Miracle-dz/UniFAR-FTUs huggingface dataset:
Page Number Problem: An Evaluation of Heuristics and its Solution Using a Hybrid Evolutionary Algorithm

The page number problem is to determine the minimum number of pages in a book in which a graph G can be embedded with the vertices placed in a sequence along the spine and the edges on the pages of the book such that no two edges cross each other in any drawing. In this paper we have (a) statistically evaluated five heuristics for ordering vertices on the spine for minimum number of edge crossings with all the edges placed in a single page, (b) statistically evaluated four heuristics for distributing edges on a minimum number of pages with no crossings for a fixed ordering of vertices on the spine and (c) implemented and experimentally evaluated a hybrid evolutionary algorithm (HEA) for solving the pagenumber problem. In accordance with the results of (a) and (b) above, in HEA, placement of vertices on the spine is decided using a random depth first search of the graph and an edge embedding heuristic adapted from Chung et al. is used to distribute the edges on a minimal number of pages. The results of experiments with HEA on selected standard and random graphs show that the algorithm achieves the optimal pagenumber for the standard graphs. HEA performance is also compared with the Genetic Algorithm described by Kapoor et al. It is observed that HEA gives a better solution for most of the graph instances.
A Novel 3D Analytical Scattering Model for Air-to-Ground Fading Channels

A geometry-based three-dimensional (3D) novel stochastic channel model for air-to-ground (A2G) and ground-to-air (G2A) radio propagation environments is proposed. The vicinity of a ground station (GS) is modelled as surrounded by effective scattering points; whereas the elevated air station’s (AS) vicinity is modelled as a scattering-free region. Characterization of the Doppler spectrum, dispersion in the angular domain and second order fading statistics of the A2G/G2A radio communication channels is presented. Closed-form analytical expressions for joint and marginal probability density functions (PDFs) of Doppler shift, power and angle of arrival (AoA) are derived. Next, the paper presents a comprehensive analysis on the characteristics of angular spread on the basis of shape factors (SFs) for A2G/G2A radio propagation environments independently in both the azimuth and elevation planes. The analysis is further extended to second order statistics of the fading channel; where the behaviour of the level crossing rate (LCR), average fade duration (AFD), auto-covariance and coherence distance for the A2G/G2A radio propagation environment is studied. Finally, the impact of physical channel parameters, such as the mobility of AS, the height of AS, the height of GS and the delay of the longest propagation path, on the distribution characteristics of Doppler shift, angular spread and second order statistics is thoroughly studied.
Kinodynamic Planning in the Configuration Space via Velocity Interval Propagation

We propose a method that enables kinodynamic planning in the configuration space (of dimension n) instead of the state space (of dimension 2n), thereby potentially cutting down the complexity of usual kinodynamic planning algorithms by an exponential factor. At the heart of this method is a new technique – called Velocity Interval Propagation – which, given a path in the configuration space and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this technique with usual sampling-based methods gives rise to a family of new motion planners that can appropriately handle kinodynamic constraints while avoiding the complexity explosion and, to some extent, the conceptual difficulties associated with a move to the state space.
Discovery of Natural Products as Novel and Potent FXR Antagonists by Virtual Screening

Farnesoid X receptor (FXR) is a member of nuclear receptor family involved in multiple physiological processes through regulating specific target genes. The critical role of FXR as a transcriptional regulator makes it a promising target for diverse diseases, especially those related to metabolic disorders such as diabetes and cholestasis. However, the underlying activation mechanism of FXR is still a blur owing to the absence of proper FXR modulators. To identify potential FXR modulators, an in-house natural product database (NPD) containing over 4,000 compounds was screened by structure-based virtual screening strategy and subsequent hit-based similarity searching method. After the yeast two-hybrid (Y2H) assay, six natural products were identified as FXR antagonists which blocked the CDCA-induced SRC-1 association. The IC50 values of compounds 2a, a diterpene bearing polycyclic skeleton, and 3a, named daphneone with chain scaffold, are as low as 1.29 and 1.79 μM, respectively. Compared to the control compound guggulsterone (IC50 = 6.47 μM), compounds 2a and 3a displayed 5- and 3-fold higher antagonistic activities against FXR, respectively. Remarkably, the two representative compounds shared low topological similarities with other reported FXR antagonists. According to the putative binding poses, the molecular basis of these antagonists against FXR was also elucidated in this report.
Context-aware User Interaction for Mobile Recommender Systems

We are witnessing more and more mobile recommender systems applied in e-tourism, utilizing contextual information in order to boost productivity and user experience by generating more tailored suggestions to them. In the meantime, the design of these applications is getting more and more user-centered. Context of use is playing an important role in the appropriateness of a user interface (UI). This motivates our study to devise a novel approach for augmenting user interaction experience on smartphones by exploiting the current context of the end user. For this purpose, we have designed and implemented a context-aware UI based on an existing mobile application for recommending tourist places. We have conducted a user study and measured the effectiveness of our method in terms of three attributes: task completion time, the perceived user ease of use and the perceived user satisfaction. The within-subject evaluation conducted with 25 participants confirms that the proposed contextaware UI can enrich user interaction experiences.
Low-dimensional attractor for neural activity from local field potentials in optogenetic mice

We used optogenetic mice to investigate possible nonlinear responses of the medial prefrontal cortex (mPFC) local network to light stimuli delivered by a 473 nm laser through a fiber optics. Every 2 s, a brief 10 ms light pulse was applied and the local field potentials (LFPs) were recorded with a 10 kHz sampling rate. The experiment was repeated 100 times and we only retained and analyzed data from six animals that showed stable and repeatable response to optical stimulations. The presence of nonlinearity in our data was checked using the null hypothesis that the data were linearly correlated in the temporal domain, but were random otherwise. For each trail, 100 surrogate data sets were generated and both time reversal asymmetry and false nearest neighbor (FNN) were used as discriminating statistics for the null hypothesis. We found that nonlinearity is present in all LFP data. The first 0.5 s of each 2 s LFP recording were dominated by the transient response of the networks. For each trial, we used the last 1.5 s of steady activity to measure the phase resetting induced by the brief 10 ms light stimulus. After correcting the LFPs for the effect of phase resetting, additional preprocessing was carried out using dendrograms to identify “similar” groups among LFP trials. We found that the steady dynamics of mPFC in response to light stimuli could be reconstructed in a three-dimensional phase space with topologically similar “8”-shaped attractors across different animals. Our results also open the possibility of designing a low-dimensional model for optical stimulation of the mPFC local network.
Hypofrontality and negative symptoms in major depressive disorder.

UNLABELLED The purpose of the current study was to compare regional cerebral blood flow (rCBF) in patients with major depressive disorder (MDD) to that of healthy subjects and to examine the relationship between rCBF, depressive symptoms (DS) and negative symptoms (NS) in these patients. METHODS Eleven psychiatric inpatients with diagnosed (MDD) and 15 normal control subjects were administered the scale for the assessment of negative symptoms (SANS) and the modified Hamilton rating scale for depression with items descriptive of NS excluded (HRSD-DS). Each patient underwent a SPECT scan using 99mTc-HMPAO at rest. Cortical and subcortical regions of interest (ROIs) were symmetrically defined in each hemisphere. Cortical-to-cerebellar perfusion ratios were established quantitatively using ADAC software. RESULTS Subjects in the MDD group had significantly lower rCBF in the frontal cortex and cinglulate gyrus (MANOVA, p = 0.038) due to differences in dorsolateral prefrontal cortex bilaterally (right F = 7.69, p = 0.01; left F = 8.41, p = 0.01) in the right orbitofrontal cortex (F = 6.79, p = 0.02) and in the cingulate gyrus (F = 5.34, p = 0.03). The MDD group also had lower rCBF in the posterior cortical structures (MANOVA, p = 0.072), which was due to decreased perfusion in the right parietal cortex (F = 7.54, p = 0.01). There were negative correlations between the SANS total score and rCBF in both the left dorsolateral prefrontal cortex (Pearson's correlation coefficient r = .-67, p < 0.05) and the left anterior temporal cortex (r = -0.71, p < 0.01) in MDD patients. Additionally, there were positive correlations between HRSD scores and rCBF in the left anterior temporal (r = 0.71, p < 0.01), left dorsolateral prefrontal (r = 0.70, p < 0.01), right frontal (r = 0.82, p < 0.01) and right posterior temporal (r = 0.74, p < 0.01) cortices. Cerebral blood flow was not correlated with either mini-mental state examination scores or age. CONCLUSION This preliminary study replicates the finding of hypofrontality in MDD and indicates that decreased perfusion is associated specifically with negative symptom severity. These results support the hypothesis that, in MDD, negative symptoms and symptoms of depression are distinct phenomena and underscore the importance of negative symptom evaluation in neuroimaging studies of MDD and other disorders.
The effect of normalization methods on the identification of differentially expressed genes in microarray data

In this thesis the effect of normalization methods on the identification of differentially expressed genes is investigated. A zebrafish microarray dataset called Swirl was used in this thesis work. First the Swirl dataset was extracted and visualized to view if the robust spline and print tip loess normalization methods are appropriate to normalize this dataset. The dataset was then normalized with the two normalization methods and the differentially expressed genes were identified with the LimmaGUI program. The results were then evaluated by investigating which genes overlap after applying different normalization methods and which ones are identified uniquely after applying the different methods. The results showed that after the normalization methods were applied the differentially expressed genes that were identified by the LimmaGUI program did differ to some extent but the difference was not considered to be major. Thus the main conclusion is that the choice of normalization method does not have a major effect on the resulting list of differentially expressed genes.
Bilinear models for item recommendation based on tags

Recently, collaborative tagging has been gaining increasing popularity in a large variety of websites. The tags generated from collaborative tagging provide highly abstracted information about users' personal taste on information item, and therefore could be used to profile both users and items. However, flattening the three dimensional user-tag-item matrix into two-way matrices will lead to loss of two dimensional relationships between users, items and tags completely. In this paper, we use a predictive bilinear model to capture the informative interaction patterns among user-tag and item-tag matrices, and employ the Nonnegative Matrix Factorization (NMF) algorithm to extract lower dimensional representative features from tags. Experiments on two real-world datasets show that our approach substantially outperforms the traditional CF methods as well as tag-based recommendation methods reported in the literature.
Post-GAVI sustainability of the Haemophilus influenzae type b vaccine program: The potential role of economic evaluation

ABSTRACT Haemophilus influenzae type b (Hib) can cause severe invasive diseases which are, however, preventable by vaccination. To increase access to Hib vaccine, GAVI – the Vaccine Alliance – has provided financial support for 73 lower income countries worldwide. At the same time, GAVI has been implementing its co-financing policy, requiring recipient countries to pay a portion of vaccine costs and to increase this amount over time. Starting in 2016, 5 countries will stop receiving GAVI funding and procure the vaccine themselves. Although the graduating countries have access to the UNICEF/GAVI tendered vaccine price for 5 more years, the uncertainty in market vaccine price may hamper the post-GAVI program sustainability. A possible increase in vaccine price would cause a significant burden on governmental budgets, discouraging countries to continue the program. As a special tool, economic evaluation (EE) can assist decision makers by identifying the maximum affordable vaccine price for countries to pay. Given that only 6 GAVI-eligible countries have such analyses published, more EEs are necessary to strengthen countries' commitment during this transition period. The information will also be useful for manufacturers to determine their pricing policy.
Uncertainties in Climate Model Outputs

A great challenge for scientists is to estimate future climate changes. Very useful instruments for these estimates are climate models. This article focuses on thee- dimensional climate models. The main sources of the uncertainties in the climate model outputs are reviewed, as well as some of the methods used for their evaluation, with focus on multi-model ensembles. The Reliability Averaging (REA) Method introduced in Giorgi and Mearns (2002) is described in detail. Attention is paid to problems connected to the criteria used in the REA method for evaluation of the climate models.
An entity-relationship approach to the implementation of frame-based systems

Use of Entity-Relationship (ER) approach for the implementation of knowledge base systems is considered with a specific emphasis on frame-based systems. In the approach followed in this paper, domain descriptions are first analyzed using an extended ER-model to obtain ER-diagrams which are then implemented using appropriate frame data structures. The extended ER-model developed for this purpose is presented. Implementation details are also discussed.
L-BAND CONTRIBUTION TO C-BAND INSAR STUDIES OF AFRICAN VOLCANIC AREAS

1 Dept. of Geology, Royal Museum for Central Africa, 3080 Tervuren, Belgium, christelle.wauthier@africamuseum.be 2 Dept. ArGEnCo,University of Liège, Sart Tilman B52, 4000 Liège, Belgium 3 Delft Inst. of Earth Obs. and Space Systems, Delft Univ. of Tech., 2629HS Delft, The Netherlands 4 Dept. of Geophysics/Astrophysics, National Museum of Natural History, Luxembourg 5 Centre d’Informations Géographiques SODERU, Goma, North-Kivu, DRC 6 Instituto de Astronomia y Geodesia (CSIC-UCM), Facultad de Ciencias Mathematicas, University of Madrid, Spain 7 Lab. Magmas et Volcans, Université Blaise Pascal – UMR 6524, 63038 Clermont-Ferrand, France
Probability, confirmation, and the conjunction fallacy

The conjunction fallacy has been a key topic in debates on the rationality of human reasoning and its limitations. Despite extensive inquiry, however, the attempt to provide a satisfactory account of the phenomenon has proved challenging. Here we elaborate the suggestion (first discussed by Sides, Osherson, Bonini, & Viale, 2002) that in standard conjunction problems the fallacious probability judgements observed experimentally are typically guided by sound assessments of confirmation relations, meant in terms of contemporary Bayesian confirmation theory. Our main formal result is a confirmation-theoretic account of the conjunction fallacy, which is proven robust (i.e., not depending on various alternative ways of measuring degrees of confirmation). The proposed analysis is shown distinct from contentions that the conjunction effect is in fact not a fallacy, and is compared with major competing explanations of the phenomenon, including earlier references to a confirmation-theoretic account.
The role of user interaction and acceptance in a cloud-based demand response model

Demand response management is one of the key applications of future energy systems. Most of the corresponding models and algorithms require a certain amount of user interaction to define appliances and rules for these processes. In this paper we implemented a cloud-based demand response approach to evaluate its efficiency depending on users input data. For simulation we used results of an online survey, mainly focusing on selection of devices and temporal flexibility concerning possible switch-off times. Contrary to our expectations the costs per load reduction (incentive prices to be paid by the utility) does not decrease when users have more than the day/night option to define switch-off timeslots. A high effect on cost cutting can be identified in the amount of participating users which underlines the relevance of user acceptance concerning demand response solutions.
A Parallel Genetic Algorithm for Clustering

Parallelization of genetic algorithms (GAs) has received c onsiderable attention in recent years. The reason for this is the availability of su itable computational resources and the need for solving harder problems in reasonab le time. We describe a new parallel self-adaptive GA for solving the data cluster ing problem. The algorithm utilizes island parallelization implemented using g enebank model, in which GA processes communicate with each other only through the ge n bank process. This model allows one to easily implement different migrati on topologies. Experiments show that significant speedup can be reached by par allelization. The effect of migration parameters is also studied and the devel opment of diversity is examined by several measures, some of which are new.
Learning to Sail

This article describes a fully functional, already deployed intelligent adaptive system, called the Odys Intelligent Pilot, built by RoboSail systems [11]. It is an intelligent sailboat autopilot and diagnostics system, built upon a hybrid architecture with use of machine learning methods. These methods are used in a knowledge discovery process that assists the incremental build-up of a rule-base that describes higher-level knowledge about the activity of sailing.
Statistical-based filtering system against DDOS attacks in cloud computing

A Distributed Denial of Service (DDOS) attack can make huge damages to resources and access of the resources to genuine users. Offered defending system cannot be easily applied in cloud computing due to their relatively low competence and wide storage. In this work we presented statistical technique to detect and filter DDOS attacks. The proposed model requires small storage and ability of fast detection. The obtained results show that our model has the ability to mitigate most of TCP attacks. Detection accuracy and Time consumption were the metrics used to evaluate the performance of our proposed model. From the simulation results, it is visible our algorithms achieve high detection accuracy (97%) with fewer false alarms.
Color Based Hand and Finger Detection Technology for User Interaction

The aim of this paper is to present the methodology for hand detection and propose the finger detection method. The detected hand and finger can be used to implement the non-contact mouse. This technology can be used to control the home devices such as curtain and television. Skin color is used to segment the hand region from background and counter is extracted from the segmented hand. Analysis of counter gives us the location of finger tip in the hand. We have performed extensive experiment and achieve very encouraging result.
miR-203 Suppresses the Proliferation and Migration and Promotes the Apoptosis of Lung Cancer Cells by Targeting SRC

SRC, also known as proto-oncogene c-Src, is a non-receptor tyrosine kinase that plays an important role in cancer progression by promoting survival, angiogenesis, proliferation, and invasion pathways. In this study, we found that SRC protein levels were consistently upregulated in lung cancer tissues, but that SRC mRNA levels varied randomly, suggesting that a post-transcriptional mechanism was involved in SRC regulation. Because microRNAs (miRNAs) are powerful post-transcriptional regulators of gene expression, we used bioinformatic analyses to search for miRNAs that potentially target SRC. We identified specific targeting sites for miR-203 in the 3′-untranslated region (3′-UTR) of SRC. We then experimentally validated miR-203 as a direct regulator of SRC using cell transfection and luciferase assays and showed that miR-203 inhibited SRC expression and consequently triggered suppression of the SRC/Ras/ERK pathway. Finally, we demonstrated that the repression of SRC by miR-203 suppressed the proliferation and migration and promoted the apoptosis of lung cancer cells. In summary, this study provides the first clues regarding the role of miR-203 as a tumor suppressor in lung cancer cells through the inhibition of SRC translation.