Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Feedback Reduction of Relay Aided Multiuser Networks using Compressive Sensing

In this paper, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). To minimize the effect of noise on the estimated CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI and derive the error covariance matrix of the post-detection noise. In addition to this, we provide exact closed form expressions for the average maximum equivalent SNR at the destination user. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by scheduling schemes that require dedicated error-free feedback from all the network users.
Bayesian filtering for nonlinear state-space models in symmetric α-stable measurement noise

Bayesian filtering appears in many signal processing problems, reason why it attracted the attention of many researchers to develop efficient algorithms, yet computationally affordable. In many real systems, it is appropriate to consider α-stable noise distributions to model possible outliers or impulsive behavior in the measurements. In this paper, we consider a nonlinear state-space model with Gaussian process noise and symmetric α-stable measurement noise. To obtain a robust estimation framework we consider that both process and measurement noise statistics are unknown. Using the product property of α-stable distributions we rewrite the measurement noise in a conditionally Gaussian form. Within this framework, we propose an hybrid sigma-point/Monte Carlo approach to solve the Bayesian filtering problem, what leads to a robust method against both outliers and a weak knowledge of the system.
Roles of Protein Ubiquitination and Degradation Kinetics in Biological Oscillations

Protein ubiquitination and degradation play important roles in many biological functions and are associated with many human diseases. It is well known that for biochemical oscillations to occur, proper degradation rates of the participating proteins are needed. In most mathematical models of biochemical reactions, linear degradation kinetics has been used. However, the degradation kinetics in real systems may be nonlinear, and how nonlinear degradation kinetics affects biological oscillations are not well understood. In this study, we first develop a biochemical reaction model of protein ubiquitination and degradation and calculate the degradation rate against the concentration of the free substrate. We show that the protein degradation kinetics mainly follows the Michaelis-Menten formulation with a time delay caused by ubiquitination and deubiquitination. We then study analytically how the Michaelis-Menten degradation kinetics affects the instabilities that lead to oscillations using three generic oscillation models: 1) a positive feedback mediated oscillator; 2) a positive-plus-negative feedback mediated oscillator; and 3) a negative feedback mediated oscillator. In all three cases, nonlinear degradation kinetics promotes oscillations, especially for the negative feedback mediated oscillator, resulting in much larger oscillation amplitudes and slower frequencies than those observed with linear kinetics. However, the time delay due to protein ubiquitination and deubiquitination generally suppresses oscillations, reducing the amplitude and increasing the frequency of the oscillations. These theoretical analyses provide mechanistic insights into the effects of specific proteins in the ubiquitination-proteasome system on biological oscillations.
ECG Based Recognition Using Second Order Statistics

This paper investigates the applicability of electrocardiogram (ECG) signals for human recognition. Current approaches apply feature extraction on a fiducial points basis. In this paper we demonstrate an autocorrelation based feature extraction approach, in conjunction with the discrete cosine transform or linear discriminant analysis. As an optimization, we introduce a template matching technique that substantially improves the classification performance while also acting as an intruder detector. The experimental results show considerably high recognition rates, rendering identification applications based on ECG very promising.
Fras1, a basement membrane-associated protein mutated in Fraser syndrome, mediates both the initiation of the mammalian kidney and the integrity of renal glomeruli

FRAS1 is mutated in some individuals with Fraser syndrome (FS) and the encoded protein is expressed in embryonic epidermal cells, localizing in their basement membrane (BM). Syndactyly and cryptophthalmos in FS are sequelae of skin fragility but the bases for associated kidney malformations are unclear. We demonstrate that Fras1 is expressed in the branching ureteric bud (UB), and that renal agenesis occurs in homozygous Fras1 null mutant blebbed (bl) mice on a C57BL6J background. In vivo, the bl/bl bud fails to invade metanephric mesenchyme which undergoes involution, events replicated in organ culture. The expression of glial cell line-derived neurotrophic factor and growth-differentiation factor 11 was defective in bl/bl renal primordia in vivo, whereas, in culture, the addition of either growth factor restored bud invasion into the mesenchyme. Mutant primordia also showed deficient expression of Hoxd11 and Six2 transcription factors, whereas the activity of bone morphogenetic protein 4, an anti-branching molecule, was upregulated. In wild types, Fras1 was also expressed by nascent nephrons. Foetal glomerular podocytes expressed Fras1 transcripts and Fras1 immunolocalized in a glomerular BM-like pattern. On a mixed background, bl mutants, and also compound mutants for bl and my, another bleb strain, sometimes survive into adulthood. These mice have two kidneys, which contain subsets of glomeruli with perturbed nephrin, podocin, integrin α3 and fibronectin expression. Thus, Fras1 protein coats branching UB epithelia and is strikingly upregulated in the nephron lineage after mesenchymal/epithelial transition. Fras1 deficiency causes defective interactions between the bud and mesenchyme, correlating with disturbed expression of key nephrogenic molecules. Furthermore, Fras1 may also be required for the formation of normal glomeruli.
Regularization opportunities for the Gauss-Newton and BFGS algorithms : application to the di ff use optical tomography problem

In optical tomography, the optical properties of the medium under investigation are obtained through the solution of an inverse problem where some light is injected on a boundary and the measurement of intensity is performed elsewhere on boundary. The properties of interest are the scattering and the absorption coefficients, denoted σ(x) and κ(x) where x stands for the space variable in a bounded region. Such an inverse problem is solved through optimization with the help of gradient-type methods. Since it is well known that such inverse problems are ill-posed, regularization is required. This paper compares three distinct regularization strategies for two very different kinds of optimization algorithms, namely the Gauss-Newton and the L-BFGS algorithms for the two-dimensional diffuse approximation model of radiation propagation. The conclusion is that the use of Tikhonov regularization is absolutely compulsary when considering optimization algorithms that rely on matrix inversion. Moreover, combining this regularization with appropriate parameterization enhances the quality of reconstructions. However, the Tikhonov regularization does not bring much improvements when considering optimizers that do not rely on matrix inversion, while the combination of an appropriate parameterization of the control space and the use of Sobolev gradients brings much more improvements. Finally, a parallel BFGS algorithm is proposed and applied to the three-dimensional diffuse approximation model.
C-TEST: Supporting novelty and diversity in testfiles for search tuning

Tuning a search facility such as a Web search engine, or an enterprise search tool deployed in a particular organisation, is an economically important activity. Intuitively, an important end goal of tuning should be to maximise satisfaction across the searchers who will use the facility. Tuning should therefore use an unbiased sample of actual search requests, a judging process accurately modelling that of real searchers, and measures optimally correlated with real satisfaction. The process must reflect that the result set for a particular query may be judged by submitters with different underlying information needs. It should also model users’ impatience with duplicate results. Here, we describe a formally-defined, practical, public-domain testfile format suitable for use in batch tuning but capable of reflecting these important aspects. We hope that the formats will soon be supported by all major academic IR systems. DTDs for test and result files, plus an associated toolkit and C-TEST example testfiles for some TREC tasks, are available at es.csiro. au/C-TEST.
Multiple brain networks contribute to the acquisition of bias in perceptual decision-making

Bias occurs in perceptual decisions when the reward associated with a particular response dominates the sensory evidence in support of a choice. However, it remains unclear how this bias is acquired and once acquired, how it influences perceptual decision processes in the brain. We addressed these questions using model-based neuroimaging in a motion discrimination paradigm where contextual cues suggested which one of two options would receive higher rewards on each trial. We found that participants gradually learned to choose the higher-rewarded option in each context when making a perceptual decision. The amount of bias on each trial was fit well by a reinforcement-learning model that estimated the subjective value of each option within the current context. The brain mechanisms underlying this bias acquisition process were similar to those observed in reward-based decision tasks: prediction errors correlated with the fMRI signals in ventral striatum, dlPFC, and parietal cortex, whereas the amount of acquired bias correlated with activity in ventromedial prefrontal (vmPFC), dorsolateral frontal (dlPFC), and parietal cortices. Moreover, psychophysiological interaction analysis revealed that as bias increased, functional connectivity increased within multiple brain networks (dlPFC-vmPFC-visual, vmPFC-motor, and parietal-anterior-cingulate), suggesting that multiple mechanisms contribute to bias in perceptual decisions through integration of value processing with action, sensory, and control systems. These provide a novel link between the neural mechanisms underlying perceptual and economic decision-making.
Hybrid logical-statistical simulation with thermal and IR-drop mapping for degradation and variation prediction

We present a statistical life-time description for digital systems, which is characterised by short term functional simulation. Temperature and IR-drops for each hardware task of the system are regarded based on a coarse RT floorplan and a component wise prediction of the dynamic and leakage power. By iteratively updating threshold voltage and supply resistances, then dynamic and leakage power, then temperature and IR-drop distribution, electro-thermal coupling as well as long term degradation effects can be described.
Robust control and rate coordination for efficiency and fairness in ABR traffic with explicit rate marking

The problem of efficiency and fairness of ABR (available bit rate) traffic with explicit rate-marking is investigated from a control point of view. It is revealed that due to significant uncertainty on time delays in ABR traffic and strong interaction among switches, ABR traffic imposes a great challenge to control strategy development. Focusing on robustness of traffic control and asymptotic fairness, we propose a new control strategy that employs robust control to accommodate delay uncertainty and rate coordination for fairness. Conditions are presented under which robust stability and convergence are guaranteed.
The Social Patterning of Tobacco Use Among Women in Jordan: The Protective Effect of Education on Cigarette Smoking and the Deleterious Effect of Wealth on Cigarette and Waterpipe Smoking.

INTRODUCTION The presence of social inequalities in tobacco-use has been fully recognized in the international literature. Even though cigarette and waterpipe tobacco smoking (WTS) are prevalent in the Arab region, the literature has not addressed the social determinants of the impending tobacco epidemic. This study examined the socioeconomic patterning of cigarette and WTS among Jordanian women. METHODS We analyzed pooled data from four waves of the Jordan Demographic and Health Surveys: 2002 (N = 5851); 2007 (N = 10 654); 2009 (N = 9879), and 2012 (N = 11 113). We specified logistic regression models to test the association between education and household wealth and the two outcome measures, cigarette and WTS, adjusting for other covariates. For each outcome, we ran time-unadjusted and time-adjusted logistic models. RESULTS Cigarette smoking prevalence among Jordanian women remained almost constant (around 10%) between 2002 and 2012. WTS prevalence steadily increased from 4.1% in 2002 to 10.2% in 2012. Increasing education predicted lower odds of cigarette smoking, whereas increasing household wealth weakly predicted higher odds. As to WTS, increasing household wealth strongly predicted higher odds of use. CONCLUSIONS Among Jordanian women, increasing education is protective against cigarette smoking. Household wealth, on the other hand, exerts a deleterious effect on both forms of tobacco consumption, particularly WTS. This pattern shows that Jordan has not fully undergone the socioeconomic crossover in tobacco prevalence which characterizes high-income countries. Future control policies should aim to decrease prevalence but also preempt increasing social inequalities in tobacco use.
RNA Editing in Chloroplasts of Spirodela polyrhiza, an Aquatic Monocotelydonous Species

RNA editing is the post-transcriptional conversion from C to U before translation, providing a unique feature in the regulation of gene expression. Here, we used a robust and efficient method based on RNA-seq from non-ribosomal total RNA to simultaneously measure chloroplast-gene expression and RNA editing efficiency in the Greater Duckweed, Spirodela polyrhiza, a species that provides a new reference for the phylogenetic studies of monocotyledonous plants. We identified 66 editing sites at the genome-wide level, with an average editing efficiency of 76%. We found that the expression levels of chloroplast genes were relatively constant, but 11 RNA editing sites show significant changes in editing efficiency, when fronds turn into turions. Thus, RNA editing efficiency contributes more to the yield of translatable transcripts than steady state mRNA levels. Comparison of RNA editing sites in coconut, Spirodela, maize, and rice suggests that RNA editing originated from a common ancestor.
A Novel pH-dependent Drift Improvement Method for Zirconium Dioxide Gated pH-Ion Sensitive Field Effect Transistors

A novel compensation method for Zirconium dioxide gated Ion Sensitive Field Effect Transistors (ISFETs) to improve pH-dependent drift was demonstrated. Through the sequential measurements for both the n-channel and p-channel ISFETs, 75–100% pH-dependent drift could be successfully suppressed for the first seven hours. As a result, a nearly constant drift rate versus pH value was obtained, which increases the accuracy of pH measurements. Meanwhile, the drawback of the hyperbolic-like change with time of the common drift behavior for ISFETs was improved. A state-of-the-art integrated scheme adopting this method was also illustrated.
A methodological approach for the evaluation of preparedness of pharmaceutical services.

OBJECTIVE The aim of this article is to provide an outcome evaluation model for preparedness of pharmaceutical services (PS) in disaster situations. METHODS A five-step evaluation model development was conducted. The first step was a search of disaster preparedness and PS literature. The second step consisted of a description of the political and organizational aspects, external context, implementation, and performance of PS in disaster preparedness. A theoretical model on PS preparedness in disaster situations, encompassing pharmaceutical services variables and measures of preparedness, was developed as the third step. The fourth step produced a comprehensive theoretical model for evaluating PS preparedness, combining the two approaches used in steps two and three. The fifth and final step examined the development of the indicator framework. RESULTS The results of this methodological approach are presented in the logic model of PS preparedness and the indicator framework, both of which were developed based on the disaster preparedness and PS literature and organized to provide a structured evaluation approach. CONCLUSIONS PS was conceptualized as a program that can be evaluated by measurable effects. These effects can only be measured based on documented, on-site conditions before and after an event recognized as a disaster situation. This conceptual approach is context-modulated and therefore only applicable where the logistic cycle has been adopted as the rationale for PS.
Stability of Rigidly Rotating Relativistic Stars with Soft Equations of State against Gravitational Collapse

We study secular stability against a quasi-radial oscillation for rigidly rotating stars with soft equations of state in general relativity. The polytropic equations of state with polytropic index n between 3 and 3.05 are adopted for modeling the rotating stars. The stability is determined in terms of the turning-point method. It is found that (1) for n ≳ 3.04, all the rigidly rotating stars are unstable against the quasi-radial oscillation and (2) for n ≳ 3.01, the nondimensional angular momentum parameter q ≡ cJ/GM2 (where J, M, G, and c denote the angular momentum, the gravitational mass, the gravitational constant, and the speed of light, respectively) for all marginally stable rotating stars is larger than unity. A semianalytic calculation is also performed, and good agreement with the numerical results is confirmed. The final outcome after axisymmetric gravitational collapse of rigidly rotating and marginally stable massive stars with q > 1 is predicted, assuming that the rest-mass distribution as a function of the specific angular momentum is preserved and that the pressure never halt the collapse. It is found that even for 1 < q ≲ 2.5, a black hole may be formed as a result of the collapse, but for q ≳ 2.5, the significant angular momentum will prevent the direct formation of a black hole.
Limited Bifurcation Asymmetry in Coronary Arterial Tree Models Generated by Constrained Constructive Optimization

Models of coronary arterial trees are generated by the algorithm of constrained constructive optimization (CCO). In a given perfusion area a binary branching network of straight cylindrical tubes is generated by successively adding terminal segments to the growing structure. In each step the site of connection is chosen according to an optimization target function (total intravascular volume), and in any stage of development the tree fulfills physiologic boundary conditions (constraints involving pressures, flows and bifurcation rules). CCO generates structures which in many aspects resemble real coronary arterial trees, except for very asymmetric bifurcations, occurring when a large branch gives off a tiny terminal segment. In the present work we evaluate an additional constraint within CCO, namely imposing a limit on the asymmetry of bifurcations during the construction process. Model trees are grown with different limits imposed, and the effects on structure are studied both phenomenologically and via statistical descriptors. As the limit to asymmetry is tightened, blood is conveyed to the perfusion sites via detours rather than directly and the comparison with measured data shows the structure to change from a conveying to a delivering type of function. Simultaneously total intravascular volume, surface and sum of segments' lengths increase. It is shown why and how local bifurcation asymmetry is able to determine the global structure of the optimized arterial tree model. Surprisingly, the pressure profile from inlet to terminals, being a functional characteristic, remains unaffected.
Improved Templates for Photometric Redshifts of Submillimeter Sources

There is growing evidence that some star-forming galaxies at z > 1 are characterized by high efficiencies and specific star formation rates. In the local universe, these traits are shared by "active" blue compact dwarf galaxies (BCDs) with compact and dense star-forming regions. The spectral energy distributions (SEDs) of these BCDs are dominated by young massive star clusters, embedded in a cocoon of dust. In this Letter, we incorporate these BCD SEDs as templates for two samples of high-redshift galaxy populations selected at submillimeter wavelengths. Because of the severe absorption of the optical light, the featureless mid-infrared spectrum, and the relatively flat radio continuum, the dusty star-cluster SEDs are good approximations to most of the submillimeter sources in our samples. In most cases, the active BCD SEDs fit the observed photometric points better than the "standard" templates, M82 and Arp 220, and predict photometric redshifts significantly closer to the spectroscopic ones. Our results strongly suggest that the embedded dusty star clusters in BCD galaxies are superior to other local templates such as M82 and Arp 220 in fitting distant submillimeter starburst galaxies.
Probabilistic Intelligent Systems for Thermal Power Plants Sistemas Inteligentes Probabilistas para Plantas Termoeléctricas

Artificial Intelligence applications in large-scale industry, such as thermal power plants, require the ability to manage uncertainty because current applications are large, complex and influenced by unexpected events and their evolution in time. This paper shows some of the efforts developed at the Instituto de Investigaciones Electricas (IIE) to assist operators of thermal power plants in the diagnosis and planning tasks using probabilistic intelligent systems. A diagnosis system, a planning system and a decision support system are presented. The diagnosis system is based on qualitative probabilistic networks, and the decision support system uses influence diagrams. The planning system is based on the Markov Decision Processes formalism. These approaches were validated in different power plant applications. Current results have shown that the use of probabilistic techniques can play an important role in the design of intelligent support systems for thermal power plants.
Extracting Road Networks from Natural Terrain for Visualization

The ability to effectively generate and assimilate terrain information is central to many computer vision (CV) and computer graphics (CG) applications. In particular, effective interactive visualization applications require techniques for generating a digital elevation map (DEM) and ortho-image, classifying ground cover, and extracting symbolic information. In this paper we describe a program underway at the University of Massachusetts for generating complex geospatial databases using photogrammetric techniques and context-sensitive strategies. We present a specific example in which the input are two monochromatic aerial images of a rural area of Fort Hood, Texas, and the results are a DEM, ortho-image, classified ground cover and a symbolic representation of a road network.