Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum independent dominating sets of random cubic graphs

We present a heuristic for finding a small independent dominating set 𝒟 of cubic graphs. We analyze the performance of this heuristic, which is a random greedy algorithm, on random cubic graphs using differential equations, and obtain an upper bound on the expected size of 𝒟. A corresponding lower bound is derived by means of a direct expectation argument. We prove that 𝒟 asymptotically almost surely satisfies 0.2641n ≤ |𝒟| ≤ 0.27942n. © 2002 Wiley Periodicals, Inc. Random Struct. Alg. , 21: 147–161, 2002
Super-potentials for currents on compact Kähler manifolds and dynamics of automorphisms

We introduce a notion of super-potential (canonical function) associated to positive closed (p, p)-currents on compact Kahler manifolds and we develop a calculus on such currents. One of the key points in our study is the use of deformations in the space of currents. As an application, we obtain several results on the dynamics of holomorphic automorphisms: regularity and uniqueness of the Green currents. We also get the regularity, the entropy, the ergodicity and the hyperbolicity of the equilibrium measures. AMS classification : 37F, 32H50, 32U40. Key-words : super-potential, structural variety of currents, moderate measure, Green current, equilibrium measure.
Enhanced Log-Likelihood Ratio Calculation for LDPC Coded SC-FDE Systems in 60-GHz Millimeter-Wave WLAN/WPAN

Single-carrier frequency domain equalization (SC-FDE) has been standardized as an alternative of orthogonal frequency division multiplexing (OFDM) in IEEE 802.11ad and 802.15. 3c. In addition, with the use of low density parity-check (LDPC) codes, multi-Giga bits transmission can be supported in 60-GHz millimeter-wave (mmW) WLAN/WPAN with desired decoding complexity. In this paper, we present and evaluate an enhanced log-likelihood ratio (LLR) calculation method to facilitate the LDPC decoding in SC-FDE. In contrast to the virtual channel (VC) based LLR calculation approach (i.e., the Unique Word (UW) tracking scheme), our proposed method exhibits better bit error rate (BER) performance, and is more spectrally efficient.
Joint question clustering and relevance prediction for open domain non-factoid question answering

Web searches are increasingly formulated as natural language questions, rather than keyword queries. Retrieving answers to such questions requires a degree of understanding of user expectations. An important step in this direction is to automatically infer the type of answer implied by the question, e.g., factoids, statements on a topic, instructions, reviews, etc. Answer Type taxonomies currently exist for factoid-style questions, but not for open-domain questions. Building taxonomies for non-factoid questions is a harder problem since these questions can come from a very broad semantic space. A few attempts have been made to develop taxonomies for non-factoid questions, but these tend to be too narrow or domain specific. In this paper, we address this problem by modeling the Answer Type as a latent variable that is learned in a data-driven fashion, allowing the model to be more adaptive to new domains and data sets. We propose approaches that detect the relevance of candidate answers to a user question by jointly 'clustering' questions according to the hidden variable, and modeling relevance conditioned on this hidden variable. In this paper we propose 3 new models: (a) Logistic Regression Mixture (LRM), (b) Glocal Logistic Regression Mixture (G-LRM) and (c) Mixture Glocal Logistic Regression Mixture (MG-LRM) that automatically learn question-clusters and cluster-specific relevance models. All three models perform better than a baseline relevance model that uses explicit Answer Type categories predicted by a supervised Answer-Type classifier, on a newsgroups dataset. Our models also perform better than a baseline relevance model that does not use any answer-type information on a blogs dataset.
Genetics and Population Analysis an Efficient Hierarchical Generalized Linear Mixed Model for Pathway Analysis of Genome-wide Association Studies

Motivation: In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models. Results: The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS. Availability: The software code for mixed models analysis is freely available at
“Too thin is no longer in” : The influence of exposing ‘healthy vs. too thin’ models on body discrepancy, attitude and purchase intention.

The present study examined the effects of exposure to model types (healthy vs. too thin) on women’s body discrepancy level, attitude towards advertisement and purchase intention. It was argued that next to the type of model, the congruence between the model and the product was important as well. Within this present study, the interaction (congruence) between the model and product was therefore added as an extra dependent variable. In line with other researches, we set up hypotheses. Unfortunately, in our study we were not able to find significant evidence to support any of our hypotheses and we were therefore not able to claim that the model type (too thin vs. healthy) had an influence on body discrepancy, attitude, buying intention or congruence. Results did show that a large part of the samples evaluated themselves with a high body discrepancy rate, meaning that they wanted to lose one body size or more. This led to a higher intention to do sports and work on a healthy body. Furthermore, significant results were found for the effect between the type of model and the perceived attractiveness. Participants perceived a healthy model as more attractive and ‘healthy looking’ compared to the thin model. We could also confirm that the type of product had an effect on buying intention. Participants were more willing to buy a healthy product than an unhealthy product. Additionally, this present research claims that congruence between the model and the product plays an important role in the effectiveness of the advertisement, however, follow up research should examine this more thoroughly and focus more on whether or not participants actually notice the congruence or non-congruence within the advertisement. The study (n=111) was mainly conducted within the cafeteria of a University where women were randomly assigned to one of the four conditions (1. thin model vs. healthy product, 2. thin model vs. unhealthy product, 3. healthy model vs. healthy product, 4. healthy model vs. unhealthy product).
Ice Cream / I Scream for YA Books

From a 40-year perspective, Don Gallo examines the field of young adult literature, comparing it to ice cream—its various flavors and levels of richness. The article proclaims the profundity of the field and the quality of its writers, summarizes historical highlights, defends it against its detractors, and explains the importance of helping students make informed choices by talking about books and having them available in our classrooms. The author also states his personal preferences along with his concern about the increasing length of YA books.
Adalimumab for the treatment of fistulas in patients with Crohn’s disease

Objective: To evaluate the efficacy of adalimumab in the healing of draining fistulas in patients with active Crohn’s disease (CD). Design: A phase III, multicentre, randomised, double-blind, placebo controlled study with an open-label extension was conducted in 92 sites. Patients: A subgroup of adults with moderate to severely active CD (CD activity index 220–450) for ⩾4 months who had draining fistulas at baseline. Interventions: All patients received initial open-label adalimumab induction therapy (80 mg/40 mg at weeks 0/2). At week 4, all patients were randomly assigned to receive double-blind placebo or adalimumab 40 mg every other week or weekly to week 56 (irrespective of fistula status). Patients completing week 56 of therapy were then eligible to enroll in an open-label extension. Main Outcome Measures: Complete fistula healing/closure (assessed at every visit) was defined as no drainage, either spontaneous or with gentle compression. Results: Of 854 patients enrolled, 117 had draining fistulas at both screening and baseline (70 randomly assigned to adalimumab and 47 to placebo). The mean number of draining fistulas per day was significantly decreased in adalimumab-treated patients compared with placebo-treated patients during the double-blind treatment period. Of all patients with healed fistulas at week 56 (both adalimumab and placebo groups), 90% (28/31) maintained healing following 1 year of open-label adalimumab therapy (observed analysis). Conclusions: In patients with active CD, adalimumab therapy was more effective than placebo for inducing fistula healing. Complete fistula healing was sustained for up to 2 years by most patients in an open-label extension trial. ClinicalTrials.gov Identifier: NCT00077779 and NCT00195715.
Spectral saliency model for an appearance only SLAM in an indoor environment

Simultaneous Localization and Mapping, SLAM, is a prime requirement for autonomous navigation and it is an integral part of a modern robot. An incremental appearance only SLAM based on non-quantized local features drastically suffers from large number of landmarks detected in the captured imagery. In order to decrease the number of landmarks, we propose a visual saliency model that is used as first stage in a SLAM algorithm; whereby it detects a small region (25% of image) in the captured imagery. This small region consists of those salient landmarks which are most conspicuous in the surrounding and brain gives more important to them. Only landmarks lying within the selected small salient region of the image are considered for use in the SLAM algorithm. The saliency stage of the algorithm we present is based on a spectral visual saliency model. We analyze its efficacy in terms of Receiver Operating Characteristic, ROC, and shuffled Area Under the Curve, sAUC, based on human fixation data tested on a popular dataset. In the second stage a tailored form of an existing SLAM framework is used to verify the applicability of the proposed saliency stage in a SLAM application. Finally, we use an indoor dataset and with numerous simulations of different parameters of SLAM to show the effectiveness of the proposed approach.
A case report of acute dystonia caused by a therapeutic dose of metoclopramide

Metoclopramide is a dopamine receptor antagonist, frequently used as an antiemetic drug. Side effects include extrapyramidal reactions such as tardive dyskinesia, Parkinsonism, akathisia, neuroleptic malignant syndrome, and acute dystonic reaction requiring acute intervention. While the incidence of acute dystonic reaction is 0.5-1% in children, it is 25% in young adults and elderly. Patients can be misdiagnosed with meningitis, encephalitis, hypocalcaemia, seizures, and tetanus. In this report, a 24-year-old woman with acute dystonia precipitated by a therapeutic dose of metoclopramide, is presented.
Effect of catecholamines, l-epinephrine and l-norepinephrine on coronary flow and oxygen metabolism of the myocardium.

The effect of continuously infused intravenous l-epinephrine and l-norepinephrine (0.1–2.5 gamma/kg/min.) was determined in the open-chest, anesthetized dog prepared for measurement of total coronary flow. Coronary blood flow, myocardial oxygen availability and coronary venous oxygen content consistently increased after catecholamine administration despite wide fluctuations, above and below control values, in heart rate and mean aortic pressure at constant cardiac output. Thus, there was a significant decrease in the coronary arteriovenous oxygen difference after catecholamine administration. The increase in coronary flow and decrease in the coronary A-V oxygen difference were seen even when blood pressure and heart rate were lowered. It is concluded that the departure from the usual relationship between coronary flow and myocardial oxygen consumption is attributable to coronary vasodilatation. However, myocardial oxygen consumption is still the primary factor controlling coronary flow during catecholamin.
SMT: A Reliability Based Interactive DTI Tractography Algorithm

Tractography refers to the in vivo reconstruction of fiber bundles, e.g., in brain, via the analysis of anisotropic diffusion patterns measured by diffusion weighted magnetic resonance imaging (DWI). The data provides a probabilistic model of local diffusion which was shown to correlate with the underlying fibrous structure under certain assumptions. Deterministic tractography suffers from uncertainties at kissing and crossing fibers, at different levels depending on the diffusion model employed (e.g., DTI, HARDI), yet it is easy to interpret and use in clinic. In this study, a novel generic algorithm, split and merge tractography (SMT), is proposed that provides a real-time, interactive and reliability ranked assessment of potential pathways, communicating the true information content of the data without sacrificing the usability of tractography. Specifically, SMT takes in a precomputed set of tracts and the diffusion data (e.g., DTI, HARDI) as its input, generates a set of short (reliable) tracts via splitting at unreliable points and forms quasi-random clusters of short tracts by means of which the space of short tract clusters, representing complete tracts, is sampled. A histogram of thus formed clusters is built in an efficient way and used for real-time, interactive assessment of pathways. The current implementation uses DTI and fourth-order Runge-Kutta integration based streamline tractography as its input. The method is qualitatively assessed on phantom DTI data and real DTI data. Phantom experiments demonstrated that SMT is capable of highlighting the problematic regions and suggesting pathways that are completely overseen by input streamline tractography. Real data experiment results correlate well with known anatomy and also demonstrate that the reliability ranking can efficiently suppress the erroneous tracts interactively. The method is compared to a recent method that also pursues a similar approach, yet in a global optimization based framework. The comparative study on real DTI data revealed the lower computational load of SMT and a better correlation with known anatomy.
Amplification of mitochondrial DNA from archival temporal bone specimens

Archival temporal bone collections are an invaluable resource for studying the molecular genetics of many types of otopathology. The irreplaceable nature of temporal bone sections makes efficiency of DNA extraction of paramount importance. Several protocols are available for extracting DNA from fresh and preserved tissue. To establish the best protocol for reliably extracting DNA from celloidin‐embedded temporal bone sections, a variety of DNA extraction techniques were tested. Using the optimum protocol, mitochondrial DNA fragments ranging in size from less than 100 base pairs to more than 400 base pairs were amplified, and the authenticity of polymerase chain reaction (PCR) products was confirmed through comparative sequence analysis.
Towards a Foundational Framework for Developing and Testing Inter-organizational Business Processes

Modeling and analyzing inter-organizational business processes is complicated substantially by heterogeneous process-modeling languages (e.g., surface vs. analysis languages) as well as by their inherent properties of loose coupling and local control (views). On top, multiple concerns must be addressed when modeling interorganizational business processes, such as process data, behavior, distribution, and resources management. We believe that more research effort is needed towards establishing a language-oriented foundation for modeling inter-organizational business processes. This paper lays out one direction of language engineering towards this goal, collects generic and specific requirements on a language framework for IOPs, and discusses them tentatively.
Longitudinal MRI study of hippocampal volume in trauma survivors with PTSD.

OBJECTIVE The authors prospectively explored whether a reduction in the volume of the hippocampus occurs in recent trauma survivors who develop posttraumatic stress disorder (PTSD). METHOD Thirty-seven survivors of traumatic events were assessed within a week of the traumatic event and 6 months later. The assessment included magnetic resonance imaging of the brain (including 124 coronal slices of 1.5-mm thickness), psychometric testing, and structured clinical interviews. The Clinician-Administered PTSD Scale conferred PTSD diagnoses at 6 months. RESULTS Ten subjects (27%) had PTSD at 6 months. The subjects with PTSD did not differ from those without PTSD in hippocampal volume (right or left) at 1 week or 6 months. There was no reduction in hippocampal volume in the PTSD subjects between 1 week and 6 months. CONCLUSIONS Smaller hippocampal volume is not a necessary risk factor for developing PTSD and does not occur within 6 months of expressing the disorder. This brain abnormality might occur in individuals with chronic or complicated PTSD.
Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study

Standalone literature reviews are fundamental in every scientific discipline. Their value is reflected by a profound scientific impact in terms of citations. Although previous empirical research has shown that this impact has a large variance, it is largely unknown which specific factors influence the impact of literature reviews. Against this background, the purpose of our study is to shed light on the driving factors that make a difference in the scientific impact of literature reviews. Our analysis of an exhaustive set of 214 IS literature reviews reveals that factors on the author level (e.g., expertise, collaboration, and conceptual feedback) and on the article level (e.g., methodological rigor) are significant and robust predictors of scientific impact over and above journal level factors. These insights enhance our understanding of what distinguishes highly cited literature reviews. In so doing, our study informs future guidelines on literature reviews and provides insights for prospective authors.
Statistical rare event analysis using smart sampling and parameter guidance

In this paper, we propose a new efficient statistical method for failure probability estimation of analog circuits with rate failure events, which is a time-consuming process using the existing Monte Carlo method. On top of this, the new method can also provide the estimation of parameter regions to achieve targeted performance to facilitate the design process, which is missing in the traditional fast statistical methods such as the statistical blockage based method. The new method employs two new techniques to speed up the analysis. First, to reduce the large number of samples for rare event analysis, the new approach employs a smart sample selection scheme, which can consider the effectiveness of samples and well-coverage for the parameter space. As a result, it can reduce an additional simulation costs by pruning less effective samples while keeping the accuracy of failure estimation. Second, the new approach identifies the failure regions in terms of parameters to provide a good design guideline for designers and optimization tools. This is enabled by applying the variance based feature selection to find the dominant parameters. A quasi-random sampling with dominant parameters is then applied to determine in-spec boundaries of those parameters. In addition, we also provide the complete formula for the probability determinations of failure regions in the iterative failure region searching framework. We demonstrate the advantage of our proposed method using two test benches: 6T-SRAM reading failure diagnosis with 27 process parameters, charge pump operation failure diagnosis in a PLL circuit with 81 process parameters. Experimental results show that the new method can be 4X more accurate than the recently proposed REscope method. Furthermore, the new method reduces the simulation cost by 2X than the recursive statistical blockage (RSB) method with same accuracy level. Our approach also provides the precise guidance of diverse parameters with 1.21% estimation error.
Spatial Mapping of Time-Variable Errors in Jason-1 and TOPEX/Poseidon Sea Surface Height Measurements

Abstract Fitting ocean models to altimeter sea surface height (SSH) measurements requires knowledge of instrument noise (radar noise, sea state bias, path delay corrections, and orbit errors) and “representation” errors related to SSH signals (e.g., tidal or pressure driven) not computed in the models. Comparisons between the independent Ocean Topography Experiment (TOPEX)/Poseidon and Jason-1 altimetric missions when they were in identical orbits show that point by point the data are consistent within the mission specifications of about 3-cm rms, but large-scale dependences exist in the data differences, and these are both poorly known and capable of introducing major errors into oceanic state estimates. Here the authors focus on the time-variable component of the spatially dependent errors. The analysis reveals errors ranging from 2 cm in the Tropics to 4 cm at mid- and high latitudes and roughly consistent with a dependence of instrument noise on significant wave height. Analysis of the representation .
On the sum-rate capacity of a 2-user Multiple Access Wiretap Channel with one secret user

We consider a 2-user Gaussian Multiple-Access Wiretap channel (MA-WC), with the unintended receiver having access to signal from only one of the two transmitters. We have characterized the sum-rate capacity of this channel within . 5 bits and proved that Gaussian signalling at the transmitters is approximate sum-rate capacity optimal. For the scenario when the additive noise plus the signal from first transmitter have a combined strength poorer than the additive noise of the evesdropper, the above sum-rate capacity characterization leads to an approximate capacity region characterization for the channel. It is shown that, under this circumstances, a timesharing scheme between the two users can achieve the capacity region of the channel within . 25 bits. It has been proved (Xie and Ulukus, ISIT 2013 [1]) recently that on a Multiple-Access Wiretap channel interference alignment (IA) is necessary to achieve optimal performance. Our channel model is an interesting variation of the MA-WC for which IA is not necessary and independent Gaussina signalling is sufficient to achieve the optimal sum-rate within . 5 bits.