Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarization leakage in epoch of reionization windows – II. Primary beam model and direction-dependent calibration

Leakage of diffuse polarized emission into Stokes I caused by the polarized primary beam of the instrument might mimic the spectral structure of the 21-cm signal coming from the epoch of reionization (EoR) making their separation difficult. Therefore, understanding polarimetric performance of the antenna is crucial for a successful detection of the EoR signal. Here, we have calculated the accuracy of the nominal model beam of Low Frequency ARray (LOFAR) in predicting the leakage from Stokes I to Q, U by comparing them with the corresponding leakage of compact sources actually observed in the 3C 295 field. We have found that the model beam has errors of ≤10 per cent on the predicted levels of leakage of ˜1 per cent within the field of view, i.e. if the leakage is taken out perfectly using this model the leakage will reduce to 10-3 of the Stokes I flux. If similar levels of accuracy can be obtained in removing leakage from Stokes Q, U to I, we can say, based on the results of our previous paper, that the removal of this leakage using this beam model would ensure that the leakage is well below the expected EoR signal in almost the whole instrumental k-space of the cylindrical power spectrum. We have also shown here that direction-dependent calibration can remove instrumentally polarized compact sources, given an unpolarized sky model, very close to the local noise level.
Timed Games for Computing Worst-Case Execution-Times

In this paper we introduce a framework for computing upper bounds yet accurate WCET for hardware platforms with caches and pipelines. The methodology we propose consists of 3 steps: 1) given a program to analyse, compute an equivalent (WCET-wise) abstract program; 2) build a timed game by composing this abstract program with a network of timed automata modeling the architecture; and 3) compute the WCET as the optimal time to reach a winning state in this game. We demonstrate the applicability of our framework on standard benchmarks for an ARM9 processor with instruction and data caches, and compute the WCET with UPPAAL-TiGA. We also show that this framework can easily be extended to take into account dynamic changes in the speed of the processor during program execution. %
Quadratic Interpolation and Linear Lifting Design

A quadratic image interpolation method is stated. The formulation is connected to the optimization of lifting steps. This relation triggers the exploration of several interpolation possibilities within the same context, which uses the theory of convex optimization to minimize quadratic functions with linear constraints. The methods consider possible knowledge available from a given application. A set of linear equality constraints that relate wavelet bases and coefficients with the underlying signal is introduced in the formulation. As a consequence, the formulation turns out to be adequate for the design of lifting steps. The resulting steps are related to the prediction minimizing the detail signal energy and to the update minimizing the l2-norm of the approximation signal gradient. Results are reported for the interpolation methods in terms of PSNR and also, coding results are given for the new update lifting steps.
Improving contact force estimation accuracy by optimal redundancy resolution

Estimating Cartesian contact forces and torques enables external force supervision for robotic manipulators and even force-controlled applications while avoiding the need for additional external sensing. Redundant manipulators facilitate the problem of Cartesian contact force and torque estimation (CCFE) at the TCP, since an increased amount of joint level information is available for estimating the six components of external wrench. In this paper, we point out that the errors in CCFE estimates arising from inevitable uncertainties in available joint level information (either measurements or estimates) depend on the manipulator configuration. Based on this, an algorithm is proposed that resolves redundancy in an optimal way with respect of the achievable CCFE quality. For a given TCP pose, the joint configuration resulting in minimized CCFE errors is calculated. The proposed method is verified by means of experimental data gathered from a 7DOF manipulator.
A novel method for identification of local conformational changes in proteins

Motivation: Proteins are known to undergo conformational changes in the course of their functions. The changes in conformation are often attributable to a small fraction of residues within the protein. Therefore identification of these variable regions is important for an understanding of protein function. Results: We propose a novel method for identification of local conformational changes in proteins. In our method, backbone conformations are encoded into a sequence of letters from a 16-letter alphabet (called D2 codes) to perform structural comparison. Since we do not use clustering analysis to encode local structures, the D2 codes not only provides a intuitively understandable description of protein structures, but also covers wide varieties of distortions. This paper shows that the D2 codes are better correlated with changes in the dihedral angles than a structural alphabet and a secondary structure description. In the case of the N37S mutant of HIV-1 protease, local conformational changes were captured by the D2 coding method more accurately than other methods. The D2 coding also provided a reliable representation of the difference between NMR models of an HIV-1 protease mutant.
Image mapping using local and global statistics

We describe a set of techniques for mapping one image to another based on the statistics of a training set. We apply these techniques to the problems of image denoising and superresolution, but they should also be useful for many vision problems where training data are available. Given a local feature vector computed from an input image patch, we learn to estimate a subband coefficient of the output image conditioned on the patch. This entails approximating a multidimensional function, which we make tractable by nested binning and linear regression within bins. This method performs as well as nearest neighbor techniques, but is much faster. After attaining this local (patch based) estimate, we force the marginal subband histograms to match a set of target histograms, in the style of Heeger and Bergen.1 The target histograms are themselves estimated from the training data. With the combined techniques, denoising performance is similar to state of the art techniques in terms of PSNR, and is slightly superior in subjective quality. In the case of superresolution, our techniques produce higher subjective quality than the competing methods, allowing us to attain large increases in apparent resolution. Thus, for these two tasks, our method is very fast and very effective.
Current sensorless solution for PFC boost converter operating both in DCM and CCM

In this paper a current sensorless solution for Power Factor Control (PFC) of an AC-DC boost converter operating in light load condition has been presented. A PI-based control with input voltage feedforward has been implemented in order to increase Power Factor (PF). A current observer, able to operate in either Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM), has been adopted in order to eliminate the need for expensive current sensors and to improve boost converter performances under light load conditions. The proposed solution has been numerically tested in three different load conditions using a powerful software simulation platform.
Justice and Home Affairs databases and a Smart Borders System at EU external borders. An evaluation of current and forthcoming proposals. CEPS Paper in Liberty and Security No. 52/December 2012

This study examines current and forthcoming measures related to the exchange of data and information in EU Justice and Home Affairs policies, with a focus on the ‘smart borders’ initiative. It argues that there is no reversibility in the growing reliance on such schemes and asks whether current and forthcoming proposals are necessary and original. It outlines the main challenges raised by the proposals, including issues related to the right to data protection, but also to privacy and non-discrimination.
Galactic Cosmic Rays from Superbubbles and the Abundances of Lithium, Beryllium, and Boron

In this article we study the Galactic evolution of the LiBeB elements within the framework of a detailed model of the chemical evolution of the Galaxy that includes Galactic cosmic-ray (GCR) nucleosynthesis by particles accelerated in superbubbles. The chemical composition of the superbubble consists of varying proportions of interstellar medium (ISM) and freshly supernova-synthesized material. The observational trends of 6LiBeB evolution are nicely reproduced by models in which GCRs come from a mixture of 25% supernova material with 75% ISM, except for 6Li, for which perhaps an extra source is required at low metallicities. To account for 7Li evolution, several additional sources have been considered (neutrino-induced nucleosynthesis, nova outbursts, and C stars). The model fulfills the energetic requirements for GCR acceleration.
PRINTS-S: the database formerly known as PRINTS

The PRINTS database houses a collection of protein family fingerprints. These are groups of motifs that together are diagnostically more potent than single motifs by virtue of the biological context afforded by matching motif neighbours. Around 1200 fingerprints have now been created and stored in the database. The September 1999 release (version 24.0) encodes approximately 7200 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. In addition to its continued steady growth, we report here several major changes to the resource, including the design of an automated strategy for database maintenance, and implementation of an object-relational schema for more efficient data management. The database is accessible for BLAST, fingerprint and text searches at http://www.bioinf. man.ac. uk/dbbrowser/PRINTS/
Ridge Leverage Scores for Low-Rank Approximation

Often used as importance sampling probabilities, leverage scores have become indispensable in randomized algorithms for linear algebra, optimization, graph theory, and machine learning. A major body of work seeks to adapt these scores to low-rank approximation problems. However, existing "low-rank leverage scores" can be difficult to compute, often work for just a single application, and are sensitive to matrix perturbations. We show how to avoid these issues by exploiting connections between low-rank approximation and regularization. Specifically, we employ ridge leverage scores, which are simply standard leverage scores computed with respect to an $\ell_2$ regularized input. Importance sampling by these scores gives the first unified solution to two of the most important low-rank sampling problems: $(1+\epsilon)$ error column subset selection and $(1+\epsilon)$ error projection-cost preservation. Moreover, ridge leverage scores satisfy a key monotonicity property that does not hold for any prior low-rank leverage scores. Their resulting robustness leads to two sought-after results in randomized linear algebra. 1) We give the first input-sparsity time low-rank approximation algorithm based on iterative column sampling, resolving an open question posed in [LMP13], [CLM+15], and [AM15]. 2) We give the first single-pass streaming column subset selection algorithm whose real-number space complexity has no dependence on stream length.
Graph Run-Length Matrices for Histopathological Image Segmentation

The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from “graph run-length matrices” lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.
Quantifying lower limb joint position sense using a robotic exoskeleton: A pilot study

Clinicians and scientists often focus on tracking the recovery of motor skills after spinal cord injury (SCI), but less attention is paid to the recovery of sensory skills. Measures of sensory function are imperative for evaluating the efficacy of treatments and therapies. Proprioception is one sensory modality that provides information about static position and movement sense. Because of its critical contribution to motor control, proprioception should be measured during the course of recovery after neurological injury. Current clinical methods to test proprioception are limited to crude, manual tests of movement and position sense. The purpose of this study was to develop a quantitative assessment tool to measure joint position sense in the legs. We used the Lokomat, a robotic exoskeleton, and custom software to assess joint position sense in the hip and knee in 9 able-bodied (AB) subjects and 1 person with incomplete SCI. We used two different test paradigms. Both required the subject to move the leg to a target angle, but the presentation of the target was either a remembered or visual target angle. We found that AB subjects had more accurate position sense in the remembered task than in the visual task, and that they tended to have greater accuracy at the hip than at the knee. Position sense of the subject with SCI was comparable to those of the AB subjects. We show that using the Lokomat to assess joint position sense may be an effective clinical measurement tool.
Accelerating Inference: towards a full Language, Compiler and Hardware stack

We introduce Dimple, a fully open-source API for probabilistic modeling. Dimple allows the user to specify probabilistic models in the form of graphical models, Bayesian networks, or factor graphs, and performs inference (by automatically deriving an inference engine from a variety of algorithms) on the model. Dimple also serves as a compiler for GP5, a hardware accelerator for inference.
Demand side management in smart grid by multi-agent systems technology

In this paper, we present a new approach for demand side management in Smart Grid. The Multi Agent Systems (MAS) Technology is actually the most suitable to face its emerging challenges, such as complex management and control, self-healing with necessity of collaboration and communication to solve a local or global problem, and many other problematic aspects. Nowadays, there is no complete environment to simulate MAS. The Agent Oriented Programming is based on multithreading, while the simulation tools don't allow it or causes their instability. Our solution consists on combining these technologies to take benefit from their advantages and strength with available features and characteristics to model and simulate smart grid as a MAS. That leads to demand side management optimization and meets the smart grid objectives and requirements.
A review of leadership

Nowadays, the global business context is becoming more complex, complicated and changing with a lot of opportunities and threats rising from the turbulent times we are facing nowadays. It is this dynamic nature which gives rise to major opportunities for international business developments and innovations. Due to the new communication tools and to the fall of commercial barriers, the economic world is more interlinked than ever before. This new economic stage has both benefits (lower costs in production, better specialization, higher speed in production, velocity in supply chain) and disadvantages, stemming from more risk and uncertainty. An economic change in one part of the world can escalate and create massive turbulences in the world economic system. The most recent example is that of financial crisis of 2008, caused by the loose loan policies of the American banking system in the real estate sector, causing great instability in the financial markets and affecting the global economy. In this context, the issue of leadership as a differentiator and key success factor in a global business environment has become of great interest first to multiple organizations such as: multinationals, universities, both private and public, social and political institutions, non-governmental organizations, being regarded as an important factor of success or failure. Most situations and decisions are complex because some communication tools are introduced and used in modern business. The capacity to quick react to changes arising from the external environment is becoming a big pressure for these players. The leadership concept has been intensely debated in literature, in the last fifty years, belonging to the social and human sciences and has many implications in the economic field, resulting in a differentiation in transactional and transformational leadership model.
A practical method for watermarking Java programs

Java programs distributed through the Internet are now suffering from program theft. This is because Java programs can be easily decomposed into reusable class files and even decompiled into source code by program users. We propose a practical method that discourages program theft by embedding Java programs with a digital watermark. Embedding a program developer's copyright notation as a watermark in Java class files will ensure the legal ownership of class files. Our embedding method is indiscernible by program users, yet enables us to identify an illegal program that contains stolen class files. The result of the experiment to evaluate our method showed most of the watermarks (20 out of 23) embedded in class files survived two kinds of attacks that attempt to erase watermarks: an obfuscactor attack, and a decompile-recompile attack.
Estimation of Tikhonov Regularization Parameter for Image Reconstruction in Electromagnetic Geotomography

The aim of this research was to devise an efficient way of finding the optimal value of the parameter in the Tikhonov regularization applied to image reconstruction in electromagnetic (EM) geotomography. We employed three basic approaches: a plot of norm l2 of the error vector versus the regularization parameter, the Ordinary Cross-Validation (OCV) and the L-curve. These tools were applied to synthetic noise-free and noisy data generated from an original image. In a simulation study, the first tool was used as the background for comparisons with the other methods. The OCV criterion has been found to estimate the regularization parameter most precisely. Its rather high computing cost can be reduced through a combination of powerful hardware with sophisticated implementation. Furthermore, this criterion combined with any regularization method constitutes a robust tool for solving ill-posed inverse problems.
A Simple Gradient-based Metric Learning Algorithm for Object Recognition

The Nearest Neighbor (NN) classification/regression techniques, besides their simplicity, is one of the most widely applied and well studied techniques for pattern recognition in machine learning. Their only drawback is the assumption of the availability of a proper metric used to measure distances to k nearest neighbors. It has been shown that K-NN classifier’s with a right distance metric can perform better than other sophisticated alternatives like Support Vector Machines (SVM) and Gaussian Processes (GP) classifiers. That’s why recent research in k-NN methods has focused on metric learning i.e., finding an optimized metric. In this paper we have proposed a simple gradient based algorithm for metric learning. We discuss in detail the motivations behind metric learning, i.e., error minimization and margin maximization. Our formulation is different from the prevalent techniques in metric learning where goal is to maximize the classifier’s margin. Instead our proposed technique (MEGM) finds an optimal metric by directly minimizing the mean square error. Our technique not only resulted in greatly improving k-NN performance but also performed better than competing metric learning techniques. We also compared our algorithm’s performance with that of SVM. Promising results are reported on major faces, digits, object and UCIML databases.
Query shredding: efficient relational evaluation of queries over nested multisets

Nested relational query languages have been explored extensively, and underlie industrial language-integrated query systems such as Microsoft's LINQ. However, relational databases do not natively support nested collections in query results. This can lead to major performance problems: if programmers write queries that yield nested results, then such systems typically either fail or generate a large number of queries. We present a new approach to query shredding, which converts a query returning nested data to a fixed number of SQL queries. Our approach, in contrast to prior work, handles multiset semantics, and generates an idiomatic SQL:1999 query directly from a normal form for nested queries. We provide a detailed description of our translation and present experiments showing that it offers comparable or better performance than a recent alternative approach on a range of examples.
The Acculturation Process and Philanthropic Giving: Evaluating the Effect of Sociocultural Adaptation and Ethnic Identity

Although literature on the field of philanthropy has examined why and how individual give, there is a lack of attention paid to the acculturation process that occurs after immigrants’ arrival to a host country. This study focuses on two salient parts of the acculturation process―sociocultural adaptation and ethnic identity―and empirically examines their respective effects on giving. Based on data from the Korean-American Philanthropic Survey, regression models are constructed to estimate such effects. The empirical results show that immigrants with a higher level of sociocultural adaptation are more likely to give than their counterparts in terms of both participation and the amount. In addition, immigrants with a stronger Korean identity are more likely to give more than their counterparts. Finally, some resources that immigrants possess are positively related to giving, including education, religiosity, age, and household income.
Investigating inclusive education in Ireland: constructing case studies for the identification of commonalities and exceptionalities

Project IRIS (Inclusive Research in Irish Schools) is a four year longitudinal study into special educational needs provision across the Republic of Ireland. As part of this project the research team has constructed case studies around schools and individual pupils in order to interrogate policy, provision, experiences and outcomes in relation to the services provided. This paper presents a model of comparative case study development based upon the work of Charles Ragin that enabled researchers to make fair comparisons between schools taking account of a range of variables. The presenters suggest that the approach developed enables an understanding to be gained of commonalities and exceptionalities that provide insights into current approaches to managing special education in Ireland