Miracle-dz/UniFAR-FTUs huggingface dataset:
Robustness envelopes of networks

We study the robustness of networks under node removal, considering random node failure, as well as targeted node attacks based on network centrality measures. Whilst both of these have been studied in the literature, existing approaches tend to study random failure in terms of average-case behavior, giving no idea of how badly network performance can degrade purely by chance. Instead of considering average network performance under random failure, we compute approximate network performance probability density functions as functions of the fraction of nodes removed. We find that targeted attacks based on centrality measures give a good indication of the worst-case behavior of a network. We show that many centrality measures produce similar targeted attacks and that a combination of degree centrality and eigenvector centrality may be enough to evaluate worst-case behavior of networks. Finally, we study the robustness envelope and targeted attack responses of networks that are rewired to have high- and low-degree assortativities, discovering that moderate assortativity increases confer more robustness against targeted attacks whilst moderate decreases confer more robustness against random uniform attacks.
Pseudomonas aeruginosa Lifestyle: A Paradigm for Adaptation, Survival, and Persistence

Pseudomonas aeruginosa is an opportunistic pathogen affecting immunocompromised patients. It is known as the leading cause of morbidity and mortality in cystic fibrosis (CF) patients and as one of the leading causes of nosocomial infections. Due to a range of mechanisms for adaptation, survival and resistance to multiple classes of antibiotics, infections by P. aeruginosa strains can be life-threatening and it is emerging worldwide as public health threat. This review highlights the diversity of mechanisms by which P. aeruginosa promotes its survival and persistence in various environments and particularly at different stages of pathogenesis. We will review the importance and complexity of regulatory networks and genotypic-phenotypic variations known as adaptive radiation by which P. aeruginosa adjusts physiological processes for adaptation and survival in response to environmental cues and stresses. Accordingly, we will review the central regulatory role of quorum sensing and signaling systems by nucleotide-based second messengers resulting in different lifestyles of P. aeruginosa. Furthermore, various regulatory proteins will be discussed which form a plethora of controlling systems acting at transcriptional level for timely expression of genes enabling rapid responses to external stimuli and unfavorable conditions. Antibiotic resistance is a natural trait for P. aeruginosa and multiple mechanisms underlying different forms of antibiotic resistance will be discussed here. The importance of each mechanism in conferring resistance to various antipseudomonal antibiotics and their prevalence in clinical strains will be described. The underlying principles for acquiring resistance leading pan-drug resistant strains will be summarized. A future outlook emphasizes the need for collaborative international multidisciplinary efforts to translate current knowledge into strategies to prevent and treat P. aeruginosa infections while reducing the rate of antibiotic resistance and avoiding the spreading of resistant strains.
Identifying active transcription factors and kinases from expression data using pathway queries

MOTIVATION Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data. RESULTS We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.
Power capacity based bus voltage region partition and online droop coefficient tuning for real-time operation of DC microgrids

Multiple-voltage-region control, in which the bus voltage range is divided into several regions, is usually implemented for DC microgrid operation in distributed manner. Voltage/power droop relationships are imposed for active power sharing among slack terminals. Conventionally, threshold voltages for voltage region partition are determined with fixed percentage of variation around the nominal value, which may result in unevenness of droop coefficients in different regions. If system droop coefficient is too high, significant bus voltage step change due to load variation will occur. On the other hand, significant power sharing error among slack terminals will be induced if the droop coefficient is too low. In this paper, a compromised solution with power capacity based bus voltage region partition is proposed to equalize the droop coefficients in different regions. However, the droop coefficients are determined based on the rated power capacity of system units. Bus voltage discontinuity appears when the power capacity reduces in actual implementation. To eliminate the voltage discontinuity, online droop coefficient tuning according to the real-time power capacity is implemented. Algorithms for local power capacity estimation of solar PV and battery energy storage have been proposed. A lab-scale DC microgrid has been developed for verification of the proposed methods.
Molecular dissection of the pseudoknot governing the translational regulation of Escherichia coli ribosomal protein S15.

The ribosomal protein S15 controls its own translation by binding to a mRNA region overlapping the ribosome binding site. That region of the mRNA can fold in two mutually exclusive conformations that are in dynamic equilibrium: a structure with two hairpins and a pseudoknot. A mutational analysis provided evidence for the existence and requirement of the pseudoknot for translational control in vivo and S15 recognition in vitro. In this study, we used chemical probing to analyze the structural consequences of mutations and their effect on the stem-loop/pseudoknot equilibrium. Interactions between S15 and the pseudoknot structure were further investigated by footprinting experiments. These data, combined with computer modelling and the previously published data on S15 binding and in vivo control, provide important clues on pseudoknot formation and S15 recognition. An unexpected result is that the relevant control element, here the pseudoknot form, can exist in a variety of topologically equivalent structures recognizable and shapable by S15. S15 sits on the deep groove of the co-axial stack and makes contacts with both stems, shielding the bridging adenine. The only specific sequence determinants are found in the helix common to the pseudoknot and the hairpin structures. Images
Predicting the Virtual Temperature of Web-Blog Articles as a Measurement Tool for Online Popularity

A Blog provides commentary, news, or content on a particular subject. The important part of many blogs is interactive format. Sometimes, there is a heated debate on a topic, any article becomes a political or sociological issue. However, users not pay much attention to most articles. So, how we can predict the popularity of articles in advance and what is a standard for popularity? In this paper, we propose a methodology to predict the popularity of an article. First, we use an analogy between the virtual temperature and the popularity of the on-line articles. Thus, we define four different types of discrete temperature scale, such as explosive, hot, warm, and cold, according to the number of reviews in the saturated state of the article. We are concerned with how to predict the final temperature of the submitted articles in the internet Web-blog space. An experimental data set was collected from the articles submitted to "SEOPRISE", a well known political discussion blog in Korea that more than 50,000 users visit per day. The hit count is used as a factor to predict the popularity, analogous to the number of viewers in the popularity of movies. We calculated the saturation point using the variation of hit count over the lifetime. We derived a sound regression model to predict the popularity temperature of the subject article in terms of the hit counts at the saturation point via the correlation coefficient of hourly hit count and hit count of the saturation point. We can predict the popularity temperature of Internet discussion articles using the hit count of the saturation point with more than 70% accuracy, exploiting only the first 30 minutes' information. Because of low predictive value of explosive, the results of prediction were worse than we think. In the hot, warm, and cold categories, we can predict more than 86% accuracy from 30 minutes and more than 90% accuracy from 70 minutes.
Endothelial Cell Self-fusion during Vascular Pruning

During embryonic development, vascular networks remodel to meet the increasing demand of growing tissues for oxygen and nutrients. This is achieved by the pruning of redundant blood vessel segments, which then allows more efficient blood flow patterns. Because of the lack of an in vivo system suitable for high-resolution live imaging, the dynamics of the pruning process have not been described in detail. Here, we present the subintestinal vein (SIV) plexus of the zebrafish embryo as a novel model to study pruning at the cellular level. We show that blood vessel regression is a coordinated process of cell rearrangements involving lumen collapse and cell–cell contact resolution. Interestingly, the cellular rearrangements during pruning resemble endothelial cell behavior during vessel fusion in a reversed order. In pruning segments, endothelial cells first migrate toward opposing sides where they join the parental vascular branches, thus remodeling the multicellular segment into a unicellular connection. Often, the lumen is maintained throughout this process, and transient unicellular tubes form through cell self-fusion. In a second step, the unicellular connection is resolved unilaterally, and the pruning cell rejoins the opposing branch. Thus, we show for the first time that various cellular activities are coordinated to achieve blood vessel pruning and define two different morphogenetic pathways, which are selected by the flow environment.
Functional map of human cytomegalovirus AD169 defined by global mutational analysis

Human cytomegalovirus has a complex double-stranded DNA genome of ≈240,000 bp that contains ≈150 ORFs likely to encode proteins, most of whose functions are not well understood. We have used an infectious bacterial artificial chromosome to introduce 413 defined insertion and substitution mutations into the human cytomegalovirus AD169 genome by random and site-directed transposon mutagenesis. Mutations were produced in all unique ORFs with a high probability of encoding proteins for which mutants have not been previously documented and in many previously characterized ORFs. The growth of selected mutants was assayed in cultured human fibroblasts, and we now recognize 41 essential, 88 nonessential, and 27 augmenting ORFs. Most essential and augmenting genes are located in the central region, and nonessential genes generally cluster near the ends of the viral genome.
A method for detecting salient regions using integrated features

We develop a novel algorithm for detecting salient regions. By analyzing the advantages and disadvantages of the existing methods, five principles for designing salient region detection algorithms are summarized. Based on these principles, we propose a novel method that generates saliency map with highlighted salient regions by utilizing two different features, namely visual saliency value and spatial weight. The visual saliency value is determined based on local contrast differences and low-level feature frequencies. The spatial weight is computed by analyzing the size and location of salient regions. Experimental results show that the proposed algorithm outperforms 7 state-of-the-art methods on the public image set.
Distilled sensing: selective sampling for sparse signal recovery

A selective sampling procedure called distilled sensing (DS) is proposed, and shown to be an efiective method for recovering sparse signals in noise. Based on the notion that it is often easier to rule out locations that do not contain signal than it is to directly identify non-zero signal components, DS is a sequential method that systematically focuses sensing resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery| dramatically weaker sparse signals can be recovered using DS compared with conventional non-adaptive sensing procedures.
Compact mm-wave power combiners in 65nm CMOS-SOI

Compact, slow-wave coplanar waveguide (S-CPW) power combiners are characterized in the 60GHz band. A floating electric shield promotes slow-wave propagation, while implementation in 65nm CMOS-SOI technology allows utilization of the entire interconnect metal stack to lower losses. Measured slowing factors as high as 11 lead to a reduction in size by a factor up to 4.4 for a 60GHz combiner. Measured attenuation and insertion loss of the S-CPW combiners are &#60;0.3dB per λ/4 and &#60;0.6dB/150µm length at 60GHz, respectively, with quality factors ranging from 25 to 45. The effects of ground-signal gap and shield density on characteristic impedance, attenuation, Q-factor and effective permittivity are described.
Axiomatic Analysis and Optimization of Information Retrieval Models

The accuracy of a search engine is mostly determined by the optimality of the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general retrieval model would enable all search engines to be more useful, thus have immediate broad impact. Extensive research has been done on developing an optimal retrieval model since 1960s, leading to multiple effective retrieval models, including, e.g., Pivoted Normalization Vector Space model, BM25, Dirichlet Prior Query Likelihood, and PL2. However, these state of the art retrieval models were all developed at least a decade ago, suggesting that it has been difficult to further improve them. One reason why we could not easily improve these models is because we do not have a good understanding of their deficiencies and have mostly relied on empirical evaluation to assess the superiority of a retrieval model. Recently, an axiomatic way of analyzing and optimizing retrieval models has been developed and shown great promise in both understanding the deficiencies of retrieval models and developing more effective ones. The basic idea of this axiomatic framework is to specify a number of formal constraints that an optimal retrieval model is expected to satisfy, and use them to assess the optimality of a retrieval model. Such an axiomatic way of modeling relevance provides a theoretical way to study how to develop an ultimately optimal retrieval model, enables analytical comparison of different retrieval models without necessarily requiring empirical evaluation, and has led to the development of multiple more effective retrieval models. The purpose of this tutorial is to systematically explain this emerging axiomatic approach to developing optimal retrieval models, review and summarize the research progress achieved so far on this topic, and discuss promising future research directions in optimizing general retrieval models. Tutorial attendees can expect to learn, among others, (1) the basic methodology of axiomatic analysis and optimization of retrieval models, (2) how to formalize retrieval heuristics with mathematical constraints, (3) the major retrieval constraints proposed so far, (4) the new retrieval functions derived by using the axiomatic approaches, (5) specific research directions to further develop more effective retrieval models, and (6) general open challenges in developing an ultimately optimal retrieval model. The tutorial should appeal to those who work on information retrieval models and those who are interested in applying axiomatic analysis to optimize specific retrieval functions in real applications. The tutorial should also be interesting to researchers who work on ranking problems in general. Attendees will be assumed to know the basic concepts in information retrieval models.
Smoothing Hazard Functions and Time-Varying Effects in Discrete Duration and Competing Risks Models

Abstract State-space or dynamic approaches to discrete or grouped duration data with competing risks or multiple terminating events allow simultaneous modeling and smooth estimation of hazard functions and time-varying effects in a flexible way. Full Bayesian or posterior mean estimation, using numerical integration techniques or Monte Carlo methods, can become computationally rather demanding or even infeasible for higher dimensions and larger datasets. Therefore, based on previous work on filtering and smoothing for multicategorical time series and longitudinal data, our approach uses posterior mode estimation. Thus we have to maximize posterior densities or, equivalently, a penalized likelihood, which enforces smoothness of hazard functions and time-varying effects by a roughness penalty. Dropping the Bayesian smoothness prior and adopting a nonparametric viewpoint, one might also start directly from maximizing this penalized likelihood. We show how Fisher scoring smoothing iterations can be carried ou.
Automatic language identification using Gaussian mixture and hidden Markov models

Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five-language Rome Laboratory database, the 20-language CCITT database, and the ten-language OGI (Oregon Graduate Institute) telephone speech database. In general, the performance of a single state HMM (i.e., a static Gaussian mixture classifier) was comparable with that of the multistate HMMs, indicating that the sequential modeling capabilities of HMMs were not exploited. <<ETX>>
Design of stable adaptive recursive filters using a new parametrization of 2nd order balanced realizations

A new characterization of input-balanced realizations is derived for 2nd order filters, based on which a filter structure is proposed. Like the normalized lattice structure, the stability of the proposed structure can be ensured in a time-varying environment and has a much better behavior in terms of signal dynamic range than the direct-form II structure. Compared with the normalized lattice structure, it is more efficient. Based on this structure, an adaptive filter is derived. The results can be extended easily to any filter by cascading the denominator of the filter transfer function with a set of 2nd-order sub-filters that are implemented with the proposed structure. Extensive simulations are given to support the theoretical results and to compare the performance of the proposed structure with that of the classical direct-form II structure.
A REVIEW ON REAL TIME VIDEO STABILIZATION

In the latest technology, there are different applications which are working on videos. Particularly, video from camera’s which are placed on moving platform. But due to uneven surface or camera vibration, captured video tends to have undesired jitters, shakes and blurs. This results into unpleasant viewing experience and also affect on video processing application such as surveillance or some military application also. Hence video stabilization technique is essential in such cases. There are different video stabilization techniques are available but they need extra hardware. Hence, Digital video stabilization is an essential video improvement technology which is focusing on removing unnecessary camera vibrations from image sequences by using different digital image algorithms. Again digital stabilization algorithms are of two types, block based and feature based. Features are interesting points in the images and the starting point of many algorithms. Hence it is necessary to have good feature detector and descriptor. In the proposed paper, different feature detector and descriptor pairs are compared based on their speed and number of feature points. Experimental study shows that Features from Accelerated Segment Test (FAST) corner detection algorithm and Fast Retina Key point (FREAK) descriptor are comparatively faster than other pairs. Hence, it is much suitable for real time applications.
The molecular genetics of male infertility

Spermatogenesis is an elaborate process involving both cell division and differentiation, and cell‐cell interactions. Defects in any of these processes can result in infertility, and in some cases these can be genetic in cause. Mapping experiments have defined at least three regions of the human Y chromosome that are required for normal spermatogenesis. Two of these contain the genes encoding the RNA binding proteins RBM and DAZ, suggesting that the control of RNA metabolism is likely to be an important control point for human spermatogenesis. A similar analysis in mice has shown that at least two regions of the mouse Y chromosome are essential for spermatogenesis. Both genetic and reverse genetic approaches have been used to identify mouse autosomal genes required for spermatogenesis. These studies have shown that genes in a number of different pathways are essential for normal spermatogenesis, and also provide putative models of human infertility.