nils-herrmann/clean_scirepeval huggingface dataset:
A White Paper on keV sterile neutrino Dark Matter

We present a comprehensive review of keV scale sterile neutrino Dark Matter, collecting views and insights from all disciplines involved cosmology, astrophysics, nuclear, and particle physics in each case viewed from both theoretical and experimental/observational perspectives. After reviewing the role of active neutrinos in particle physics, astrophysics, and cosmology, we focus on sterile neutrinos in the context of the Dark Matter puzzle. Here, we first review the physics motivation for sterile neutrino Dark Matter, based on challenges and tensions in purely cold Dark Matter scenarios. We then round out the discussion by critically summarizing all known constraints on sterile neutrino Dark Matter arising from astrophysical observations, laboratory experiments, and theoretical considerations. In this context, we provide a balanced discourse on the possibly positive signal from X ray observations. Another focus of the paper concerns the construction of particle physics models, aiming to explain how sterile neutrinos of keV scale masses could arise in concrete settings beyond the Standard Model of elementary particle physics. The paper ends with an extensive review of current and future astrophysical and laboratory searches, highlighting new ideas and their experimental challenges, as well as future perspectives for the discovery of sterile neutrinos.
Intelligent financial fraud detection: A comprehensive review
Venue: Comput. Secur.

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods involving manual detection are not only time consuming, expensive and inaccurate, but in the age of big data they are also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive review of financial fraud detection research using such data mining methods, with a particular focus on computational intelligence (CI) based techniques. Over fifty scientific literature, primarily spanning the period 2004 2014, were analysed in this study; literature that reported empirical studies focussing specifically on CI based financial fraud detection were considered in particular. Research gap was identified as none of the existing review articles addresses the association among fraud types, CI based detection algorithms and their performance, as reported in the literature. We have presented a comprehensive classification as well as analysis of existing fraud detection literature based on key aspects such as detection algorithm used, fraud type investigated, and performance of the detection methods for specific financial fraud types. Some of the key issues and challenges associated with the current practices and potential future direction of research have also been identified.
Cooperative and Noncooperative R D in Duopoly with Spillovers and Upstream Input Providers

This paper extends the model in d'Aspremont and Jacquemin (1988) to include upstream input providers. We show that, for a sufficiently large number of input providers, noncooperative R&D investments are higher vis a vis cooperative R&D for any level of spillovers. This contrasts the finding in the literature that cooperative R&D investments are higher with high spillovers. Such result comes as not a surprise in our model because unit cost of production increases as the number of upstream firms goes up. Consequently, both cooperative R&D and noncooperative R&D decline, but the former declines faster than the latter.
Integration of price driven demand response using plug in electric vehicles in smart grids
Venue: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

This paper discusses integration of plug in electric vehicles in smart grids from different perspectives. First, in order to achieve a grid friendly charging load profile, a strategy is proposed based on the transactive control paradigm. This charging strategy enables electric vehicle owners to participate in real time pricing electricity markets to reduce their charging costs. Second, the impact of large scale adoption of electric vehicles on electricity generation and inter area flow schedules is discussed. In order to quantify potential changes, an interconnection scale optimal scheduling problem should be solved to determine hourly tie line flows. In the presence of price sensitive loads, the objective function of the scheduler is to maximize the total social welfare (i.e. economic surplus) rather than minimize production cost. Third, the utilization of demand response for frequency control purpose is studied. Fast acting demand response eases the use of slow ramping generation units, which is particularly important in the case of high penetration levels of intermittent renewable resources to maintain the real time balance between supply and demand. The procedure described enables estimation the benefits of smart charging in terms of cost and GHG emissions reduction for different renewable portfolio policies.
Task based load balancing for cloud aware massively Multiplayer Online Games
Venue: 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA)

In this paper we propose a task based load distribution framework for Massively Multiplayer Online Games running in hybrid cloud environments. Our solution breaks down high level tasks into subtasks in such a way that i) core subtasks (those with strong timing constraints) are executed at private resources owned by game operators; while ii) background subtasks (those with looser timing/reliability constraints) can be offloaded to temporary resources acquired from a public cloud. Our approach is lightweight and allows for faster deployment of newly acquired servers, making it more suitable for temporary overload situations. We present evaluation results confirming our solution as a viable alternative to traditional strategies.
Early mover advantage in e commerce platforms with low entry barriers: The role of customer relationship management capabilities
Venue: Inf. Manag.

This research investigates whether early mover advantage (EMA) exists among entrepreneurial e tailers operating on third party e commerce platforms. Contrary to traditional wisdom, the current research hypothesizes that e tailers may enjoy early mover advantages because of the consumer demand inertia amplified by the nature of the Internet and the system design characteristics of e commerce platforms. We also argue that customer relationship management capabilities help enhance early mover advantages in an online setting. We employ panel data on 7309 e tailers to perform analyses and find empirical evidence that strongly supports the abovementioned hypotheses.
Synthesis of recurrent neural networks for dynamical system simulation
Venue: Neural Networks

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time.
Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

simulations, particle particle contact laws determine the macroscopic simulation results. Particle based contact laws, in turn, commonly rely on semi empirical parameters which are difficult to obtain by direct microscopic measurements. In this study, we present a method for the identification of DEM simulation parameters that uses artificial neural networks to link macroscopic experimental results to microscopic numerical parameters. In the first step, a series of DEM simulations with varying simulation parameters is used to train a feed forward artificial neural network by backward propagation reinforcement. In the second step, this artificial neural network is used to predict the macroscopic ensemble behaviour in relation to additional sets of particle based simulation parameters. Thus, a comprehensive database is obtained which links particle based simulation parameters to specific macroscopic bulk behaviours of the ensemble. The trained artificial neural network is able to predict the behaviours of additional sets of input parameters accurately and highly efficiently. Furthermore, this method can be used generically to identify DEM material parameters. For each set of calibration experiments, the neural network needs to be trained only once. After the training, the neural network provides a generic link between the macroscopic experimental results and the microscopic DEM simulation parameters. Based on these experiments, the DEM simulation parameters of any given non cohesive granular material can be identified.
Deformation analysis of reinforced core auxetic assemblies by close range photogrammetry

In this article, we present an experimental study validating the concept of the reinforced core auxetic assemblies. The main distinctive feature of these assemblies is the presence of a reinforcing core in the microstructural units that increases the shear stiffness and thus produces negative Poisson's ratio. We tested a number of prototypes manufactured using the stereolithography and selective laser sintering methods. To measure the deformations, we employed the close range photogrammetry method, which enables monitoring of locations of unlimited number of points from digital images. We developed a procedure for multi point measurement of planar deformations using a single digital camera. This procedure allows for continuous measurement of deformations without interruption to the loading sequence. Camera self calibration is based on multiple images of stable reference points, which enables high accuracy of the measurements even using a consumer grade digital camera. We compare the obtained experimental results with a simplified continuum model. We demonstrate a good agreement between the theoretical predictions and the experimental results.
Analysis of Factors on Errors of the CCD Camera Calibration

This paper analyses the main effect factors of parametric accuracy in CCD camera calibration.and the practical camera calibration results based on these factors.In addition, this paper analyses the effect of light condition for the accuracy of feature extraction.The results show that the measuring precision of the world coordinate has a greater influence on the camera calibration, and 12 20 is the suitable number of images .Our research provide effective reference for well selection and manufacture of calibrating board in camera calibration, well choice of the number of images, and for obtaining stability calibration results.
High accuracy calibration of low cost camera using image disturbance factor.
Venue: Optics express

Nonindustrial low cost cameras have the advantages of cheap and simple structure, but have the disadvantages of low resolution and large image noise. When the existing camera calibration methods are used to calibrate nonindustrial low cost cameras, high accuracy calibration cannot be obtained. A high accuracy calibration method using a high accuracy planar target is introduced in this study to solve this problem. First, the initial values and the uncertainties of all image feature points are determined by the multiscale image analysis method. Then, the image disturbance factor is added to each target image feature point. In addition, the image projection error is established as the minimum objective function according to the homography matrix between the target plane and the image plane. Thus, the optimal coordinates of all image feature points are obtained by the nonlinear optimization method. Finally, the calibration of the intrinsic and extrinsic parameters of the camera will be achieved by using Zhang's method according to the image feature points obtained from the previous step. Simulative and real experiments have been conducted to evaluate the performance of the proposed method, and results show that the calibration accuracy of the proposed method is at least three times that of Zhang's method.
Multi task support vector machines for feature selection with shared knowledge discovery
Venue: Signal Process.

Feature selection is an effective way to reduce computational cost and improve feature quality for the large scale multimedia analysis system. In this paper, we propose a novel feature selection method in which the hinge loss function with a 2 1 norm regularization term is used to learn a sparse feature selection matrix for each learning task. Meanwhile, shared information exploiting across multiple tasks has been also taken into account by imposing a constraint which globally limits the combined feature selection matrices to be low rank. A convex optimization method is proposed to use in the framework by minimizing the trace norm of a matrix instead of minimizing the rank of a matrix directly. Afterwards, gradient descent is applied to find the global optimum. Extensive experiments have been conducted across eight datasets for different multimedia applications, including action recognition, face recognition, object recognition and scene recognition. Experimental results demonstrate that the proposed method performs better than other compared approaches. Especially, when the shared information across multiple tasks is very beneficial to the multi task learning, obvious improvements can be observed. HighlightsWe efficiently select discriminative features for each task by restricting feature selection matrix for each task to be sparse.Relatedness of features among multiple tasks has been also taken into account by mining the shared structures across all tasks.Instead of directly minimizing the rank of a matrix, we propose to minimize trace norm of a matrix.
Persistent Sharing of Fitness App Status on Twitter
Venue: CSCW

As the world becomes more digitized and interconnected, information that was once considered to be private such as one's health status is now being shared publicly. To understand this new phenomenon better, it is crucial to study what types of health information are being shared on social media and why, as well as by whom. In this paper, we study the traits of users who share their personal health and fitness related information on social media by analyzing fitness status updates that MyFitnessPal users have shared via Twitter. We investigate how certain features like user profile, fitness activity, and fitness network in social media can potentially impact the long term engagement of fitness app users. We also discuss implications of our findings to achieve a better retention of these users and to promote more sharing of their status updates.
Exploiting Fitness Apps for Sustainable Mobility Challenges Deploying the GoEco! App

The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves fitness tracker to collect mobility measurements) and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.
Smartphone Applications for Patients' Health and Fitness.
Venue: The American journal of medicine

Healthcare providers are often looking for ways to objectively monitor and improve their patients' health and fitness, especially in between patient visits. Some insurance companies are using applications data as incentives to improve health and lower premiums. As more and more people start to use smartphones, they may provide a tool to help improve a patient's health and fitness. Specifically, fitness applications or "apps" on smartphones are programs that use data collected from a smartphone's inbuilt tools, such as the Global Positioning System, accelerometer, microphone, speaker, and camera, to measure health and fitness parameters. The apps then analyze these data and summarize them, as well as devise individualized plans based on users' goals, provide frequent feedback, personalized coaching, and additional motivation by allowing milestones to be shared on social media. This article introduces evidence that apps can better help patients reach their health and fitness goals. It then discusses what features to look for in an app, followed by an overview of popular health and fitness apps. Last, patient scenarios with app recommendations, limitations of apps, and future research are discussed.
Risk Analysis of Gas Hydrate Formation during Deepwater Gas Well Testing

During deep water gas wells testing period, predicting the generating zone of hydrate precisely in the whole flow range (bore holes and surface flow lines) is the key prerequisite of guarantee for testing flow. The unusual deep water environment and low gas density make it easy to meet the conditions of hydrate generation during deep water gas wells testing period, such as low temperature and high pressure, especially under the testing conditions, for instance, near mud line, surface chock line, low gas generating rate and surface shut in. Wherefore, in view of all the operational modes during testing period (clean out, variable rate flow, downhole and surface shut in) based on temperature field of the whole flow range and phase equilibrium condition of hydrate, predicting method of hydrate generation zone is published; by taking enthalpy as the object of study, temperature calculation model increases the accuracy of temperature prediction; by integration of bore holes and surface lines, predicting plate of hydrate generation in the whole flow range is published. During flow period, the generating condition of hydrate is affected by rate of flow, and the lower the rate of flow is, the wider the hydrate generation zone is; during the stage of shut in and initial flow, if pressure of strings is higher and temperature is lower, risk of hydrate generation will be greater and hydrate generation zone will be larger, so relevant actions should be taken to restrain hydrate generation.
Implementation of Good Maufacturing Practices (GMP) in the Kitchen Hospital

Food safety is one of the important thing in public health improvement in Indonesia. Hospitals are required to keep food safety for patients by conducting the principle Good Manufacturing Practices (GMP) The purpose of this research to identify the application of GMP in Installation Nutrition Hospital. Design of this study was using descriptive research in observational method with cross sectional design. Variables the treatment were the physical building, utility, equipment, storage, and food handlers. Data collection was done by observation and interview. The research results show that the variable subjects enough to fulfi l with Kepmenkes No. 1204 of 2004 and Thaheer 2008. But there were several that must be considered as location building, fl oor ventilation, lighting, kitchens temperature, storage temperature of food that were not appropriate. sorting and processing of solid waste, hand washing facilities, drink water quality, storage temperature of food that were not appropriate, behavior the use of a mask and gloves while working. This suggestion are increase the open window as ventilation, turning lights when a food processing, adding exhauster for good air circulation, do the processing of solid waste, given the tool dryer, controlling at a temperature storage food, health check on rectal swab of food handlers is routinely, and sanctions or fi ne associated disobedience of food handlers in the use of mask and gloves while working.  Good Manufacturing Practices, food handlers, food safety
Visualizing Deep Convolutional Neural Networks Using Natural Pre images
Venue: International Journal of Computer Vision

Image representations, from SIFT and bag of visual words to convolutional neural networks (CNNs) are a crucial component of almost all computer vision systems. However, our understanding of them remains limited. In this paper we study several landmark representations, both shallow and deep, by a number of complementary visualization techniques. These visualizations are based on the concept of "natural pre image" namely a natural looking image whose representation has some notable property. We study in particular three such visualizations: inversion, in which the aim is to reconstruct an image from its representation, activation maximization, in which we search for patterns that maximally stimulate a representation component, and caricaturization, in which the visual patterns that a representation detects in an image are exaggerated. We pose these as a regularized energy minimization framework and demonstrate its generality and effectiveness. In particular, we show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Venue: ArXiv

Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging task. To remedy this unsatisfactory situation, a number of groups have recently proposed different methods to visualize the learned models. In this work we suggest a general taxonomy to classify and compare these methods, subdividing the literature into three main categories and providing researchers with a terminology to base their works on. Furthermore, we introduce the FeatureVis library for MatConvNet: an extendable, easy to use open source library for visualizing CNNs. It contains implementations from each of the three main classes of visualization methods and serves as a useful tool for an enhanced understanding of the features learned by intermediate layers, as well as for the analysis of why a network might fail for certain examples.