Miracle-dz/UniFAR-FTUs huggingface dataset:
Trivariate mover‐stayer counting process models for investigating joint damage in psoriatic arthritis

In psoriatic arthritis, many patients do not develop permanent joint damage even after a prolonged follow‐up. This has led several authors to consider the possibility of a subpopulation of stayers (those who do not have the propensity to experience the event of interest), as opposed to assuming the entire population consist of movers (those who have the propensity to experience the event of interest). In addition, it is recognised that the damaged joints process may act very differently across different joint areas, particularly the hands, feet and large joints. From a clinical perspective, interest lies in identifying possible relationships between the damaged joints processes in these joint areas for the movers and estimating the proportion of stayers in these joint areas, if they exist. For this purpose, this paper proposes a novel trivariate mover‐stayer model consisting of mover‐stayer truncated negative binomial margins, and patient‐level dynamic covariates and random effects in the models for the movers and stayers, respectively. The model is then extended to have a two‐level mover‐stayer structure for its margins so that the nature of the stayer property can be investigated. A particularly attractive feature of the proposed models is that only an optimisation routine is required in their model fitting procedures. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction

Objective: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multiclass dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. Significance: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.
Identification and chemical characterization of N‐acyl‐homoserine lactone quorum sensing signals across sponge species and time

ABSTRACT Marine sponges form symbiotic relationships with complex microbial communities, yet little is known about the mechanisms by which these microbes regulate their behavior through gene expression. Many bacterial communities regulate gene expression using chemical signaling termed quorum sensing. While a few previous studies have shown presence of N‐acyl‐homoserine lactone (AHL)‐based quorum sensing in marine sponges, the chemical identity of AHL signals has been published for only two sponge species. In this study, we screened for AHLs in extracts from 15 sponge species (109 specimens in total) from the Mediterranean and Red Sea, using a wide‐range AHL biosensor. This is the first time that AHL presence was examined over time in sponges. We detected the presence of AHL in 46% of the sponge species and found that AHL signals differ for certain sponge species in time and across sponge individuals. Furthermore, for the Mediterranean sponge species Sarcotragus fasciculatus, we identified 14 different AHLs. The constant presence of specific AHL molecules in all specimens, together with varying signaling molecules between the different specimens, makes Sa. fasciculatus a good model to further investigate the function of quorum sensing in sponge‐associated bacteria. This study extends the knowledge of AHL‐based quorum sensing in marine sponges.
Activation of the NRF2 antioxidant program generates an imbalance in central carbon metabolism in cancer

During tumorigenesis, the high metabolic demand of cancer cells results in increased production of reactive oxygen species. To maintain oxidative homeostasis, tumor cells increase their antioxidant production through hyperactivation of the NRF2 pathway, which promotes tumor cell growth. Despite the extensive characterization of NRF2-driven metabolic rewiring, little is known about the metabolic liabilities generated by this reprogramming. Here, we show that activation of NRF2, in either mouse or human cancer cells, leads to increased dependency on exogenous glutamine through increased consumption of glutamate for glutathione synthesis and glutamate secretion by xc- antiporter system. Together, this limits glutamate availability for the tricarboxylic acid cycle and other biosynthetic reactions creating a metabolic bottleneck. Cancers with genetic or pharmacological activation of the NRF2 antioxidant pathway have a metabolic imbalance between supporting increased antioxidant capacity over central carbon metabolism, which can be therapeutically exploited.
Post-annealing behaviors of small-size synchronous reluctance motors

The paper reports the experimental results of a vast test campaign conducted on synchronous reluctance motors of fractional power. These motors have relatively small geometric dimensions and it is important to evaluate the negative effects of lamination punching. Two twin 550 W motor prototypes have been produced, one with annealed magnetic cores. By means of an experimental-based comparison, the usefulness of the annealing thermal process on the final motor performance is discussed. Finally, to further prove the possibility to correctly account for the punching process in numerical models, the obtained measurement results have been compared with finite element method simulations, getting a good agreement.
Deep Colorization for Facial Gender Recognition

Recent research suggests that colorization models have the capability of generating plausible color versions from grayscale images. In this paper, we investigate whether colorization prior to gender classification improves classification performance on the FERET grayscale face dataset. For this, we colorize the images using an existing Lab colorization model, both with and without class rebalancing, and our novel HSV colorization model without class rebalancing. Then we construct gender classification models on the grayscale and colorized datasets using a reduced GoogLeNet convolutional neural network. Several models are trained using different loss functions (cross entropy loss, hinge loss) and gradient optimization solvers (Nesterov's Accelerated Gradient Descent, Stochastic Gradient Descent), initialized using both random and pre-trained weights. Finally, we compare the gender classification accuracies of the models when applied to the face image color variants. The best performances are obtained by models initialized using pre-trained weights, and models using colorization without class rebalancing.
Color Transfer and Colorization based on Textural Properties

This paper targets two related color manipulation problems: Color transfer for modifying an image colors and colorization for adding colors to a greyscale image. Automatic methods for these two applications propose to modify the input image using a reference that contains the desired colors. Previous approaches usually do not target both applications and suffer from two main limitations: possible misleading associations between input and reference regions and poor spatial coherence around image structures. In this paper, we propose a unified framework that uses the textural content of the images to guide the color transfer and colorization. Our method introduces an edge-aware texture descriptor based on region covariance, allowing for local color transformations. We show that our approach is able to produce results comparable or better than state-of-the-art methods in both applications.
Comparative Analysis of Clustering Methods

The innumerable clustering methods which exist today form the basis of Data Mining and Cluster Analysis. This paper details the distinct classifications of clustering methods, describes prominent examples for each such classification and aims to bring about the comparison between the primary clustering techniques which form the basis of all the others, i.e. the Hierarchical and Partitional algorithms. General Terms Cluster Analysis, Data Mining, Classification
Fixed points and the spontaneous breaking of scale invariance

We investigate critical N-component scalar field theories and the spontaneous breaking of scale invariance in three dimensions using functional renormalization. Global and local renormalization group flows are solved analytically in the infinite N limit to establish the exact phase diagram of the theory including the Wilson-Fisher fixed point and a line of asymptotically safe UV fixed points. We also study the Bardeen-Moshe-Bander phenomenon of spontaneously broken scale invariance and the stability of the vacuum for general regularization. Our findings clarify a long-standing puzzle about the apparent unboundedness of the effective potential. Implications for other theories are indicated.
Top-down impact through a bottom-up mechanism. In situ effects of limpet grazing on growth, light requirements and survival of the eelgrass Zostera marina

Temporal changes in abundance, size, productivity, resource allocation and light requirements of a subtidal eelgrass (Zostera marina L.) population were followed for 2 yr after the September 1993 appearance of a previously rare oval form of the commensal limpet Tectura depicta (Berry) in Monterey Bay, California, USA. By exclusively targeting the epidermis, limpet grazing impaired photosynthetic performance but left respiratory demand, meristematic growth and more than 90% of the leaf biomass intact. The resulting low P:R ratios of grazed plants raised the light requirements for the maintenance of positive carbon balance almost 2-fold relative to healthy ungrazed plants and prevented the summertime accumulation of internal carbon reserves. Shoot density in this once-continuously vegetated 30 ha meadow declined from more than 50 shoots m 2 (2230 g fresh wt [FW] m -2 ) to sparse patches supporting an average of 16 shoots m -2 (380 g FW m -2 ). More than 50 % of the continuously vegetated meadow was converted to bare sand despite ambient light availability and water temperatures that were favorable for growth of healthy, ungrazed plants. Plant size declined by 50% and internal sugar reserves declined more than 4-fold within 6 mo after the appearance of T. depicta. Plant losses were most extensive during winter, when internal carbon reserves were minimal. The dramatic decline in eelgrass vigor and abundance reported here, despite a physical environment that was favorable for healthy eelgrass survival, illustrates the amplification of top-down control by this relatively inconspicuous limpet through a feeding mechanism that specifically impairs photosynthesis, a bottom-up process.
Cache-aided fog radio access networks with partial connectivity

Centralized coded caching and delivery is studied for a partially-connected fog radio access network (F-RAN), whereby a set of H edge nodes (ENs) (without caches), connected to a cloud server via orthogonal fronthaul links, serve K users over the wireless edge. The cloud server is assumed to hold a library of N files, each of size F bits; and each user, equipped with a cache of size MF bits, is connected to a distinct set of r ENs; or equivalently, the wireless edge from the ENs to the users is modeled as a partial interference channel. The objective is to minimize the normalized delivery time (NDT), which refers to the worst case delivery latency, when each user requests a single file from the library. An achievable coded caching and transmission scheme is proposed, which utilizes maximum distance separable (MDS) codes in the placement phase, and real interference alignment (IA) in the delivery phase, and its achievable NDT is presented for r = 2 and arbitrary cache size M, and also for arbitrary values of r when the cache capacity is sufficiently large.
RF-compass: robot object manipulation using RFIDs

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches). This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.
Flocking control of multi-agent system with leader-follower architecture using consensus based estimated flocking center

This paper is concerned about flocking control of a group of mobile agents having leader-follower configuration where the number of leaders is lesser in number than followers. The leaders have the navigation information where the followers only know who the leaders are. Artificial potential function is used to design the distributed control law of the agents. The leaders move as per the tracking information while avoiding collision with other agents. The followers estimate the position of the flocking center and tend to move towards it while aligning velocities and avoiding collisions with neighboring agents. A novel algorithm for estimation of flocking center is introduced in this paper. This algorithm uses consensus concept that makes the followers reach an agreement regarding the flocking center position. The stability analysis of this algorithm shows that the estimation error is asymptotically ε-stable. The agents do not need to know their own positions in global coordinates. The leaders do not access any information from other agents while the followers communicate with its neighboring agents to estimate the flocking center and also to control their motion, the neighborhood being defined by the limited communication range. Simulation results show the effectiveness of the algorithm.
Goal Recognition Design

We propose a new problem we refer to as goal recognitiondesign (grd), in which we take a domain theory and a set ofgoals and ask the following questions: to what extent do theactions performed by an agent within the model reveal its objective, and what is the best way to modify a model so thatany agent acting in the model reveals its objective as early aspossible. Our contribution is the introduction of a new measure we call worst case distinctiveness (wcd) with which weassess a grd model. The wcd represents the maximal lengthof a prefix of an optimal path an agent may take within a system before it becomes clear at which goal it is aiming. Tomodel and solve the grd problem we choose to use the models and tools from the closely related field of automated planning. We present two methods for calculating the wcd of agrd model, one of which is based on a novel compilation to aclassical planning problem. We then propose a way to reducethe wcd of a model by limiting the set of available actions anagent can perform and provide a method for calculating theoptimal set of actions to be removed from the model. Our empirical evaluation shows the proposed solution to be effectivein computing and minimizing wcd.
Serum Potassium Levels and Its Variability in Incident Peritoneal Dialysis Patients: Associations with Mortality

Background Abnormal serum potassium is associated with an increased risk of mortality in dialysis patients. However, the impacts of serum potassium levels on short- and long-term mortality and association of potassium variability with death in peritoneal dialysis (PD) patients are uncertain. Methods We examined mortality-predictability of serum potassium at baseline and its variability in PD patients treated in our center January 2006 through December 2010 with follow-up through December 2012. The hazard ratios (HRs) were used to assess the relationship between baseline potassium levels and short-term (≤1 year) as well as long-term (>1 year) survival. Variability of serum potassium was defined as the coefficient of variation of serum potassium (CVSP) during the first year of PD. Results A total of 886 incident PD patients were enrolled, with 248 patients (27.9%) presented hypokalemia (serum potassium <3.5 mEq/L). During a median follow-up of 31 months (range: 0.5–81.0 months), adjusted all-cause mortality hazard ratio (HR) and 95% confidence interval (CI) for baseline serum potassium of <3.0, 3.0 to <3.5, 3.5 to <4.0, 4.5 to <5.0, and ≥5.0 mEq/L, compared with 4.0 to <4.5 (reference), were 1.79 (1.02–3.14), 1.15 (0.72–1.86), 1.31 (0.82–2.08), 1.33 (0.71–2.48), 1.28 (0.53–3.10), respectively. The increased risk of lower potassium with mortality was evident during the first year of follow-up, but vanished thereafter. Adjusted all-cause mortality HR for CVSP increments of 7.5% to <12.0%; 12.0% to <16.7% and ≥16.7%, compared with <7.5% (reference), were 1.35 (0.67–2.71), 2.00 (1.05–3.83) and 2.18 (1.18–4.05), respectively. Similar association was found between serum potassium levels and its variability and cardiovascular mortality. Conclusions A lower serum potassium level was associated with all-cause and cardiovascular mortality during the first year of follow-up in incident PD patients. In addition, higher variability of serum potassium levels conferred an increased risk of death in this population.
Correcting for binomial measurement error in predictors in regression with application to analysis of DNA methylation rates by bisulfite sequencing

Motivated by a genetic application, this paper addresses the problem of fitting regression models when the predictor is a proportion measured with error. While the problem of dealing with additive measurement error in fitting regression models has been extensively studied, the problem where the additive error is of a binomial nature has not been addressed. The measurement errors here are heteroscedastic for two reasons; dependence on the underlying true value and changing sampling effort over observations. While some of the previously developed methods for treating additive measurement error with heteroscedasticity can be used in this setting, other methods need modification. A new version of simulation extrapolation is developed, and we also explore a variation on the standard regression calibration method that uses a beta‐binomial model based on the fact that the true value is a proportion. Although most of the methods introduced here can be used for fitting non‐linear models, this paper will focus primarily on their use in fitting a linear model. While previous work has focused mainly on estimation of the coefficients, we will, with motivation from our example, also examine estimation of the variance around the regression line. In addressing these problems, we also discuss the appropriate manner in which to bootstrap for both inferences and bias assessment. The various methods are compared via simulation, and the results are illustrated using our motivating data, for which the goal is to relate the methylation rate of a blood sample to the age of the individual providing the sample. Copyright © 2016 John Wiley & Sons, Ltd.
COLLAGEN: when agents collaborate with people

We take the position that autonomous agents, when they interact with people, should be governed by the same principles that underlie human collaboration. These principles come from research in computational linguistics, speciﬁcally collaborative discourse theory, which describes how people communicate and coordinate their activities in the context of shared tasks. We have implemented a prototype toolkit, called Collagen, which embodies collaborative discourse principles
Fault tolerant operation strategy design for modular multilevel converters

In this paper a new post fault operation strategy is proposed for modular multilevel converter (MMC). The conventional MMC fault tolerant operation methods use redundant cells in spinning or cold reserve schemes, however in this work the MMC fault tolerant operation is achieved without requiring any redundant cells. The introduced strategy modifies capacitors reference voltages and arms modulation reference waveforms in faulty leg of MMC while other legs operate normally. The applied technique leads to having some degree of overvoltage in faulty leg capacitors which could be neglected since the number of cells is high. Simple realization and significant economic savings would be attained as main advantages of the proposed post fault operation strategy because of no need to redundant cells. Simulation results verify the good dynamic performance of the implemented technique.
DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas

An organization's databases are often one of its most valuable assets. Data engineers commonly use a relational database because its schema ensures the validity and consistency of the stored data through the specification and enforcement of integrity constraints. To ensure their correct specification, industry advice recommends the testing of the integrity constraints in a relational schema. Since manual schema testing is labor-intensive and error-prone, this paper presents DOMINO, a new automated technique that generates test data according to a coverage criterion for integrity constraint testing. In contrast to more generalized search-based approaches, which represent the current state of the art for this task, DOMINO uses tailored, domain-specific operators to efficiently generate test data for relational database schemas. In an empirical study incorporating 34 relational database schemas hosted by three different database management systems, the results show that DOMINO can not only generate test suites faster than the state-of-the-art search-based method but that its test suites can also detect more schema faults.