Miracle-dz/UniFAR-FTUs huggingface dataset:
A coherent feedforward loop design principle to sustain robustness of biological networks

MOTIVATION Many studies have investigated the relationship between structural properties and dynamic behaviors in biological networks. In particular, feedback loop (FBL) and feedforward loop (FFL) structures have received a great deal of attention. One interesting and common property of FBL and FFL structures is their coherency of coupling. However, the role of coherent FFLs in relation to network robustness is not fully known, whereas that of coherent FBLs has been well established. RESULTS To establish that coherent FFLs are abundant in biological networks, we examined gene regulatory and signaling networks and found that FFLs are ubiquitous, and are in a coherently coupled form. This result was also observed in the species-based signaling networks that are integrated from KEGG database. By using a random Boolean network model, we demonstrated that these coherent FFLs can improve network robustness against update-rule perturbations. In particular, we found that coherent FFLs increase robustness because these structures induce downstream nodes to be robust against update-rule perturbations. Therefore, coherent FFLs can be considered as a design principle of human signaling networks that improve network robustness against update-rule perturbations.
Fakulta elektrotechnickÆ

Prezentace virtuálních měst ve webovém prostředí Web-based Visualization of Virtual Cities 2 Summary This lecture is an overview of problems, approaches, and solutions dealing with modeling and visualization of large-scale models of real cities in a web environment. While acquisition issues are outlined only briefly, the study focuses on the optimal organization of city models and efficient rendering techniques. We also address data optimization for real-time delivery. A special attention is paid to user interfaces for manipulation with and examination of three-dimensional virtual objects and scenes. The methods under discussion are illustrated by examples taken from our long-term project called Virtual Old Prague. Although the presented principles are general, we show their implementations that take advantage of international standards in the field of computer graphics and virtual reality. The presented topic represents a complex problem that requires an integration of several branches of knowledge. From the pedagogical point of view, the topic contains a number of issues that are both interesting and important subjects for education in the field of computer science and informatics.
A SCORM-compliant Ubi-learning grid by employing CC/PP

In this study, a SCORM-compliant ubiquitous learning grid was constructed by using the grid services technologies combined with CC/PP. The purpose was to let anyone to access any information at anyplace, anytime, with any device to learn. Several SCORM-compliant learning management systems collaborated by Globus Toolkit V3.2 grid engine and CC/PP were implemented to provide a content adaptive environment. In the experiment, English learning objects were produced with access to learn and made accessible using PC, Laptop, Tablet PC, PDA, and mobile phones. Results of this study demonstrate the feasibility of the proposed framework.
Coming in at an Unusual Angle: Exo-Surprise and the Fieldworking Cosmonaut

Valentin Lebedev is a pioneer of space and earth science in Russia. He is also the first ethnographic diarist of outer space. In 1982, while “on orbit” for 211 days as a fieldworking cosmonaut, Lebedev produced a thickly descriptive account of the intimate sociality and technoculture of the Soviet space complex Salyut 7. Crafted to defamiliarize (ostranenie) a spaceworld that publics saw as flawlessly engineered and managed, the diary is an argument for the value of exospheric (exo-) surprise in human experience. But on another level, we learn that the surprise is on humans who would claim to conquer “space-as-itself”—a “zero gravity” environment of force fields both extremely inhospitable to life as we know it and also generative of life in all its expressions.
Spectrum estimation and adaptive denoising of eElectrocardiographic signals using Kalman filters

A method of time-varying parametric spectrum estimation from ECG sequences is presented. Model parameters are estimated recursively using a Kalman algorithm, which extracts the time-varying parameters and state variables of an ECG sequence, as well. We consider the noisy time- sequence generated by nonlinear auto regression, when the observations of the series contain measurement noise in addition to the signal. The spectrum estimates for each time instant then are obtained from the estimated model parameters. Proposed Kalman filter model turns to be adequate for either noise reduction or parameter estimation of processed sequence. Results thus obtained show better performance of Kalman-based filtration algorithm, in the sense of SNR and WDD distortion measurements, in comparison to conventional stationary spectrum estimation.
Reducing indirect function call overhead in C++ programs

Modern computer architectures increasingly depend on mechanisms that estimate future control flow decisions to increase performance. Mechanisms such as speculative execution and prefetching are becoming standard architectural mechanisms that rely on control flow prediction to prefetch and speculatively execute future instructions. At the same time, computer programmers are increasingly turning to object-oriented languages to increase their productivity. These languages commonly use run time dispatching to implement object polymorphism. Dispatching is usually implemented using an indirect function call, which presents challenges to existing control flow prediction techniques. We have measured the occurrence of indirect function calls in a collection of C++ programs. We show that, although it is more important to predict branches accurately, indirect call prediction is also an important factor in some programs and will grow in importance with the growth of object-oriented programming. We examine the improvement offered by compile-time optimizations and static and dynamic prediction techniques, and demonstrate how compilers can use existing branch prediction mechanisms to improve performance in C++ programs. Using these methods with the programs we examined, the number of instructions between mispredicted breaks in control can be doubled on existing computers.
CT Perfusion of the Neck: Internal Carotid Artery versus External Carotid Artery as the Reference Artery

SUMMARY: Arterial selection for reference time-enhancement curve generation in deconvolution-based perfusion CT (PCT) studies of the head and neck is underevaluated. This study of 11 patients with confirmed head and neck squamous cell carcinoma demonstrates significant correlation (range, r = 0.85–0.95) between perfusion parameter values derived with internal carotid artery (ICA) as compared to an external carotid artery reference, supporting the use of the ICA as arterial reference in PCT studies of the neck.
Curricular decision-making among public sex educators

The content of sex education in government-funded schools in the USA varies dramatically and reflects its contested nature and goals. Drawing from 50 interviews with sex educators working in the public, government-funded school system in a Midwestern US state, this study explores the processes through which sex educators decide what and how to teach. Working from the theoretical framework of argument sphere theory, and, specifically, socio-scientific controversy, we find that sex educators often resolve the competing arguments they face through processes that we call deliberative conflation and deliberative co-optation. Deliberative conflation involves the use of criteria from one or more argumentative spheres to judge evidence appropriate to a different sphere. Deliberative co-optation involves the use of discourse practices from one sphere to make arguments actually grounded in another. Both of these processes enable the reconciliation of otherwise incommensurate arguments, but they do so in ways that foster unstable and ambiguous curricular decisions. Our findings provide guidance towards improving sex education and its attendant outcomes.
Regularizing linear discriminant analysis for speech recognition

Feature extraction is an essential first step in speech recognition applications. In addition to static features extracted from each frame of speech data, it is beneficial to use dynamic features (called Δ and ΔΔ coefficients) that use information from neighboring frames. Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+Δ+ΔΔ features in most tasks. However, since LDA is obtained using statistical averages trained on limited data, it is reasonable to regularize LDA transform computation by using prior information and experience. In this paper, we regularize LDA and heteroschedastic LDA transforms using two methods: (1) Using statistical priors for the transform in a MAP formulation (2) Using structural constraints on the transform. As prior, we use a transform that computes static+Δ+ΔΔ coefficients. Our structural constraint is in the form of a block structured LDA transform where each block acts on the same cepstral parameters across frames. The second approach suggests using new coefficients for static, first difference and second difference operators as compared to the standard ones to improve performance. We test the new algorithms on two different tasks, namely TIMIT phone recognition and AURORA2 digit sequence recognition in noise. We obtain consistent improvement in our experiments as compared to MFCC features. In addition, we obtain encouraging results in some AURORA2 tests as compared to LDA+MLLT features.
A Comparative Evaluation of Deep and Shallow Approaches to the Automatic Detection of Common Grammatical Errors

This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram frequency statistics: we present two versions, one which uses frequency thresholds and one which uses a decision tree trained on the frequencies of the rarest n-grams in the input sentence. We find that the use of a decision tree improves on the basic approach only for the deep parser-based approach. We also show that combining both the shallow and deep decision tree features is effective. Our evaluation is carried out using a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting grammatical errors into well-formed BNC sentences.
Demographics Affecting Parental Expectations from Early Deaf Intervention

Abstract Parent participation in rehabilitation programs for children with special needs requires program designers to accommodate each family's particular needs and characteristics. This study investigated maternal reports on various characteristics of 50 Israeli deaf preschoolers, their parents, and families, and examined these characteristics' links with mothers' expectations from early intervention. Characteristics linked with maternal expectations from intervention included: mother's pessimism, need for independence, ability to control her life, and receipt of practical assistance and formal and informal support; family's degree of communication and interaction; children's communication mode and age at onset of treatment; and additional deaf siblings in the family. A four-cluster model depicts the relations between mothers', families', and deaf children's attributes and the mothers' expectations, entailing recommendations for intervention planning. Copyright © 2009 John Wiley & Sons, Ltd.
Familial Constraints on the Initiation of Cigarette Smoking Among Adolescents: An Elaboration of Social Bonding Theory and Differential Association Theory

Researchers have identified specific familial risk factors associated with the onset of smoking among adolescents. Two theoretical perspectives have been employed to integrate the research. Social bonding theory suggests that weak family relations have a direct influence on adolescent smoking behavior, whereas the differential association perspective proposes that the quality of family relations has an indirect impact in the initiation of smoking through facilitating association with smoking peers. Parental smoking behavior and the nature of the family structure are acknowledged as important factors that may interact with or directly influence family relations. This article explores how familial risk factors play a direct and an indirect role in influencing the initiation of smoking. Implications for school-based smoking preventive programs and recommendations for future research are presented and discussed.
Anticoagulation Reversal and Treatment Strategies in Major Bleeding: Update 2016

Anticoagulation is the mainstay of medical treatment, prevention and reduction of recurrent venous thromboembolism, stroke prevention in patients with non-valvular atrial fibrillation, and it reduces the incidence of recurrent ischemic events and death in patients with acute coronary syndrome. Options for anticoagulation have been steadily increasing. Physicians need to be aware of the clinical profile of anticoagulation agents, reversal agents and treatment strategies in the face of major bleeding.
Rice leaf detection with genetic programming

This paper describes an approach to the detection rice plants in images of rice fields by using genetic programming. The method involves the evolution of a genetic programming classifier of 20 × 20 pixel windows to distinguish rice and nonrice windows, applies the evolved classifier to each pixel position in a test image in a scanning window fashion and determines the class of a pixel by majority voting. The individual pixel values in the window comprise the terminal set. The four arithmetic operators, augmented by square root, comprise the function set. Fitness is a weighted sum of true positive and true negative rates. The classifier achieves an accuracy of 90% on positive and negative windows and is highly accurate in localizing rice leaves in test images for micro-spraying of nutritional supplements. The evolutionary approach clearly outperforms a thresholding approach based on colour which is unable to distinguish between rice an leaves.
Minimally invasive percutaneous nephrolithotomy for upper urinary tract calculi in transplanted kidneys

Authors from China describe their experience with percutaneous nephrolithotomy for upper urinary tract calculi in transplanted kidneys. They confirm that it is a suitable way to treat such stones, and suggest it as initial therapy for stones other than those in the middle or lower calyx, particularly if they are small stones.
[Characteristics of persister cells and the diversity of type II toxin - antitoxin system in Acinetobacter baumannii].

OBJECTIVE To study the biological features of the Acinetobacter baumannii persisters and the correlation between Type II toxin-antitoxin systems (TAs) and the persisters. METHODS Different antibiotics were used to isolate the persisters; BLAST programs were adopted to analyze candidate TAs; PCR was used to detect the distribution of TAs. RESULTS The number of persisters was different when treated by different antibiotics. For most antibiotics, the higher the concentration used, the lower the persister number was obtained. Persister numbers in exponential phase populations were lower than that in stationary phase. Polymyxin B and tobramycin could kill persisters. CONCLUSION The persistence level of A. baumannii is related to the growth state of the strains, the class and concentration of antibiotics.
Reliable data aggregation protocol for wireless sensor networks

Current routing protocols in WSNs or even in wireless Ad hoc networks are very susceptible to many attack i.e. stealthy attack. The most simple among those is where the adversary injects malicious routing information into the network. This results in routing inconsistencies leading to high increase in end-to-end delays or even packet losses in the network. In our paper, first, we abstract two fundamental routing protocols, which can be generally grouped in two broad categories based on the intrinsic nature of WSN. We argue that none of previous proposed routing protocols satisfies all of them at the same time. The novelty of our protocol is building a general routing protocol based on two methods, which takes into consideration two factors, first additional of sensor nodes to the aggregation process and second by considering complex report interaction between base station and aggregator. Finally, to evaluate the efficiency and effectiveness of the proposed protocol, we carry out comparison experiment of our proposed protocol to general known protocols. Performance cost evaluation of our proposed protocol shows essential advantage over existing protocol.
The Use of Benevolent Leadership Development to Advance Principles of Responsible Management Education

This paper proposes benevolent leadership development as a framework to incorporate principles of responsible management education to mainstream management curriculum, specifically within the context of leadership courses. This paper makes three contributions. The first is the development of a practical framework for benevolent leadership as a means to impart the principles of responsible management. The second contribution is the review and integration of four anchors of benevolent leadership: ethical sensitivity, spiritual depth, positive engagement, and community responsiveness. The third contribution is sharing knowledge about pedagogical strategies including implementation of the benevolent leadership model in leadership development courses through use of individual and team projects and exercises, which attempts to respond the challenges about teaching leadership (Hay and Hodgkinson, 2006). The illustrative processes and projects in this paper come from leadership development courses offered in Turkey and in Canada.
White ethnic attitudes after Soweto: A field experiment

Abstract Measurements of attitude towards various South African ethnic groups were obtained for Afrikaans and English-speaking Whites, both before and after the early phase of the Soweto riots in 1976. Although the samples are not very large, a fairly clear pattern of results emerges. The riots had a negative effect on the attitudes of Afrikaans-speaking subjects towards Blocks, white English-speaking subjects showed positive attitude changes toward both Blocks and Coloureds.
Identity recognition of plantar pressure image based on compressed sensing

Herein, a new identity recognition method of plantar pressure image (PPI) was investigated based on compressed sensing. During the process of identity recognition, the PPIs were collected with platform system in normal walking speed. The sparse representation of PPI was then obtained according to the sparse basis (i.e., wavelet basis). Finally, measurement vectors were calculated by the Topelitz measurement matrix and the PPI was recognized by compressed sensing classifier. The results showed that the accuracy of identity recognition of PPI based on compressed sensing exceeded 97.76%, demonstrating the effectiveness and stability of the Topelitz-compressed sensing algorithm. Meanwhile, the method used in this study reduced the data storage amount and increased the real-time recognition during the PPI process.
Dependency Parser-based Negation Detection in Clinical Narratives

Negation of clinical named entities is common in clinical documents and is a crucial factor to accurately compile patients’ clinical conditions and to further support complex phenotype detection. In 2009, Mayo Clinic released the clinical Text Analysis and Knowledge Extraction System (cTAKES), which includes a negation annotator that identifies negation status of a named entity by searching for negation words within a fixed word distance. However, this negation strategy is not sophisticated enough to correctly identify complicated patterns of negation. This paper aims to investigate whether the dependency structure from the cTAKES dependency parser can improve the negation detection performance. Manually compiled negation rules, derived from dependency paths were tested. Dependency negation rules do not limit the negation scope to word distance; instead, they are based on syntactic context. We found that using a dependency-based negation proved a superior alternative to the current cTAKES negation annotator.
Towards Building a High-Quality Workforce with Mechanical

Online crowdsourcing services provide an inexpensive and scalable platform for large-scale information verification tasks. We present our experiences using Amazon’s Mechanical Turk (AMT) sto verify over 100,000 local business listings for an online directory. We compare the performance of AMT workers to that of experts across five different types of tasks and find that most workers do not contribute high-quality work. We present the results of preliminary experiments that work towards filtering low-quality workers and increasing overall workforce accuracy. Finally, we directly compare workers’ accuracy on business categorization tasks against a Naı̈ve Bayes classifier trained from user-contributed business reviews and find that the classifier outperforms our workforce. Our report aims to inform the community of empirical results and cost constraints that are critical to understanding the problem of quality control in crowdsourcing systems.
Automated Characterization of Breast Lesions Imaged With an Ultrafast DCE-MR Protocol

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast has become an invaluable tool in the clinical work-up of patients suspected of having breast carcinoma. The purpose of this study is to introduce novel features extracted from the kinetics of contrast agent uptake imaged by a short (100 s) view-sharing MRI protocol, and to investigate how these features measure up to commonly used features for regular DCE-MRI of the breast. Performance is measured with a computer aided diagnosis (CADx) system aimed at distinguishing benign from malignant lesions. A bi-temporal breast MRI protocol was used. This protocol produces five regular, high spatial-resolution T1-weighted acquisitions interleaved with a series of 20 ultrafast view-sharing acquisitions during contrast agent uptake. We measure and compare the performances of morphological and kinetic features derived from both the regular DCE-MRI sequence and the ultrafast view-sharing sequence with four different classifiers. The classification performance of kinetics derived from the short (100 s) ultrafast acquisition starting with contrast agent administration, is significantly higher than the performance of kinetics derived from a much lengthier (510 s), commonly used 3-D gradient echo acquisition. When combined with morphology information all classifiers show a higher performance for the ultrafast acquisition (two out of four results are significantly better).