing with Liquidity Risk ∗ Viral

This paper studies equilibrium asset pricing with liquidity risk — the risk arising from unpredictable changes in liquidity over time. It is shown that a security’s required return depends on its expected illiquidity and on the covariances of its own return and illiquidity with market return and market illiquidity. This gives rise to a liquidityadjusted capital asset pricing model. Further, if a security’s liquidity is persistent, a shock to its illiquidity results in low contemporaneous returns and high predicted future returns. Empirical evidence based on cross-sectional tests is consistent with liquidity risk being priced. We are grateful for conversations with Andrew Ang, Joseph Chen, Sergei Davydenko, Francisco Gomes, Joel Hasbrouck, Andrew Jackson, Tim Johnson, Martin Lettau, Anthony Lynch, Stefan Nagel, Dimitri Vayanos, Luis Viceira, Jeff Wurgler, and seminar participants at London Business School, New York University, the National Bureau of Economic Research (NBER) Summer Institute 2002, and the Five Star Conference 2002. We are especially indebted to Yakov Amihud for being generous with his time in guiding us through the empirical tests. All errors remain our own. Acharya is at London Business School and is a Research Affiliate of the Centre for Economic Policy Research (CEPR).    Pedersen is at the Stern School of Business, New York University, 44 West Fourth Street, Suite 9-190, New York, NY 10012-1126.  (212) 998-0359.  (212) 995-4233.
Skills and Characteristics of Successful Cybersecurity Advocates

Cybersecurity advocates attempt to counter the tsunami of cyber attacks by promoting security best practices and encouraging security technology adoption. However, little is known about the skills necessary for successful advocacy. Our study explores the motivations, characteristics, and practices of cybersecurity advocates. Preliminary analysis of 19 interviews reveals that effective advocates must not only possess technical and soft skills, but also customer service orientation and context awareness. However, little cybersecurity training is available to develop these non-technical skills. Additionally, the cybersecurity profession neglects to frame the field as service-oriented, a theme identified repeatedly in our interviews. We discuss implications of these findings for recruitment and greater workforce diversity.
Deep Reinforcement Learning for Visual Object Tracking in Videos

Convolutional neural network (CNN) models have achieved tremendous success in many visual detection and recognition tasks. Unfortunately, visual tracking, a fundamental computer vision problem, is not handled well using the existing CNN models, because most object trackers implemented with CNN do not effectively leverage temporal and contextual information among consecutive frames. Recurrent neural network (RNN) models, on the other hand, are often used to process text and voice data due to their ability to learn intrinsic representations of sequential and temporal data. Here, we propose a novel neural network tracking model that is capable of integrating information over time and tracking a selected target in video. It comprises three components: a CNN extracting best tracking features in each video frame, an RNN constructing video memory state, and a reinforcement learning (RL) agent making target location decisions. The tracking problem is formulated as a decision-making process, and our model can be trained with RL algorithms to learn good tracking policies that pay attention to continuous, inter-frame correlation and maximize tracking performance in the long run. We compare our model with an existing neural-network based tracking method and show that the proposed tracking approach works well in various scenarios by performing rigorous validation experiments on artificial video sequences with ground truth. To the best of our knowledge, our tracker is the first neural-network tracker that combines convolutional and recurrent networks with RL algorithms.
Failure of Carbon Fiber Yacht Mast in Heavy Weather

For many years Round the World racers and leading yacht owners have appreciated the benefit of carbon. Carbon fiber spars are around 50% lighter and considerably stronger than traditional aluminum masts. The result is increased speed, and the lighter mast also gives the boat a lower centre of gravity and so heeling and pitching is reduced. The recent spate of carbon mast failures has left concerns amongst the general yachting public about the reliability of the concept and ultimately the material itself. The lack of knowledge about loads acting on the mast prevents designers from coming with an optimum design. But a new program, the "Smart Mast" program, developed by two of Britain's leading marine companies, has been able to monitor loads acting on a mast in real-time with an optical fiber system. This improvement could possibly be a revolution in the design of racing yachts carbon masts and fill the design data shortage. Some other evolutions in the rigging design also appeared to be of interest, like for example the free-standing mast or a video system helping the helmsman to use its sails at their maximum. Thesis supervisor: Jerome  Professor of Civil and Environmental Engineering
Dynamic Data Selection for Neural Machine Translation

Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce dynamic data selection for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call gradual fine-tuning, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.
Taking a deeper look at pedestrians

In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pretraining on surrogate tasks. We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time.
Therac-25 and the security of the computer controlled equipment Ethics of Science and Technology ( WM 0314 IN )

Therac-25, a radiation treatment machine, massively overdosed 6 people because of a software error. In this paper we describe Therac-25 briefly then explain what went wrong in each of the 6 incidents. Then we analyze the responsibility of involved parties and show why these accidents happened. In the following sections we discuss the seriousness of software errors briefly and analyze safety engineering in order to prevent such accidents. We also introduce another radiation therapy accident which happened recently, to emphasize the existence and the seriousness of software errors.
FIRST IMPRESSIONS MATTER : A MODEL OF CONFIRMATORY BIAS

Psychological research indicates that people have a cognitive bias that leads them to misinterpret new information as supporting previously held hypotheses. We show in a simple model that such conrmatory bias induces overcondence: given any probabilistic assessment by an agent that one of two hypotheses is true, the appropriate beliefs would deem it less likely to be true. Indeed, the hypothesis that the agent believes in may be more likely to be wrong than right. We also show that the agent may come to believe with near certainty in a false hypothesis despite receiving an innite amount of information.
Indian Currency Denomination Recognition for Visually Impaired

There is a population of over 37 million people in the world that is visually impaired (VI). With the continuous advancement in technology, it&apos;s now possible to build viable technological solutions to help such disadvantaged people in their day to day life. In this paper we propose a solution that shall help visually impaired people identify Indian Currency notes which shall then simplify monetary transactions for them. The method proposed includes image processing techniques which have been elaborated in this paper.
Bayesian surprise attracts human attention

We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer's beliefs yield surprise, irrespectively of how rare or informative in Shannon's sense these observations are. We test the framework by quantifying the extent to which humans may orient attention and gaze towards surprising events or items while watching television. To this end, we implement a simple computational model where a low-level, sensory form of surprise is computed by simple simulated early visual neurons. Bayesian surprise is a strong attractor of human attention, with 72% of all gaze shifts directed towards locations more surprising than the average, a figure rising to 84% when focusing the analysis onto regions simultaneously selected by all observers. The proposed theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
Recent Advances in Clustering : A Brief Survey

Unsupervised learning (clustering) deals with instances, which have not been pre-classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithms is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity. This paper introduces the fundamental concepts of unsupervised learning while it surveys the recent clustering algorithms. Moreover, recent advances in unsupervised learning, such as ensembles of clustering algorithms and distributed clustering, are described.  Pattern Analysis, Machine Intelligence, Intelligent Systems
Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices.
 Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers.
 On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.