Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kalman Filter Approach for the Estimation of Time Dependent OD Matrices Exploiting Bluetooth Traffic Data Collection

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscopic and mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to the time-dependent OD estimation have been based either on ad hoc heuristics using mathematical programming approaches, or on Kalman-Filtering. The advent of the new Information and Communication Technologies (ICT), makes available new types of traffic data of higher quality and accuracy allowing for new modeling hypothesis leading to more computationally efficient algorithms. Ad hoc procedures based on Kalman Filtering, explicitly exploiting traffic data available from Bluetooth sensors, have been designed and implemented successfully and the numerical results of the computational experiments are discussed for freeway and network test sites.
Not all high-alexithymia individuals are risk-takers: private self-consciousness moderates the relationship between alexithymia and risk-taking behaviours

This article concerns the influence which alexithymia exerts on risk-taking. In particular, alexithymia is seen as a factor which encourages risk-taking as it allows high-alexithymia individuals to feel emotions which are sufficiently intense to compensate for their deficit of emotional awareness. In this connection, we make the hypothesis that alexithymia’s influence is moderated by private self-consciousness (SC). This is because private SC increases the likelihood that high-alexithymia individuals become aware of their risk-taking tendency and that this tendency is discrepant with their pro-safety standards (‘putting someone in danger is bad’) or self-schemas (‘I am a responsible person’). Thus, private SC is likely to enable and motivate them to consciously regulate their behaviour in a safer direction. This hypothesis was empirically tested by a questionnaire amongst 372 French drivers, whose SC, alexithymia, as well as their current adoption of eight risky behaviours (with a more detailed analysis of speeding). The use of conditional process analyses reveal no main effects of alexithymia and private SC but strong interaction effects, while controlling for other predictors. More precisely, when private SC is low high-alexithymia individuals break more the speed limits and adopt more frequently risky behaviours than low-alexithymia drivers. Conversely, when private SC is high, they are less risk-prone. As practical implication, we recommend the use of techniques increasing private SC amongst high-alexithymia persons.
PTSD and olfaction: the aetiology of PTSD and the bidirectional relationship between trauma and sensitisation.

xiii DECLARATION . xv ACKNOWLEDGEMENTS . xvi CHAPTER 1: INTRODUCTION . 1 Description of the Problem . 1 Importance of the Study . 1 Methods of Study . 5 Significant Findings . 6 Implications . 7 Chapter Synopsis . 7 CHAPTER 2: LITERATURE REVIEW . 9 Background to PTSD . 9 Brief History . 9 DSM IV TR Diagnostic Criteria for Post Traumatic Stress Disorder . 10 Epidemiology . 12 Comorbidity . 13
A Systematic Approach for Data Mutation Based Test Case Generation

Mutation testing is taken as a very powerful tool dependent testing technique but it is too costly. It is a pleasant way of using large number of test requirements for ensuring quality. On the other hand it also need of heavy automation. It is considered costly because of the high number of requirements it creates compared to other testing techniques and it is tool dependent for the same reason. In this paper, we present an incipient technique called data mutation predicated on engendering an immensely colossal number of test data from initial test cases either manually or with some automatic test case generation method. It is influenced by some mutation testing methods, but varies from the manner that mutation operators are defined and utilized. While mutation testing is a technique for quantifying test adequacy, data mutation is a technique of test case generation. In traditional mutation testing, mutation operators are acclimated to convert the program under test. In contrast, mutation operators in our task are applied on input data to engender test cases, hence called data mutation operators. In this paper will be implemented with the approach on testing an automated modelling implement to describe the applicability of the proposed method.
Retrieving large-scale high density video target tracks from spatial database

With more and more live sensors being added to geospatial applications, huge amount of sensor data are generated and saved in spatial database. Managing and mining these large-scale ever-changing data becomes new challenges for geospatial studies. In this paper, we present an application-oriented case study to show how to retrieve target tracking data from big dataset saved in spatial database. Our video event retrieval system collects thirty days (8790 GB) high definition video data from six surveillance cameras, analyze them and extract roughly ten million video target tracks. These tracks are projected onto world coordinates and pumped into a spatial database. The system performance of inserting and retrieving these tracks is analyzed in terms of spatial data type design, spatial index configuration, online operation capacity, query optimization and scalability handling. Our insights of saving, managing and retrieving target tracks in a large-scale are presented.
Performance analysis and design of a hessenberg reduction using stabilized blocked elementary transformations for new architectures

The solution of nonsymmetric eigenvalue problems, Ax = λx, can be accelerated substantially by first reducing A to an upper Hessenberg matrix H that has the same eigenvalues as A. This can be done using Householder orthogonal transformations, which is a well established standard, or stabilized elementary transformations. The latter approach, although having half the flops of the former, has been used less in practice, e.g., on computer architectures with well developed hierarchical memories, because of its memory-bound operations and the complexity in stabilizing it. In this paper we revisit the stabilized elementary transformations approach in the context of new architectures -- both multicore CPUs and Xeon Phi coprocessors. We derive for a first time a blocking version of the algorithm. The blocked version reduces the memory-bound operations and we analyze its performance. A performance model is developed that shows the limitations of both approaches. The competitiveness of using stabilized elementary transformations has been quantified, highlighting that it can be 20 to 30% faster on current high-end multicore CPUs and Xeon Phi coprocessors.
Improving the Parallelized Pollard Rho Method for Computing Elliptic Curve Discrete Logarithms

Pollard rho method and its parallelized variant are at present known as the best generic algorithms for computing elliptic curve discrete logarithms. We design new iteration functions for the parallel rho method by exploiting the fact that for any two points P and Q, we can efficiently get P-Q when we compute P+Q. We present a careful analysis of the alternative rho method with new iteration functions. Compare to the previous parallel r-adding walk, generally the new method can reduce the size of the space that is being searched by a factor of 4 with the additional costs of 2 field multiplications and 1 squaring at each iteration step for computing elliptic curve discrete logarithms.
Cost-utility of transcatheter aortic valve implantation for inoperable patients with severe aortic stenosis treated by medical management: a UK cost-utility analysis based on patient-level data from the ADVANCE study

Objective To use patient-level data from the ADVANCE study to evaluate the cost-effectiveness of transcatheter aortic valve implantation (TAVI) compared to medical management (MM) in patients with severe aortic stenosis from the perspective of the UK NHS. Methods A published decision-analytic model was adapted to include information on TAVI from the ADVANCE study. Patient-level data informed the choice as well as the form of mathematical functions that were used to model all-cause mortality, health-related quality of life and hospitalisations. TAVI-related resource use protocols were based on the ADVANCE study. MM was modelled on publicly available information from the PARTNER-B study. The outcome measures were incremental cost-effectiveness ratios (ICERs) estimated at a range of time horizons with benefits expressed as quality-adjusted life-years (QALY). Extensive sensitivity/subgroup analyses were undertaken to explore the impact of uncertainty in key clinical areas. Results Using a 5-year time horizon, the ICER for the comparison of all ADVANCE to all PARTNER-B patients was £13 943 per QALY gained. For the subset of ADVANCE patients classified as high risk (Logistic EuroSCORE >20%) the ICER was £17 718 per QALY gained). The ICER was below £30 000 per QALY gained in all sensitivity analyses relating to choice of MM data source and alternative modelling approaches for key parameters. When the time horizon was extended to 10 years, all ICERs generated in all analyses were below £20 000 per QALY gained. Conclusion TAVI is highly likely to be a cost-effective treatment for patients with severe aortic stenosis.
Glycogen Metabolic Genes Are Involved in Trehalose-6-Phosphate Synthase-Mediated Regulation of Pathogenicity by the Rice Blast Fungus Magnaporthe oryzae

The filamentous fungus Magnaporthe oryzae is the causal agent of rice blast disease. Here we show that glycogen metabolic genes play an important role in plant infection by M. oryzae. Targeted deletion of AGL1 and GPH1, which encode amyloglucosidase and glycogen phosphorylase, respectively, prevented mobilisation of glycogen stores during appressorium development and caused a significant reduction in the ability of M. oryzae to cause rice blast disease. By contrast, targeted mutation of GSN1, which encodes glycogen synthase, significantly reduced the synthesis of intracellular glycogen, but had no effect on fungal pathogenicity. We found that loss of AGL1 and GPH1 led to a reduction in expression of TPS1 and TPS3, which encode components of the trehalose-6-phosphate synthase complex, that acts as a genetic switch in M. oryzae. Tps1 responds to glucose-6-phosphate levels and the balance of NADP/NADPH to regulate virulence-associated gene expression, in association with Nmr transcriptional inhibitors. We show that deletion of the NMR3 transcriptional inhibitor gene partially restores virulence to a Δagl1Δgph1 mutant, suggesting that glycogen metabolic genes are necessary for operation of the NADPH-dependent genetic switch in M. oryzae.
Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images

The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.
Sampled-data Observer Error Linearization

An analysis of the effects of time-sampling on the obseroer error lineariza-tion design methodology shows that requiring the method to be applicable for an open set of sampling times trioializes the class of allowable systems. Abstract-The effects of time-sampling on the solvability conditions for the observer linearization design methodology are investigated. It is shown that the class of systems for which this design method can be applied for an open set of sampling times is quite small. In particular, when the dimension of the state space is two, it consists only of those systems that are state-equivalent to a linear system. The practical implication is that digital implementations of this methodology will have to be approximate.
The birth weight "paradox" uncovered?

Low birth weight (LBW) infants have lower infant mortality in groups in which LBW is most frequent. For example, in 1991, US infants born to smokers had higher risks of both LBW and infant mortality than infants born to nonsmokers. However, among LBW infants, infant mortality was lower for infants born to smokers (relative rate = 0.79). There are competing theories regarding this so-called "paradox. " One is that maternal smoking is beneficial for LBW infants. The authors use causal diagrams to show that, even in the absence of any beneficial effect of smoking, an inverse association due to stratification on birth weight can be found. This variable is affected by the exposure of interest and shares common causes with the outcome. That is, LBW infants born to smokers may have a lower risk of mortality than other LBW infants whose LBW is due to causes associated with high mortality (e.g., birth defects). Under realistic causal diagrams, adjustment for birth weight is unwarranted when the analytical goal is to estimate overall effects of prenatal variables on infant mortality. Even for estimating direct effects of prenatal variables, adjustment for birth weight may be invalid when there is an unmeasured common cause of LBW and mortality. An appropriate justification for conditioning on birth weight requires specifying 1) the causal question motivating this analytical approach and 2) the assumptions regarding the proposed underlying biologic mechanisms.
Oxidative Stress and Pulmonary Changes in Experimental Liver Cirrhosis

The use of carbon tetrachloride (CCl4) in rats is an experimental model of hepatic tissue damage; which leads to fibrosis, and at the long term, cirrhosis. Cirrhosis is the consequence of progressive continued liver damage, it may be reversible when the damaging noxae have been withdrawn. The aim of this study is to evaluate the changes caused by cirrhosis in lung and liver, through the experimental model of intraperitoneal CCI4 administration. We used 18 male Wistar rats divided into three groups: control (CO) and two groups divided by the time of cirrhosis induction by CCI4: G1 (11 weeks), G2 (16 weeks). We found significant increase of transaminase levels and lipid peroxidation (TBARS) in liver and lung tissue and also increased antioxidant enzymes SOD and CAT, as well as the expression of TNF-α and IL-1β in the lung of cirrhotic animals. We observed changes in gas exchange in both cirrhotic groups. We can conclude that our model reproduces a model of liver cirrhosis, which causes alterations in the pulmonary system that leads to changes in gas exchange and size of pulmonary vessels.
Perspectives on Data Integration in Human Complex Disease Analysis

The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power allow building and processing large collections of these data types. However, the super-rapid data generation is counterweighted by a slow-pace for data integration methods development. Most currently available integrative analytic tools pertain to pairing omics data and focus on between-data source relationships, making strong assumptions about within-data source architectures. A limited number of initiatives exist aiming to find the most optimal ways to analyze multiple, possibly related, omics databases, and fully acknowledge the specific characteristics of each data type. A thorough understanding of the underlying assumptions of integrative methods is needed to draw sound conclusions afterwards. In this chapter, the authors discuss how the field of “integromics” has evolved and give pointers towards essential research developments in this context.
Discovery and Characterization of Two Novel Salt-Tolerance Genes in Puccinellia tenuiflora

Puccinellia tenuiflora is a monocotyledonous halophyte that is able to survive in extreme saline soil environments at an alkaline pH range of 9–10. In this study, we transformed full-length cDNAs of P. tenuiflora into Saccharomyces cerevisiae by using the full-length cDNA over-expressing gene-hunting system to identify novel salt-tolerance genes. In all, 32 yeast clones overexpressing P. tenuiflora cDNA were obtained by screening under NaCl stress conditions; of these, 31 clones showed stronger tolerance to NaCl and were amplified using polymerase chain reaction (PCR) and sequenced. Four novel genes encoding proteins with unknown function were identified; these genes had no homology with genes from higher plants. Of the four isolated genes, two that encoded proteins with two transmembrane domains showed the strongest resistance to 1.3 M NaCl. RT-PCR and northern blot analysis of P. tenuiflora cultured cells confirmed the endogenous NaCl-induced expression of the two proteins. Both of the proteins conferred better tolerance in yeasts to high salt, alkaline and osmotic conditions, some heavy metals and H2O2 stress. Thus, we inferred that the two novel proteins might alleviate oxidative and other stresses in P. tenuiflora.
Mass and Dust in the Disk of a Spiral Lens Galaxy

Gravitational lensing is a potentially important probe of spiral galaxy structure, but only a few cases of lensing by spiral galaxies are known. We present Hubble Space Telescope and Magellan observations of the two-image quasar PMN J2004-1349, revealing that the lens galaxy is a spiral galaxy. One of the quasar images passes through a spiral arm of the galaxy and suffers 3 mag of V-band extinction. Using simple lens models, we show that the mass quadrupole is well aligned with the observed galaxy disk. A more detailed model with components representing the bulge and disk gives a bulge-to-disk mass ratio of 0.16 ± 0.05. The addition of a spherical dark halo, tailored to produce an overall flat rotation curve, does not change this conclusion.
Credit Card Fraud Detection Using Hidden Markov Model

￻ Abstract — The most accepted payment mode is credit card for both online and offline in today's world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.
Personal Spoken Sentence Retrieval Using Two-Level Feature Matching and MPEG-7 Audio LLDs

Conventional spoken sentence retrieval (SSR) relies on a large-vocabulary continuous- speech recognition (LVCSR) system. This investigation proposes a feature-based speaker- dependent SSR algorithm using two-level matching. Users can speak keywords as the query inputs to get the similarity ranks from a spoken sentence database. For instance, if a user is looking for a relevant personal spoken sentence, "October 12, I have a meeting in New York" in the database, then the appropriate query input could be "meeting", "New York" or "October". In the first level, a Similar Frame Tagging scheme is proposed to locate possible segments of the database sentences that are similar to the user's query utterance. In the second level, a Fine Similarity Evaluation between the query and each possible segment is performed. Based on the feature-based comparison, the proposed al- gorithm does not require acoustic and language models, thus our SSR algorithm is language independent. Effective feature selection is the next issue in this paper. In addition to the conventional mel frequency cepstrum coefficients (MFCCs), several MPEG-7 audio low- level descriptors (LLDs) are also used as the features to exploit their ability for SSR. Ex- perimental results revealed that the retrieval performance using MPEG-7 audio LLDs was close to that of the MFCCs. The combination of MPEG-7 audio LLDs and the MFCCs could further improve the retrieval precision. Based on the feature-based matching, the proposed algorithm has the advantages of language independent and speaker dependent training free. Comparing to the original methods (10, 11), with only 0.026 ~ 0.05 precision decrease, the addition and multiplication numbers are reduced by around a factor of lq (frame number of query). It is particularly suitable for the use in resource-limited devices.
Identifying Moral Hazard in Car Insurance Contracts1

This paper capitalizes on a unique situation in Israel where car insurance coverage is often distributed as a bene t by employers. Employer-determined coverage creates variation in accident costs that are not a function of individual choice. Using instrumental variable analysis on data provided by an insurance rm in Israel (2001-2008), we nd that a $100 discount in accident costs increases the probability of an accident by 3.4-3.7 percentage points. These results can be interpreted as the e¤ect of moral hazard on car accidents.
Robust numerical valuation of European and American options under the CGMY process

We develop an implicit discretization method for pricing European and American options when the underlying asset is driven by an infinite activity Levy process. For processes of finite variation, quadratic convergence is obtained as the mesh and time step are refined. For infinite variation processes, better than first order accuracy is achieved. The jump component in the neighborhood of log jump size zero is specially treated by using a Taylor expansion approximation and the drift term is dealt with using a semi-Lagrangian scheme. The resulting Partial Integro-Differential Equation (PIDE) is then solved using a preconditioned BiCGSTAB method coupled with a fast Fourier transform. Proofs of fully implicit timestepping stability and monotonicity are provided. The convergence properties of the BiCGSTAB scheme are discussed and compared with a fixed point iteration. Numerical tests showing the convergence and performance of this method for European and American options under processes of finite and infinite variation are presented.