Miracle-dz/UniFAR-FTUs huggingface dataset:
A map-based visualization for improving social interaction in conference location

The social networks analysis and visualization can improve the relationships among their members supporting them in identifying their common goals and cooperating to achieve them. The visualization of the social network can be created mainly using graphs and matrixes. This paper proposes a map-based system for the visualization of people interests during a scientific conference using a matrix approach. The main purpose is allowing a user to detect people that share work interests in order to establish a direct connection by mobile devices with them. Maps are used to stimulate the rise of social networks promoting users’ interaction and communication and visualizing them. The system allows visualizing different views filtering information according to: a set of categories that fit her/his work interests; distance between the user position and the position of the selected members in the network; interests of members of the conference.
Percutaneous Tricuspid Valve Implantation: Two-Center Experience With Midterm Results

Background—Severe tricuspid valve (TV) dysfunction may lead to surgical TV replacement with a biological valve prosthesis in patients with congenital heart disease. To expand the lifetime of this valve and reduce the number of surgeries, percutaneous TV implantation (PTVI) may be an effective alternative to repeated surgery. We report on our 2-center experience with PTVI. Methods and Results—Between 2008 and 2014, 17 percutaneous valves were implanted in 16 patients with TV bioprosthesis dysfunction (9 females) from 2 centers. Median age and weight were 31.3 years (5–77.2) and 65.2 kg (17.7–107); 14 patients had congenital heart disease (univentricular heart with a right atrial to right ventricle bioprosthesis in 3, Ebstein’s anomaly of the TV in 5, and other in 6), and 2 had acquired TV dysfunction. All procedures were successful (Melody n=7, Sapien 26 mm valve n=4, Sapien XT 29 mm valve n=6). One valve showed early dysfunction. It was replaced surgically and shortly after that a repeated PTVI was performed. The median duration of follow-up was 2.1 years (3 days to 6.3 years). The percutaneous valve was performing well in 15 of 16 patients. Conclusions—PTVI was safe and effectively improved TV function in all but 1 patient at midterm follow-up. We think that PTVI is a good alternative to repeated surgical TV replacements and that it may reduce the total number of open heart surgeries in these patients.
Type II Toxin–Antitoxin Systems in the Unicellular Cyanobacterium Synechocystis sp. PCC 6803

Bacterial toxin–antitoxin (TA) systems are genetic elements, which are encoded by plasmid as well as chromosomal loci. They mediate plasmid and genomic island maintenance through post-segregational killing mechanisms but may also have milder effects, acting as mobile stress response systems that help certain cells of a population in persisting adverse growth conditions. Very few cyanobacterial TA system have been characterized thus far. In this work, we focus on the cyanobacterium Synechocystis 6803, a widely used model organism. We expand the number of putative Type II TA systems from 36 to 69 plus seven stand-alone components. Forty-seven TA pairs are located on the chromosome and 22 are plasmid-located. Different types of toxins are associated with various antitoxins in a mix and match principle. According to protein domains and experimental data, 81% of all toxins in Synechocystis 6803 likely exhibit RNase activity, suggesting extensive potential for toxicity-related RNA degradation and toxin-mediated transcriptome remodeling. Of particular interest is the Ssr8013–Slr8014 system encoded on plasmid pSYSG, which is part of a larger defense island or the pSYSX system Slr6056–Slr6057, which is linked to a bacterial ubiquitin-like system. Consequently, Synechocystis 6803 is one of the most prolific sources of new information about these genetic elements.
Comparing trotting and turning strategies on the quadrupedal oncilla robot

In this paper, we compare three different trotting techniques and five different turning strategies on a small, compliant, biologically inspired quadrupedal robot, the Oncilla. The locomotion techniques were optimized on the actual hardware using a treadmill setup, without relying on models. We found that using half ellipses as foot trajectories resulted in the fastest gaits, as well as the highest robustness against parameter changes. Furthermore, we analyzed the importance of using the scapulae for turning, from which we observed that although not necessary, they are needed for turning with a higher speed.
sayI : Trusted User Authentication at Internet Scale

With Internet-scale user authentication, an organization authenticates users with which it has no prior association. Of necessity, the organization must rely on third parties, which make up the authentication infrastructure and can vouch for these users. These third parties are trusted. And since different organizations have different adversaries and different security needs, it is up to the organization to determine which third parties to trust. Unfortunately authentication infrastructures which meet the above trust requirements have been inefficient, suffering from high latency, excessive bandwidth, and high CPU load. These inefficiencies significantly impede wide-scale deployment. We introduce sayI, a Public-Key based authentication Infrastructure (PKI). It is the first PKI which is efficient at Internet scale and enables organizations to determine their risk from third parties. It protects privacy and provides security. It is designed to minimize bandwidth and latency through a careful and novel integration of authorization and authentication. In sayI, irrelevant certificates do not negatively impact performance. An Internet user authentication is guaranteed to complete in a single Internet round trip, significantly faster than alternative authentication infrastructures.
Isa be lle β α Isar The Isabelle / Isar Reference Manual

Intelligible semi-automated reasoning (Isar) is a generic approach to readable formal proof documents. It sets out to bridge the semantic gap between any internal notions of proof based on primitive inferences and tactics, and an appropriate level of abstraction for user-level work. The Isar formal proof language has been designed to satisfy quite contradictory requirements, being both “declarative” and immediately “executable”, by virtue of the Isar/VM interpreter. The current version of Isabelle offers Isar as an alternative proof language interface layer. The Isabelle/Isar system provides an interpreter for the Isar formal proof document language. The input may consist either of proper document constructors, or improper auxiliary commands (for diagnostics, exploration etc.). Proof texts consisting of proper document constructors only, admit a purely static reading, thus being intelligible later without requiring dynamic replay that is so typical for traditional proof scripts. Any of the Isabelle/Isar commands may be executed in single-steps, so basically the interpreter has a proof text debugger already built-in. Employing the Isar instantiation of Proof General, the generic Emacs interface for interactive proof assistants of LFCS Edinburgh, we arrive at a reasonable environment for live document editing. Thus proof texts may be developed incrementally by issuing proper document constructors, including forward and backward tracing of partial documents; intermediate states may be inspected by diagnostic commands. The Isar subsystem is tightly integrated into the Isabelle/Pure metalogic implementation. Theories, theorems, proof procedures etc. may be used interchangeably between classic Isabelle proof scripts and Isabelle/Isar documents. Isar is as generic as Isabelle, able to support a wide range of object-logics. Currently, the end-user working environment is most complete for Isabelle/HOL.
Correlative super-resolution fluorescence and electron microscopy of the nuclear pore complex with molecular resolution

ABSTRACT Here, we combine super-resolution fluorescence localization microscopy with scanning electron microscopy to map the position of proteins of nuclear pore complexes in isolated Xenopus laevis oocyte nuclear envelopes with molecular resolution in both imaging modes. We use the periodic molecular structure of the nuclear pore complex to superimpose direct stochastic optical reconstruction microscopy images with a precision of <20 nm on electron micrographs. The correlative images demonstrate quantitative molecular labeling and localization of nuclear pore complex proteins by standard immunocytochemistry with primary and secondary antibodies and reveal that the nuclear pore complex is composed of eight gp210 (also known as NUP210) protein homodimers. In addition, we find subpopulations of nuclear pore complexes with ninefold symmetry, which are found occasionally among the more typical eightfold symmetrical structures.
The presequence pathway is involved in protein sorting to the mitochondrial outer membrane

The mitochondrial outer membrane contains integral α‐helical and β‐barrel proteins that are imported from the cytosol. The machineries importing β‐barrel proteins have been identified, however, different views exist on the import of α‐helical proteins. It has been reported that the biogenesis of Om45, the most abundant signal‐anchored protein, does not depend on proteinaceous components, but involves direct insertion into the outer membrane. We show that import of Om45 occurs via the translocase of the outer membrane and the presequence translocase of the inner membrane. Assembly of Om45 in the outer membrane involves the MIM machinery. Om45 thus follows a new mitochondrial biogenesis pathway that uses elements of the presequence import pathway to direct a protein to the outer membrane.
An Alternative Formulation for the Pricing of Stock Index Futures: Theoretical and Empirical Perspectives

Assuming that a futures price is a function of the underlying asset and the basis, and that a Brownian bridge process drives the basis, this article provides the closed-form solution of futures with basis risk (FBR). The Brownian bridge process ensures that the basis is zero at the maturity of a futures contract. The FBR model is empirically tested with daily S&P500 futures data and is found to outperform both the Cornell and French (CF, 1983a) and Yan (2002) models. The overall mean errors in terms of index points and percentages are 0.1918 and -0.002% for the FBR model, compared to -1.8806 and -0.2088% for the CF model, and 2.5072 and 0.0973% for the Yan model.
A Comparison of Calcium Currents in Rat and Guinea Pig Single Ventricular Cells

The slow inward calcium currents were compared in rat and guinea pig heart using enzymatically dissociated, single ventricular cells. A single electrode voltage damp was used, in which current and voltage were sampled separately using a time-sharing method. Spatial homogeneity of membrane potential during peak slow inward calcium current was assessed by measuring the potential with two microelectrodes 50 μm apart; the potentials were within 3 mV of each other. Peak current-voltage relations for slow inward calcium currents were similar for the two species, but the individual currents showed a faster time course of inactivation and a slower time course of recovery from inactivation for rat, compared with guinea pig. The potassium current blockers 4-aminopyridine and tetraethylammonium chloride did not produce significant effects on the net membrane currents recorded at the holding potentials (−50 to −40 mV) used in this study. The underlying mechanism for the inactivation of the slow inward calcium currents was explored using a double pulse procedure. In both rat and guinea pig heart cells prepulses to very positive potentials were associated with a partial restoration of the slow inward calcium current in the following test pulse. In addition, internal ethylene glycol-bis N,N,N′,N′-tetraacetic acid or substitution of barium for calcium slowed the rate of inactivation of the slow inward calcium current in rat heart cells. Calcium activation of nonspecific currents was thought less likely to have produced these results due to the lack of effect of depolarizing prepulses on hyperpolarizing test pulses. A calcium-dependent component of inactivation may be responsible for the differences observed in both the inactivation and the recovery time courses of the slow inward calcium current in these species.
A Graph-Based Approach for Modeling and Indexing Video Data

In this work, we propose new graph-based data model and indexing to organize and manage video data. To consider spatial and temporal characteristics of video, we introduce a new graph-based data model called spatio-temporal region graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs object graphs (OGs) and background graphs (BGs). In addition, a new distance measure, called extended graph edit distance (EGED), is introduced in metric space for matching and indexing. Based on clustering and EGED, we propose a new indexing method STRG-index, which is faster and more accurate. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-index outperforms the M-tree in terms of cost and speed
Automated ECG Delineation using Machine Learning Algorithms

The aim of automated electrocardiogram (ECG) delineation system is the reliable detection of fundamental ECG components and from these fundamental measurements, the parameters of diagnostic significance, namely, P-duration, PR-interval, QRS-duration, QT- interval, are to be identified and extracted. In this work, two supervised machine learning algorithms, K-Nearest neighbour (KNN) and Support Vector Machine (SVM) have been applied for accurate and efficient delineation of ECG signals. The algorithms were evaluated on a standard database CSE DS-3. The mean and standard deviations of the basic intervals obtained by KNN and SVM algorithms have been calculated and compared with three 12-lead programs used in the CSE study from the combined program median. The results show that the proposed algorithms give a new direction of using KNN and SVM effectively for the identification and delineation of the ECG wave components.
Against the implicit politics of service-dominant logic

Few recent topics in marketing have met such immediate popularity and critique as Vargo and Lusch’s service-dominant logic (SDL). While many have criticized SDL scholarship for a lack of cultural sophistication, coherence, and relevance, it has nevertheless maintained and expanded its own distinct stream of ideas. Recently, Vargo and Lusch have proposed that SDL could be extended into a theory of society. We criticize this notion by contrasting their views on commodity value with Marxist and post-Marxist literatures, finding SDL ill-equipped to understand consumer culture, but also continuing to propagate simplistic and misguided views of “value” in commodity markets. We conclude by challenging SDL’s suitability as candidate for all-encompassing social theorizing because of its tacit neoliberalism.
Sparse 2-D Canonical Correlation Analysis via Low Rank Matrix Approximation for Feature Extraction

Although 2-D canonical correlation analysis (2DCCA) has been proposed to reduce the computational complexity while reserving local data structure of image, the learned canonical variables of 2DCCA are the linear combination of all the original variables, which makes it hard to interpret the solutions and might have less generality. In this paper, we propose a sparse 2-D canonical correlation analysis (S2DCCA) to solve the drawbacks of the 2DCCA method and apply it to image feature extraction. The basic idea of S2DCCA is to impose two lasso penalties on the objective function of 2DCCA to obtain two sets of sparse projection directions via low rank matrix approximation. We conduct extensive experiments on both FERET and AR databases to evaluate the performance of the proposed method.
Encoding scene structures for video compression

We describe an approach to partition a video stream into structure regions that are temporally encoded and disjoint from texture regions that are synthesized so as to preserve the statistical properties of the original data stream. Structures encode regions of an image that can be put into correspondence in different images of the same scene, and are encoded via a dictionary that takes into account spatial and temporal regularities. Textures are synthesized in a manner that preserves perceptual similarity.
Imagining a constructionist game-based pedagogical model: Using tabletop role-playing game creation to enhance literature education in high school English classes

In today’s K-12 educational environment with the newly adopted Common Core State Standards (CCSS), improving student literacy as a foundational skill to obtain success in all other subject areas is one of the most important goals. Unfortunately, many literature curricula suffer from a lack of innovative pedagogy despite the introduction of various educational technologies meant to aid student learning. This study focuses on developing a new game-based constructionist pedagogical model for literature education using tabletop role-playing game creation. Using Shulman’s (1987) Pedagogical Content Knowledge (PCK) that eventually evolved into Mishra and Kohler’s (2006) Technological Pedagogical Content Knowledge (TPACK) as the main theoretical framework, this design-based research shows how tabletop role-playing game creation as a constructionist pedagogical strategy successfully helped high school students to receive the benefits of high-quality literature education.
Novel method for image splicing detection

Splicing is a very common operation performed for image forgery. It involves merging of two or more different images to form a combined image that is significantly different from the original image. In this paper an image splicing detection method based on texture features of spliced image is proposed. The proposed approach calculates grey level run length matrix (GLRLM) texture features for the forged images and original images from CASIA database and DB2 database. The statistical features thus extracted from GLRLM are used for detection of tampering. Support vector machine is used for classification. The proposed algorithm is very effective in detection of splicing forgery as marked by the results.
Monetary Policy Delegation and Equilibrium Coordination

This paper revisits the argument that the stabilisation bias that arises under discretionary monetary policy can be reduced if policy is delegated to a policymaker with redesigned objectives. We study four delegation schemes: price level targeting, interest rate smoothing, speed limits and straight conservatism. These can all increase social welfare in models with a unique discretionary equilibrium. We investigate how these schemes perform in a model with capital accumulation where uniqueness does not necessarily apply. We discuss how multiplicity arises and demonstrate that no delegation scheme is able to eliminate all potential bad equilibria. Price level targeting has two interesting features. It can create a new equilibrium that is welfare dominated, but it can also alter equilibrium stability properties and make coordination on the best equilibrium more likely.
Body Actions Change the Appearance of Facial Expressions

Perception, cognition, and emotion do not operate along segregated pathways; rather, their adaptive interaction is supported by various sources of evidence. For instance, the aesthetic appraisal of powerful mood inducers like music can bias the facial expression of emotions towards mood congruency. In four experiments we showed similar mood-congruency effects elicited by the comfort/discomfort of body actions. Using a novel Motor Action Mood Induction Procedure, we let participants perform comfortable/uncomfortable visually-guided reaches and tested them in a facial emotion identification task. Through the alleged mediation of motor action induced mood, action comfort enhanced the quality of the participant’s global experience (a neutral face appeared happy and a slightly angry face neutral), while action discomfort made a neutral face appear angry and a slightly happy face neutral. Furthermore, uncomfortable (but not comfortable) reaching improved the sensitivity for the identification of emotional faces and reduced the identification time of facial expressions, as a possible effect of hyper-arousal from an unpleasant bodily experience.
Mining Contentious Documents Using an Unsupervised Topic Model Based Approach

This work proposes an unsupervised method intended to enhance the quality of opinion mining in contentious text. It presents a Joint Topic Viewpoint (JTV) probabilistic model to analyse the underlying divergent arguing expressions that may be present in a collection of contentious documents. It extends the original Latent Dirichlet Allocation (LDA), which makes it domain and thesaurus-independent, e.g., does not rely on Word Net coverage. The conceived JTV has the potential of automatically carrying the tasks of extracting associated terms denoting an arguing expression, according to the hidden topics it discusses and the embedded viewpoint it voices. Furthermore, JTV's structure enables the unsupervised grouping of obtained arguing expressions according to their viewpoints, using a constrained clustering approach. Experiments are conducted on three types of contentious documents: polls, online debates and editorials. The qualitative and quantitative analysis of the experimental results show the effectiveness of our model to handle six different contentious issues when compared to a state-of-the-art method. Moreover, the ability to automatically generate distinctive and informative patterns of arguing expressions is demonstrated.
Deep learning in general and deep convolutional neural networks

— There are two major challenges to overcome when developing a classifier to perform automatic disease diagnosis. First, the amount of labeled medical data is typically very limited, and a classifier cannot be effectively trained to attain high disease-detection accuracy. Second, medical domain knowledge is required to identify representative features in data for detecting a target disease. Most computer scientists and statisticians do not have such domain knowledge. In this work, we show that employing transfer learning can remedy both problems. We use Otitis Media (OM) to conduct our case study. Instead of using domain knowledge to extract features from labeled OM images, we construct features based on a dataset entirely OM-irrelevant. More specifically, we first learn a codebook in an unsupervised way from 15 million images collected from ImageNet. The codebook gives us what the encoders consider being the fundamental elements of those 15 million images. We then encode OM images using the codebook and obtain a weighting vector for each OM image. Using the resulting weighting vectors as the feature vectors of the OM images, we employ a traditional supervised learning algorithm to train an OM classifier. The achieved detection accuracy is 88.5% (89.63% in sensitivity and 86.9% in specificity), markedly higher than all previous attempts, which relied on domain experts to help extract features.