Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint demosaicing and super-resolution imaging from a set of unregistered aliased images

We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data before interpolation artifacts are introduced. After image registration, a high resolution color image is reconstructed at once using the full set of images. For this, we use normalized convolution, an image interpolation method from a nonuniform set of samples. Our algorithm is tested and compared to other approaches in simulations and practical experiments.
A common neighbour based two-way collaborative recommendation method

Traditional recommendation methods offer items, that are inanimate and one way recommendation, to users. Emerging new applications such as online dating or job recruitments require reciprocal people-to-people recommendations that are animate and two-way recommendations. In this paper, we propose a reciprocal collaborative method based on the concepts of users' similarities and common neighbors. The dataset employed for the experiment is gathered from a real life online dating network. The proposed method is compared with baseline methods that use traditional collaborative algorithms. Results show the proposed method can achieve noticeably better performance than the baseline methods.
Larger-than-memory data management on modern storage hardware for in-memory OLTP database systems

In-memory database management systems (DBMSs) outperform disk-oriented systems for on-line transaction processing (OLTP) workloads. But this improved performance is only achievable when the database is smaller than the amount of physical memory available in the system. To overcome this limitation, some in-memory DBMSs can move cold data out of volatile DRAM to secondary storage. Such data appears as if it resides in memory with the rest of the database even though it does not. Although there have been several implementations proposed for this type of cold data storage, there has not been a thorough evaluation of the design decisions in implementing this technique, such as policies for when to evict tuples and how to bring them back when they are needed. These choices are further complicated by the varying performance characteristics of different storage devices, including future non-volatile memory technologies. We explore these issues in this paper and discuss several approaches to solve them. We implemented all of these approaches in an in-memory DBMS and evaluated them using five different storage technologies. Our results show that choosing the best strategy based on the hardware improves throughput by 92-340% over a generic configuration.
Towards a Flexible User-Centred Visual Presentation Approach

Leveraging the power of flexible visual presentations has become an effective way to aid information interpretation, decision making and problem solving. It is indispensable to address the high complexities with visualization problems and relieve the impact from the intrinsic limitations of human cognitive capacity. Addressing these problems raises demanding requirements for information presentation flexibility. However, many existing visualization systems tend to provide weak support for such flexibility due to the issue of closely coupled information representation and presentation in system designs. This issue limits their support for rich presentation options, flexible presentation integration and reusability, and vivid storytelling of data. To help with addressing these problems, issues and requirements, this paper generalizes typical presentation models to provide paradigm level support for achieving presentation flexibility, and identifies key requirements for presentation development to accomplish the flexibility at a system level. With articulating the requirements at both paradigm and system levels, the paper proposes a user-centred process to realize presentation flexibility by meeting both functional and cognitive requirements for information presentation. The proposed theory is validated against a realworld business case and applied to guide the development of a prototypical system, which is demonstrated through a sequence of scenario-driven illustrations.
Gendered perceptions of typical engineers across specialties for engineering majors

ABSTRACT Young women do not choose to be engineers nearly as often as young men, and they tend to cluster in particular specialties when they do. We examine these patterns and the role of gender schemas as applied to perceptions of typical engineers in understanding the choices that women make in terms of engineering specialties. We use Part 1 of two waves of survey data collected from first-year engineering students at a large Midwestern university in the fall of 2014. Respondents scored typical engineers on 60 items from the Bem Sex-Role Inventory [Bem, S. L. 1974. “The Measurement of Psychological Androgyny. ” Journal of Consulting and Clinical Psychology 42: 155–162]. Preliminary results show that women consider the typical engineer to be more masculine compared to the impressions gathered from men. Additionally, engineering gender schemas predict some specialty areas but not all, suggesting that gendered perceptions may not affect choice of specialty. Our results provide insight into the decisions that women make once entering engineering.
Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

This paper presents a new approach for breast cancer diagnosis using a combination of an Adaptive Network based Fuzzy Inference System (ANFIS) and the Information Gain method. In this approach, the ANFIS is to build an input-output mapping using both human knowledge and machine learning ability and the information gain method is to reduce the number of input features to ANFIS. An experimental result shows 98.23% accuracy which underlines the capability of the proposed algorithm.
Mindfulness And Acceptance Expanding The Cognitive Behavioral Tradition

Thank you for reading mindfulness and acceptance expanding the cognitive behavioral tradition. As you may know, people have look hundreds times for their favorite readings like this mindfulness and acceptance expanding the cognitive behavioral tradition, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer.
Ensemble based incremental SVM classifiers for changing environments

For most of the real-world applications, two main challenges are infinite data flow and time changing concepts. Generally data are gathered over a long period of time and the data generation mechanism may change with time. In a dynamic environment, knowledge about the environment is rarely complete due to time-changing concepts. In recent years, a lot of methods have been proposed for effective learning in changing environments. Due to their ability to learn from new data, incremental learning algorithms can be used for learning in changing environments. In this paper we propose an ensemble based incremental learning approach with SVM (support vector machines) classifiers to provide ability to learn new domain knowledge in a non-stationary environment. Experiments on different datasets with simulated concept drift show promising results.
Simulation of the light signal scattered by brownian colloidal particles

In dynamic light scattering experiments, the light intensity scattered by brownian colloidal particles is regarded as a stationary stochastic process. According to the theory of stationary stochastic process, a method for generating the light scattering signal is proposed. This method establishes an auto-regressive (AR) model by the autocorrelation function (ACF) of scattered light signal. When white noises pass this AR model, scattering light signal can be obtained. The numerical results show the ACF of simulated signal performs a good agreement with its theory value. Therefore, this method is feasible for the simulation the light signal scattered by brownian colloidal particles. In addition, by analyzing influence of simulation parameters on simulation precision, the paper acquires the relationship of simulation parameters and simulation precision.
SESSL

This article introduces SESSL (Simulation Experiment Specification via a Scala Layer), an embedded domain-specific language for simulation experiments. It serves as an additional software layer between users and simulation systems and is implemented in Scala. SESSL supports multiple simulation systems and offers various features (e.g., for experiment design, performance analysis, result reporting, and simulation-based optimization). It supports “cutting-edge” experiments by allowing to add custom code, enables a reuse of functionality across simulation systems, and improves the reproducibility of simulation experiments.
3 D chaotic model for sub-grid turbulent dispersion in Large Eddy Simulations

We introduce a 3D multiscale kinematic velocity field as a model to simulate Lagrangian turbulent dispersion. The incompressible velocity field is a nonlinear deterministic function, periodic in space and time, that generates chaotic mixing of Lagrangian trajectories. Relative dispersion properties, e.g. the Richardson’s law, are correctly reproduced under two basic conditions: 1) the velocity amplitudes of the spatial modes must be related to the corresponding wavelengths through the Kolmogorov scaling; 2) the problem of the lack of ”sweeping effect” of the small eddies by the large eddies, common to kinematic simulations, has to be taken into account. We show that, as far as Lagrangian dispersion is concerned, our model can be successfully applied as additional sub-grid contribution for Large Eddy Simulations of the planetary boundary layer flow.
The NEMO P2P service orchestration framework

NEMO (Networked Environment for Media Orchestration) is a policy managed, peer-to-peer, service orchestration framework that supports the formation of grass roots, self organizing service networks that can enable rich media experiences. We describe some of the interesting features of the NEMO architecture, and some experimental applications. Services are distributed across peer-to-peer communicating nodes. Each node provides message routing and orchestration using a message pump and workflow collator designed specifically for NEMO. Distributed policy management of service interfaces helps to provide trust and security, supporting commercial exchange of value. P2P messaging and workflow collation allow rich services to be dynamically created from a heterogeneous set of primitive services. We examine the possibilities of P2P computing when the shared resources are services of many different types, using different service interface bindings beyond those typically supported in a Web service deployment built on UDDI, SOAP, and WSDL. NEMO provides a media services framework enabling nodes to find one other, interact, exchange value, and cooperate across tiers of networks from WANs to PANs. Using NEMO, digital rights management is more concerned with policy management of service interfaces than copy protection, and new services can more easily ensure fair use. This has the potential to change the tension between consumers and content providers in the digital content domain as the NEMO enriched infrastructure provides consumers with better information, more useful services and instant gratification.
Tool supported development of service-based systems

Service-based systems engineering is a recent paradigm that has proven useful for the development of multifunctional systems, whose functions may be used in different contexts and have strong interrelations and dependencies between each other. Integrated into a service-oriented development process, we present an approach for tool supported design of services and execution scenarios describing their interaction, using the tool AUTOFOCUS. It includes the application of simulation, verification of typical requirements for service-based systems using model checking, and code generation. We report on our experience with this approach by means of a case study from the automotive domain, a fairly new field of application for service-based systems engineering.
Lesbian Couple Dynamics and Heterosexist Stressors: Building a Foundation for Culturally Competent Relationship Interventions

Lesbian relationships are severely underrepresented in the couples and family literature (Hartwell, Serovich, Grafsky, & Kerr, 2012). The current study sought to expand the basic science on lesbian couples with the overarching goal of informing evidence-based relationship interventions. The first aim of this study was to examine processes found to be important to relationship success in previous studies of couples in general, including communication, external support, household tasks, intimacy, and sex, as these processes are typically targeted in relationship interventions. The second aim was to examine the role of factors more specific to lesbian couples and related to heterosexist stressors as these factors may provide content areas for creating more culturally sensitive and affirming relationship programs. The heterosexist stressors analyzed included sexual minority stress—conceptualized to consist of outness, internalized homophobia, and discrimination—as well as commitment behaviors given the variable legal climate for same-sex couples. Finally, the third aim was to assess the associations between relationship quality and mental health outcomes. Participants included 103 adult female same-sex couples who provided self-report data and participated in observational communication tasks. Actor-Partner Interdependence Models (Kenny, Kashy, & Cook, 2006) were utilized for most analyses. Findings suggest that processes traditionally addressed by relationship interventions would likely be
Self-embedding Fragile Watermarking Scheme Based on Bicubic Prediction

To improve the quality of recovered images, a selfembedding fragile watermarking scheme is proposed based on the bicubic prediction. To take into account the PFA and the watermark payload, the 6-bit recovery data of a 2×2 block and the 8-bit key-based data of a 4×4 block are generated and inserted in the other 2×2 block and 4×4 block based on secret key, respectively. The validity of a 2×2 image block is determined by combining the recovery data with the key-based data. To improve the recovery quality, the recovery method based on bicubic prediction is designed to reconstruct the invalid blocks whose recovery watermark embedded in the other block is also destroyed. Simulation results demonstrate that the proposed scheme allows image recovery with an acceptable visual quality (PSNR ≈ 25 dB) up to 75% tampering.
Test Bed for Superconducting Materials

Superconducting rf cavities are increasingly used in accelerators. Gradient is a parameter of particular importance for the ILC. Much progress in gradient has been made over the past decade, overcoming problems of multipacting, field emission, and breakdown triggered by surface impurities. However, the quenching limit of the surface magnetic field for niobium remains a hard limitation on cavity fields sustainable with this technology. Further exploration of materials and preparation may offer a path to surpassing the current limit. For this purpose, we have designed a resonant test cavity. One wall of the cavity is formed by a flat sample of superconducting material; the rest of the cavity is copper or niobium. The H field on the sample wall is 75% higher than on any other surface. Multipacting is avoided by use of a mode with no surface electric field. The cavity will be resonated through a coupling iris with high-power rf at superconducting temperature until the sample wall quenches, as detected by a change in the quality factor. This experiment will allow us to measure critical magnetic fields up to well above that of niobium with minimal cost and effort.
Improved genetic algorithm for integrated steelmaking optimum charge plan

Abstract The shortcoming of the standard genetic algorithm is analysed. An improved genetic algorithm with modified mutation operator and adaptive probabilities of crossover and mutation is proposed. Simulation experiments have been carried and the results show that the modifications are very effective. In this paper, an optimum charge plan for steelmaking continuous casting production scheduling is also studied. The charge plan model is established. The modified genetic algorithm is used to solve the optimum charge plan problem. The computation with practical data shows that the model and the modified genetic algorithm are very effective.
An Anonymous waterMarking Scheme for Content Distribution Protection using Trusted Computing

Many Content Distribution Protection (CDP) schemes (e.g. Buyer-Seller Watermarking and Asymmetric Fingerprinting) have been proposed to address the problem of illegal distribution of copyrighted content. All of the existing CDP schemes rely on a Trusted Third Party in one way or another to achieve the desired security objectives. In this paper, using the functionalities of Trusted Computing, we present an anonymous CDP watermarking scheme, which minimises the reliance on a Trusted Third Party. Our scheme allows a buyer to anonymously purchase digital content, whilst enabling the content provider to blacklist the buyers that are distributing content illegally.
HMeanMax: Placing HMAX and HoG into a unified framework

Recently, the bio-inspired model HMAX has attracted much attention for its amazing biological inspired structure and comparable performance to the state-of-the-art computer vision algorithms. The success of HMAX leads to our further exploration on it especially the connection between the biological mechanism and the engineering validity. By detailedly analyzing HMAX and a totally engineering-driven approach HoG, we find such two methods have similar structures excepts the different pooling strategies, max versus mean, thus can be placed into a unified framework. Therefore, we present a unified framework named HMeanMax to integrate HMAX and HoG via combining multiple types of pooling into a single hierarchical feature extractor. All the experimental results support our findings.
Large Cell Calcifying Sertoli Cell Tumor of the Testis: A Case Study and Review of the Literature

A 24-year-old man was admitted due to an incidentally detected mass in his left testis, which showed radiopaque calcification on plain X-ray film. Left orchiectomy was performed, and the resected testis contained a well-demarcated, hard mass measuring 1.1 cm. Histological analysis revealed that the tumor was composed of neoplastic cells, fibrotic stroma, and laminated or irregularly shaped calcific bodies. The individual cells had abundant eosinophilic or clear cytoplasm with round nuclei, each of which contained one or two conspicuous nucleoli. They were arranged in cords, trabeculae, clusters, and diffuse sheets. There were several foci of intra-tubular growth patterns, with thickening of the basal lamina. Immunohistochemically, the neoplastic cells were positive for S-100 protein and vimentin, focally positive for inhibin alpha, and negative for cytokeratin, CD10, and Melan-A. In addition to reporting this rare case, we also review the relevant literature regarding large cell calcifying Sertoli cell tumors.
Interactive Left Ventricular Segmentation in Cardiac Images

This work presents a technique to perform segmentation of the left ventricle in images of the human myocardium. Segmentation is one of the first steps of the image analysis. Edge-based segmentation provides the detection of region boundaries, but the contours are only based on local computations, and is often sensitive to local variations in intensity, noise and physical artifacts. In interactive methods, problems like these fatigue the user and require many interventions at the contour delineation process. To reduce these problems, we propose the addition of two new features in the live-wire method: a region intensity and a proximity feature. Experimental results show that this alternative approach can achieve accurate edge localization and improved efficiency.
Fast Single Image super-resolution algorithm using feature based regression analysis

Single Image super-resolution (SISR) algorithms focus on increasing the resolution of digital images by establishing a relationship between corresponding low and high resolution images. The relationship is derived using redundant information across the different scales of the image or by using priori information from a training set. Matrix based regression methods have shown superior results when applied to this setting, as they use matrix level image information. In this paper, we extend the concept of a matrix operator to a low level feature super-resolution algorithm that performs edge enhancement and de-noising. Although it is competitively effective in resolving edges and other low level features, it is computationally less efficient when compared to image based operators. We propose a novel patch based implementation, in which the decision to implement the costly feature regression is based on the feature components present in that particular patch. Smooth and gradual patches are resolved by image operators and patches with strong features are resolved by feature operators. The proposed algorithm achieves competitive performance efficiency and is verified by experimental results.
Recognizing human actions based on Sparse Coding with Non-negative and Locality constraints

In this paper, Sparse Coding with Non-negative and Locality constraints (SCNL) is proposed to generate discriminative feature descriptions for human action recognition. The non-negative constraint ensures that every data sample is in the convex hull of its neighbors. The locality constraint makes a data sample only represented by its related neighbor atoms. The sparsity constraint confines the dictionary atoms involved in the sample representation as fewer as possible. The SCNL model can better capture the global subspace structures of data than classical sparse coding, and are more robust to noise compared to locality-constrained linear coding. Extensive experiments testify the significant advantages of the proposed SCNL model through evaluations on three remarkable human action datasets.
The Great Compression of the Wage Structure in France, 1969-2008 The Role of Supply and Demand Factors∗

This paper studies the evolution of the wage distribution in France from 1964 to 2008. We highlight that differences in the timing of educational expansion in the 20th century with respect to the US or the UK can explain part of the differences in the evolution of wage inequality. Our estimates suggest that supply and demand mechanisms are related with changes in the wage premium within cohorts while the overall skill premium is better explained by minimum wage increases after 1990. This suggests that both market forces and institutional factors explain the recent compression of the French wage structure. ∗I thank the Centre Maurice Halbwachs (CMH) for having made the data available. The data used in this paper are available upon request for researchers from the CMH. I thank Hervé le Bihan, Guillaume Horny and seminar participants at the Banque de France for insightful comments. This paper does not necessarily reflect the views of the Banque de France. †Service des Analyses Microéconomiques, Direction des Etudes Microéconomiques et Structurelles, Banque de France, 31 rue Croix-des-petits-champs, 75049 Paris Cedex 01, France. Email: gregory.verdugo@banquefrance.fr