Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Inter-Cell Interference Coordination based on rate splitting

We consider distributed Inter-Cell Interference Coordination based on rate splitting and interference cancellation. Link adaptation is performed based on Channel Quality Information both on the desired link and on links towards interference victims. A transmitted message is split into a private and a public part. The private part is decodable only by the intended receiver, whereas the public part is in addition decodable by some interference victims. The receivers run a best-effort interference cancellation algorithm, canceling interfering public signals. A system of two transmission pairs can be analyzed completely. In a network of transmission pairs, representing e.g. a pico/femto-cellular network, we propose a self-organizing rate splitting method, where the network nodes coordinate based on the limited information available at the nodes. Coordination complexity grows linearly with the size of coordination group.
Behavioral diversity in learning robot teams

This research investigates behavioral diversity in learning robot teams. Behavioral diversity refers to the extent to which individual agents assume distinct behavioral roles in a group. Robot groups with behaviorally distinct members are referred to as “behaviorally heterogeneous. ” Most research in multi-robot teams to date has centered on homogeneous systems, with work in heterogeneous groups focused primarily on mechanical and sensor differences. But teams of mechanically identical robots are especially interesting because they may be homogeneous or heterogeneous depending only on behavior. Because of this, behavior is an extremely flexible dimension of heterogeneity in learning teams since the agents select between hetero- or homogeneity on their own. The research focuses on the relationship between the reinforcement function used for learning and behavioral diversity and performance in the resulting multi-robot teams. Important contributions include a methodology for the development and evaluation of learning multi-robot teams and quantitative metrics of robot behavioral difference and robot team diversity.
A Regularization Approach for Identifying Cumulative Lagged Effects in Smart Health Applications

Recent development of wearable sensor technologies have made it possible to capture concurrent data streams for ambient environment and instantaneous physiological stress response at a fine granularity. Characterizing the delay in physiological stress response time to each environment stimulus is as important as capturing the magnitude of the effect. In this paper, we discuss and evaluate a new regularization-based statistical method to determine the ideal lagged effect of five environmental factors-carbon dioxide, temperature, relative humidity, atmospheric pressure and noise levels on instantaneous stress response. Using this method, we infer that the first four environment variables have a cumulative lagged effect, of approximately 60 minutes, on stress response whereas noise level has an instantaneous effect on stress response. The proposed transformations to inputs result in models with better fit and predictive performance. This study not only informs the field of environment-wellbeing research about the cumulative lagged effects of the specified environmental factors, but also proposes a new method for determining optimal feature transformation in similar smart health studies.
A scenario-aware data flow model for combined long-run average and worst-case performance analysis

Data flow models are used for specifying and analysing signal processing and streaming applications. However, traditional data flow models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous data flow model, which uses a stochastic approach to model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware data flow model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware data flow models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example
A compact 22-Gb/s transmitter for optical links with all-digital phase-locked loop

An optical transmitter for driving a Mach-Zehnder (MZ) Interferometer is proposed which incorporates an alldigital phase-locked loop (ADPLL) and a source-series terminated (SST) driver. The proposed optical transmitter is composed of a pattern generator, a clock synthesizer and a modulator driver. The transmitter for an external modulator should drive a large capacitance with a high voltage swing, which requires both large power consumption and area. In this work, the ADPLL is employed for the clock synthesizer to achieve a small area and low power consumption. A high voltage swing over 2 Vdiff-pp with low power is obtained by adopting the SST driver. The implemented transmitter occupies only 0.58 mm2 with power dissipation of 391.6 mW at operating data rate of 21.6 Gb/s.
Pre-publicaciones Del Seminario Matematico 2008 a Comparison of Two Bootstrap Schemes on Goodness of Fit Tests in Presence of Selection Bias a Comparison of Two Bootstrap Schemes on Goodness of Fit Tests in Presence of Selection Bias

If a data set suffers from selection bias then, ordinary regression estimators must be adapted in order to achieve consistency. Consequently , it is necessary to modify any goodness of fit tests based on distances between parametric and non-parametric residuals. More particularly, any bootstrap approximation to these tests must be re– designed. Our first approach to this latter task is based on a resampling of nonparametric residuals in order to obtain dependent variable boot-strap values and plug them into the adapted estimators. The second approach consists of a resampling of weighted residuals, which compensates the selection bias in a such way that ordinary regression estimators could be used. Both alternatives are theoretically analyzed and some simulations studies are carried out to illustrate the behaviour of the two approaches with finite samples.
Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods

segmentation plays an important role in medical imaging by automating detection of false structures and other regions of interest. An image segmentation method partitions an image into multiple segments, representing an image into more meaningful, simpler and easier to analyze. Several general-purpose algorithm and techniques have been developed for image segmentation. This paper explains different segmentation techniques used in medical image analysis addressing the segmentation of abdominal and liver images as case study. Experiments are performed on abdominal and liver CT scan images and the outcomes of these segmentation techniques are discussed. Performance of the methods is presented on the basis of parameters namely, pixel values, mean and standard deviation. Keywordsmentation, thresholding, clustering, artificial neural network, edge detection, region of interest
Mobile Internet Testing: An Analysis of Equivalence, Individual Differences, and Reactions

Mobile internet testing (MIT) is the latest cost effective technological push in employment testing. The ability to access assessments via the internet using mobile devices provides increased speed and convenience for both administrators and respondents. In this article, we examine the equivalence of MIT compared with testing on personal computers (PCs) and whether attitudes and other individual differences influence responses and reactions to MIT. Results demonstrated equivalence for a supervisory situational judgment test across testing modes, but not for a cognitive ability test. Significant relationships were found between anxiety and both performance and reactions to mobile assessment. Respondents also reported significantly more positive reactions when tested on a PC versus a mobile device. Future research and practical implications are discussed.
Is it worthwhile to conduct a randomized controlled trial of glaucoma screening in the United Kingdom?

Objectives To assess the value of conducting a glaucoma screening randomized controlled trial in the UK. Methods Decision model based economic evaluation and value of information analysis. Model derived from a previous health technology assessment. Model updated in terms of structure and parameter estimates with data from surveys, interviews with members of the public and health care providers and routine sources. Results On average, across a range of ages of initiating screening (40–60 years), glaucoma prevalence (1–5%), screening uptake (30–100%), and the performance of current case finding, screening was not cost-effective at a £30,000 threshold per quality adjusted life year (QALY) from the perspective of the National Health Service (NHS). The societal value of removing all uncertainty around glaucoma screening is £107 million at a threshold of £20,000 per QALY. For informing policy decisions on glaucoma screening, reducing uncertainty surrounding the NHS and personal social care cost of sight impairment (£74 million) was of most value, followed by reducing uncertainty in test performance (£14 million) and uptake of either screening or current eye care (£8 million each). Conclusions A glaucoma screening trial in the UK is unlikely to be the best use of research resources. Further research to quantify the costs of sight impairment falling on the NHS and personal social services is a priority. Further development of glaucoma tests and research into strategies to promote the uptake of screening or current eye care such as through the use of a behavioural intervention would be worthwhile.
Beliefs, Fertility, and Earnings of African American, Hispanic, and Non-Hispanic White Mothers

This study explores gender ideology, fertility factors (e.g., age at first birth, number of children), and their effects on earnings of African American (n = 413), Hispanic American (n = 271), and White (n = 817) mothers. An analysis of data from the National Longitudinal Survey of Youth over a 10-year period (1988 to 1998) shows that, on average, Hispanic American and White mothers have a significantly more conservative gender ideology than African American mothers. Nevertheless, a conservative gender ideology significantly reduces African American, Hispanic American, and White mothers' earnings when controlling for a variety of important labor force factors. Regarding fertility, the number of children is detrimental to the earnings of White mothers but has no effect on African American or Hispanic mothers in the sample. Although early childbearing significantly depresses the earnings of African American and Hispanic mothers, it does not do so for their White counterparts.
Amino acid interactions in chick nutrition. 3. Interdependence in amino acid requirements.

Synopsis The quantitative nature of specific interactions between certain indispensable amino acids has been examined in a series of factorial experiments with young chicks. It was demonstrated that when the dietary concentration of lysine was 1.10 per cent, the arginine requirement of the chick was 0.80 per cent. When the lysine concentration was raised to 1.35, 1.60 or 1.85 per cent of the diet, the arginine requirement was increased to 0.92, 1.04 and 1.15 per cent of the diet respectively. In another experiment, it was found that when due importance had been attached to the interaction between lysine and arginine, the requirements for these amino acids could be lowered to 0.94 per cent and 0.81 per cent respectively. These concentrations are considerably lower than those normally recommended for young chicks. The interrelationship between leucine and isoleucine has also been defined in quantitative terms. At concentrations of 1.40, 2.15 or 2.90 per cent leucine, the isoleucine requirement was 0.58, 0.6.
Analytical Evaluation of Uncertainty Propagation in Seismic Vulnerability Assessment of Tehran Using GIS

Disaster management processes are generally characterizing real world as composition of various criteria to overcome damage consequences. This situation usually entails development of hypotheses based on epistemic and multi-criteria treatments. These hypotheses have to be properly structured using logics which somehow handle uncertainties like fuzzy logic. One of the properties of geospatial information systems is their use in supporting spatial decision making under uncertainty. It is a complex process which is considered in different situations. The existence of uncertainty in geospatial data and various analyses has the potential to expose users to undesirable consequences in their decision making. Nowadays, natural disasters, particularly earthquake, are among the most important disturbances to sustainable development of countries and governments try to manage them in an optimum manner. They would typically try to decrease the amount of financial damages and loss of lives that would occur because of the events. In this research, geospatial information science/system has been implemented to estimate the seismic vulnerability and its probable damages for a particular scenario in Tehran. We applied fuzzy logic concepts to well known analytical hierarchical process for the damage assessment. Based on this modified approach we developed a hierarchy of effective factors in earthquake vulnerability due to definition of their priorities against a given earthquake scenario. The effect of uncertainty in geospatial data and analysis functions which are applied in estimating Tehran seismic vulnerability would affect the quality of decision making for estimating the damages. In this paper, we analyzed the implemented geospatial data of population statistics, building information, maps, digital terrain models and satellite images in the process of studying Tehran’s seismic vulnerability. In this research, we used Monte Carlo simulation approach for uncertainty modeling. We computed the statistical parameters for seismic vulnerability in various iterations of Monte Carlo process. After that we extracted the relationship between the number of Monte Carlo process and relative variation of seismic vulnerability’s layer variance. As a result of the analysis undertaken in this research, the number of iterations required to increase the precision of seismic vulnerability assessment is inversely related to the square root of the precision.
Development of Automatic Network Replication System for OpenFlow Network

In this study, we developed an automatic network replication system for Open Flow network. This system can automatically and virtually replicate Open Flow network according to captured information. This system consists of mainly two functions: Message Capture Function (MCF), Network Emulation Function (NEF). MCF is located between Open Flow controller and Open Flow switch. MCF can capture message information that is exchanged between Open Flow controller and Open Flow switch. In addition by use of LLDP, MCF can detect link information between Open Flow switches. Then, MCF performs topology inference by link information. NEF can replicate Open Flow network according to captured message information and link information. Users can do a test newly developed function of Open Flow freely and easily on just one PC by use of this system.
Process Tracing Methods in Decision Support Systems Research: Exploring the Black Box

An overview is provided of the appficability to DSS research of process tracing methodologies in general and verbal protocol analysis in particular. Rationale is developed for why process tracing methods are an important addition to the inventory of methodologies available to researchers, stressing the need to explore the "black box" of decision processes. A variety of process tracing methods are presented, along with their relative strengths and weaknesses. Verbal protocol analysis is discussed as a particularly promising method for use in DSS research. The nature of verbal protocols is outlined and methods for analysis are reviewed. The criticisms of this method and the controversy surrounding its use are discussed, and an assessment of the validity of these criticisms is provided. Finally, areas of DSS research in which protocols may prove valuable are identified and relevant studies are examined.
Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments

In recent years, a number of studies have been done on Location-Based Service (LBS) due to their wide range of potential applications. In this paper, we propose a novel data mining algorithm named Cluster-based Mobile Sequential Pattern Mine (CMSP-Mine) for efficiently discovering the Cluster-based Mobile Sequential Patterns (CMSPs) of users in LBS environments. In CMSP-Mine, we first propose a transaction similarity measurement named Location-Based Service Alignment (LBS-Alignment) to evaluate the similarity between two mobile transaction sequences. Then, we propose a transaction clustering algorithm named Cluster-Object based Smart Cluster Affinity Search Technique (CO-Smart-CAST) to form a user cluster model of the mobile transactions based on LBS-Alignment. Furthermore, we proposed the novel prediction strategy that utilizes the discovered CMSPs to precisely predict the next movement of mobile users. To our best knowledge, this is the first work on mining the mobile sequential patterns associated with moving path and user clusters in LBS environments. Finally, through a series of experiments, our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy andapplicability under various system conditions.
The Factor Binding to the Glucocorticoid Modulatory Element of the Tyrosine Aminotransferase Gene Is a Novel and Ubiquitous Heteromeric Complex (*)

Glucocorticoid induction of the tyrosine aminotransferase gene deviates from that of many glucocorticoid-responsive genes by having a lower EC and displaying more agonist activity with a given antiglucocorticoid. A cis-acting element, located 3646 base pairs upstream of the start of tyrosine aminotransferase gene transcription, has been found to be sufficient to reproduce these variations with heterologous genes and promoters (Oshima, H., and Simons, S. S., Jr.(1992) Mol. Endocrinol. 6, 416-428). This element has been called a glucocorticoid modulatory element, or GME. Others have called this sequence a cyclic AMP-responsive element (CRE) due to the binding of the cyclic AMP response element binding protein (CREB). We now report the partial purification and characterization of two new proteins (GMEB1 and −2) of 88 and 67 kDa that bind to the GME/CRE as a heteromeric complex. This purification was followed by the formation of a previously characterized, biologically relevant band in gel shift assays. By several biochemical criteria, the GMEBs differed from many of the previously described CREB/CREM/ATF family members. Partial peptide sequencing revealed that the sequences of these two proteins have not yet been described. Size exclusion chromatography and molecular weight measurements of the gel-shifted band demonstrated that the GMEBs bound to the GME as a macromolecular complex of about 550 kDa that could be dissociated by deoxycholate. Similar experiments showed that CREB bound to the GME as heteromeric complexes of about 310 and 360 kDa. As determined from gel shift assays, GMEB1 and −2 are not restricted to rat liver cells but appear to be ubiquitous. Thus, these novel GMEBs may participate in a similar modulation of other glucocorticoid-inducible genes in a variety of cells.
No evidence for the positive relationship between genetic correlations and heritabilities

Quantitative genetics predicts, that traits subjected to strong selection should show low heritability and may yield biased estimates of genetic correlations (rg). Similar pattern may also appear if genetic sources of variation are confounded with non-genetic sources. Thus, a positive relationship between genetic correlations and heritabilities (h2) of underlying traits might be observed. Here we test this prediction using a large dataset of published estimates of genetic correlations and employing a powerful meta-analytical approach. We considered both between-traits and cross-sex genetic correlations. We failed to find support for the prediction about a positive rg – h2 relationship: our analysis based on nearly 1000 published estimates of genetic parameters indicates that the predicted relationship is weak and statistically non-significant. Thus, low heritability does not preclude the possibility of detecting substantial genetic correlations. Our meta-analysis indicates that published estimates of genetic parameters coming from various experimental designs and obtained using different statistical techniques are not significantly biased in case of weakly-heritable traits.
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision applications, such as self-driving cars and virtual reality. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these point features are only sampled randomly in images, without considering geometric information such as lines, leading to large errors with the existence of poorly textured areas or in motion blur. Line segments are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.
Infinite horizon control and minimax observer design for linear DAEs

In this paper we construct an infinite horizon minimax state observer for a linear stationary differential-algebraic equation (DAE) with uncertain but bounded input and noisy output. We do not assume regularity or existence of a (unique) solution for any initial state of the DAE. Our approach is based on a generalization of Kalman's duality principle. In addition, we obtain a solution of infinite-horizon linear quadratic optimal control problem for DAE.