Miracle-dz/UniFAR-FTUs huggingface dataset:
Draft Interactive Effects of Sex and Body Size on Adfluvial Bull Trout ( Salvelinus confluentus ) Movement Ecology

Complete List of Authors: Gutowsky, Lee; Carleton University, Department of Biology Harrison, Philip; University of Waterloo, Department of Biology Guimaraes Martins, Eduardo; Universidade Estadual de Campinas Leake, Alf; BC Hydro, Environmental Risk Management Patterson, David; Fisheries and Oceans Canada, Cooperative Resource Management Institute Power, Mike; University of Waterloo, Department of Biology Cooke, Steven; Carleton University, Department of Biology; Carleton University, Institute of Environmental Science
P 8 R . 6 AN INTEGRATED DISPLAY AND ANALYSIS METHODLOGLY FOR MULTIVARIABLE RADAR DATA

During field experiments, it is necessary to make real time decisions using the available field data to direct aircraft operations, develop radar scanning strategies to best meet the scientific objectives of the project, do project nowcasting, develop scientific insights in the field, etc. These can be difficult tasks, especially for extensive projects involving several aircraft and multiple ground-based sensors. Having too much data available can inhibit operations too, since the scientist may have to take time to interpret many different variables from several radars, and combine them with data from other instruments. Large field projects in the past, such as the Severe Environmental Storms and Mesoscale Experiment (SESAME) in 1979, the Cooperative Convective Precipitation Experiment (CCOPE) in 1981, the Oklahoma-Kansas Preliminary Regional Experiment for STORM-Central (OK PRESTORM) in 1985, and most recently, the Mesoscale Alpine Programme (MAP) in 1999, have demonstrated the utility of analyzing realtime radar data for use in directing the scientific objectives of the project as well as in now-casting (Biter and Johnson, 1982; Knight, 1982; Cunning, 1986; Houze et al., 1989; Brown, 1992; Chong et al., 2000). However, interpreting the data from many radar variables, as from polarimetric radars, can be time consuming and requires advanced knowledge by the user (Vivekanandan et al., 1999). Thus, it is useful to extract information from the data and combine it into integrated products such as bulk hydrometeor identification, Dopplerderived winds, and radar mosaics to be viewed in real-time. Methods for real-time analysis and processing of radar data have been developed by Vivekanandan et al. (1999) and Chong et al. (2000) and others. The MountainZebra software system described by James et al. (2000) has been
Network intrusion detection system by using genetic algorithm

Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network.
Control Performance and Robustness of Pounding Tuned Mass Damper for Vibration Reduction in SDOF Structure

This paper investigates the control performance of pounding tuned mass damper (PTMD) in reducing the dynamic responses of SDOF (Single Degree of Freedom) structure. Taking an offshore jacket-type platform as an example, the optimal damping ratio and the gap between mass block and viscoelastic material are presented depending on a parametric study. Control efficiency influenced by material properties and contact geometries for PTMD is analyzed here, as well as robustness of the device. The results of numerical simulations indicated that satisfactory vibration mitigation and robustness can be achieved by an optimally designed PTMD. Comparisons between PTMD and traditional TMD demonstrate the advantages of PTMD, not only in vibration suppression and costs but also in effective frequency bandwidth.
The research and implementation of The Critical Path on a Processor (CPOP) Algorithm based on Pi calculus

Task scheduling algorithms in heterogeneous computing environment often provide limited performance owing to their low efficiency. This paper models and implements the first two phases of the Critical Path (CP) on a Processor Algorithm with the functional programming paradigm. Firstly, an overall research is conducted on the CPOP algorithms, then the logical relationship of the first two phrases of the algorithm is modeled based on Pi calculus theories, and finally the algorithm is implemented by nPict programming language. The experimental results demonstrate that the implementation efficiency of the new programming algorithm is higher than that of the traditional C++ language. Therefore, Pi calculus can be applied to the three phases of the CPOP algorithm to improve the task scheduling efficiency. This paper aims to prove that the CPOP algorithm can be more efficient than ever by using Pi calculus, through modeling and comparing the topological structures of the different types of task scheduling models.
A comprehensive study of the rate-distortion performance in MPEG point cloud compression

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.
Byte2vec: Malware Representation and Feature Selection for Android

Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2vec, using skip-gram with negative-sampling topology, are combined with byte-level term-frequency (tf) for malware detection. We also show that the distance between a feature vector and its corresponding context vector provides a useful measure to rank features. The top ranked features are successfully used for malware detection. We show that this feature selection algorithm is an unsupervised version of mutual information (MI). We test the proposed scheme on four freely available Android malware datasets including one obfuscated malware dataset. The model is trained only on clean APKs. The results show that the model outperforms MI in a low-dimensional feature space and is competitive with MI and other state-of-the-art models in higher dimensions. In particular, our tests show very promising results on a wide range of obfuscated malware with a false negative rate of only 0.3% and a false positive rate of 2.0%. The detection results on obfuscated malware show the advantage of the unsupervised feature selection algorithm compared with the MI-based method.
A Close-in LiDAR for Diffusive Media based on a 32 × 32 CMOS SPAD Image Sensor

The ability of light detection and ranging (LiDAR) systems to provide depth information of a scene has already sparked development and application in a wide range of areas, e.g. self-driving vehicles, video game systems, industrial automation, etc. These developments have focused on applications where the surface of objects under study provides a reflection, and the non-reflected light is absorbed. This eases the depth reconstruction since received light can be assumed to have reflected from the object’s surface. There are, however, applications where this is not the case, for example, in robotic-assisted surgery [1], where surgical instruments are inserted into the patient through small incisions. Here, a LiDAR could provide the surgeon with an accurate spatial map of the surgical environment, in particular the position of instruments in relation to the tissue. Additionally, a map of the tissue boundary would be important for applications which aim to provide information from within the medium, e.g. fluorescence lifetime imaging [2]. Such applications are enabled by the presence of an ‘optical window’ between 600 and 900 nm, in which light incident onto the surface can penetrate tissue for several centimeters. For the non-absorbed light, the scattering nature of tissue results in the diffusion of light into the tissue surface. This is demonstrated on a silicone phantom shown in Fig. 1. This presents a major challenge for LiDAR at these wavelengths, since light which returns to the sensor is not solely from a reflection from the surface. An alternative solution could be to operate with light at a shorter wavelength, e.g. green, however, this would require an additional laser for applications which seek to image beneath the surface whilst also being visible to the operator and any other instruments.
Cosmic Muon Detection for Geophysical Applications

A portable cosmic muon detector has been developed for environmental, geophysical, or industrial applications. The device is a tracking detector based on the Close Cathode Chamber, an MWPC-like technology, allowing operation in natural underground caves or artificial tunnels, far from laboratory conditions. The compact, low power consumption system with sensitive surface of 0.1 m2 measures the angular distribution of cosmic muons with a resolution of 10 mrad, allowing for a detailed mapping of the rock thickness above the muon detector. Demonstration of applicability of the muon telescope (REGARD Muontomograph) for civil engineering and measurements in artificial underground tunnels or caverns are presented.
Repository Conviction Function ? A New Decision Paradigm for Personal inancial Risk Management in the Face of Large Exogenous Shocks

This paper contributes to the limited-information literature on savings in a stochastic environment. In particular, it contributes techniques and concepts to the question of state verification (or filtering), by including learning about aggregate income shocks, based on signals. As a seminal contribution to the extant literature, a “conviction function” is introduced, which takes into account histories of past prediction errors in determining how rational agents internalize such information in taking personal investment decisions. For purpose of a more transparent illustration, a numerical rendition of the posited model is provided for five consecutive time periods. We also perform a series of Monte Carlo simulations to demonstrate how the posited approach could potentially outperform traditional forward-looking models in the presence of sudden large extraneous shocks reminiscent of the recent Global Financial Crisis.
THE IMPACT OF REAL EXCHANGE RATE VOLATILITY ON INDONESIA-US TRADE PERFORMANCE

This study examines the impact of real exchange rate volatility on bilateral trade performance between Indonesia and the United States utilizing the data period between Q1:1990 to Q3 2012. This study deploys two approaches to measure real exchange rate volatility, Autoregressive Conditional Heteroskedasticity (ARCH-1) and Moving Average standard Deviation methods. To test the long run relationship between variables, it uses Autoregressive Distributed Lag (ARDL) bounds testing procedure. The result shows that real exchange rate volatility has negative influence on Indonesia’s import from the United States but does not affect the Indonesia’s export to the United States. Hence, high exchange rate volatility leads to a decrease of Indonesia’s import volume from the United States. If Indonesia attempts to maintain it’s trade balance, it needs to keep intact managed floating exchange rate.
Top management team's vision and human resources management practices in innovative Spanish companies

The objective of the article is to analyse whether the strategic vision of the top management team (TMT) and the way employees working in teams are rewarded and assessed affect companies' innovation performance. The study was carried out on a sample of 97 Spanish companies belonging to the three most innovative sectors based on number of patents registered. The results indicate that the top management team's strategic vision alone does not explain a company's innovation performance. Innovation also requires the existence of compensation practices based on the ideas generated and developed by project teams. These results offer relevant implications for the TMT and the managers of the research and development (R&D) and human resources (HR) functions about the way teams should be rewarded and supported in order to improve the company's innovation efforts.
Evolution of insect life histories in relation to time constraints in seasonal environments : polymorphism and clinal variation

Both the length of the season that is favourable for insect growth and reproduction and the number of generations emerging per season (voltinism) increase with decreasing latitude. Thus, time constraints on reproduction and juvenile development decrease with decreasing latitude, except where voltinism changes and time constraints suddenly increase as the season must be shared with one more generation. I studied the evolution of insect life histories in relation to time constraints from two perspectives: polymorphism and clinal variation. Life history polymorphism in seasonal environments was studied with the butterfly Pieris napi that has discrete life history strategies, and polymorphic natural populations. Experimental studies showed that asymmetric intraspecific larval competition and divergent timing of reproduction between the strategies may promote the maintenance of polymorphism. A simulation model showed that the divergent timing of reproduction between the strategies is sufficient to maintain polymorphism even in the absence of intraspecific competition. Clinal variation was studied empirically with four geometrid moths (Cabera exanthemata, Cabera pusaria, Chiasmia clathrata and Lomaspilis marginata) and generally in theory. Due to latitudinal saw-tooth variation in time constraints, traditional theory predicts a saw-tooth cline in body size and development time. A common garden experiment with the four geometrid moths did not support the traditional theory even when a saw-tooth cline in body size was found, suggesting that the theory is based on unrealistic assumptions. A theoretical analysis showed that reproductive effort should be high in populations under intense time constraints and low in populations experiencing no time constraints, resulting in a saw-tooth cline. In the four geometrid moths, support for these predictions was found, although the observed clinal variation deviated from the predicted pattern. The results imply that clinal variation is expected in almost any continuous life history trait, whereas polymorphism of different strategies may emerge when life histories fall into discrete categories. When inferring the evolution of a single trait, complex interdependencies among several traits should be considered, as well as the possibility that the time constraints are not similar for each generation in multivoltine populations.
Particle Efficient Importance Sampling

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by recognising that the EIS framework has an offline sequential Monte Carlo interpretation. The particle EIS method is based on non-standard resampling weights that take into account the construction of the importance sampler as a sequential approximation to the state smoothing density. We apply the method for a range of univariate and bivariate stochastic volatility specifications. We also develop a new application of the EIS approach to state space models with Student’s t state innovations. Our results show that the particle EIS method strongly outperforms both the standard EIS method and particle filters for likelihood evaluation in high dimensions. We illustrate the efficiency of the method for Bayesian inference using the particle marginal Metropolis–Hastings and importance sampling squared algorithms.
Detection and Tracking of Vortices on Oceanographic Images

This paper deals with the problem of automatic interpretation of oceanographic images for vortices detection, modelization and tracking. We present a framework allowing the vortices detection. The processing is split into three parts: apparent motion computation from the image sequence, local interpretation of apparent motion and geometric modelization of the vortices. This scheme allows an efficient approach for vortices segmentation on very large image sequences. A set of experimental data shows the use of such framework for processing Advanced Very High Resolution Radiometer (AVHRR) and Coastal Zone Color Scanner (CZCS) image sequences
Unsteady Flow and Acoustic Behaviour of an Axial Fan: Numerical and Experimental Investigations

The purpose of this study is to investigate and qualify the unsteady flow behavior in a complete 6-bladed axial fan and its impact on the noise generation. Computational Fluid Dynamics (CFD) analysis based on the Scale-Adaptive Simulation (SAS) turbulence model has been carried out, taking into account the complex environment of the axial fan. Additionally, the unsteady variables provided by the numerical simulations have been employed in the Ffowcs-Williams and Hawkings (FW-H) model in order to predict the far field radiated sound at various locations. On the other hand, the numerical results have been compared to the measurement data at the near field.
Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment

Big data is largely influencing business entities and research sectors to be more data‐driven. Hadoop MapReduce is one of the cost‐effective ways to process large scale datasets and offered as a service over the Internet. Even though cloud service providers promise an infinite amount of resources available on‐demand, it is inevitable that some of the hired virtual resources for MapReduce are left unutilized and makespan is limited due to various heterogeneities that exist while offering MapReduce as a service. As MapReduce v2 allows users to define the size of containers for the map and reduce tasks, jobs in a batch become heterogeneous and behave differently. Also, the different capacity of virtual machines in the MapReduce virtual cluster accommodate a varying number of map/reduce tasks. These factors highly affect resource utilization in the virtual cluster and the makespan for a batch of MapReduce jobs. Default MapReduce job schedulers do not consider these heterogeneities that exist in a cloud environment. Moreover, virtual machines in MapReduce virtual cluster process an equal number of blocks regardless of their capacity, which affects the makespan. Therefore, we devised a heuristic‐based MapReduce job scheduler that exploits virtual machine and MapReduce workload level heterogeneities to improve resource utilization and makespan. We proposed two methods to achieve this: (i) roulette wheel scheme based data block placement in heterogeneous virtual machines, and (ii) a constrained 2‐dimensional bin packing to place heterogeneous map/reduce tasks. We compared heuristic‐based MapReduce job scheduler against the classical fair scheduler in MapReduce v2. Experimental results showed that our proposed scheduler improved makespan and resource utilization by 45.6% and 47.9% over classical fair scheduler.
Chapter In vivo analysis of the antimetastatic effects of anticoagulants on murine cancer cell lines B 16 , K 1735 and CT 26

Background: Cancer metastasis is facilitated by blood coagulation. Anticoagulants, such as low molecular weight heparins (LMWHs) and hirudin, a specific anti-thrombin inhibitor, reduce metastasis mainly by inhibition of thrombin formation and Land P-selectin-mediated cell-cell adhesion. However, exact molecular backgrounds are not understood especially whether the affects are dependent on the cancer cell type and the type of anticoagulant. Objectives: The effect of LMWH on the development of K1735 melanoma metastasis in mouse lungs was investigated. We also compared the antimetastatic effects of hirudin on K1735 and B16 melanoma metastasis and CT26 colon cancer metastasis in mouse lung. Methods/experimental design: Metastatic tumors were determined noninvasively each week up to day 21 in all experiments using bioluminescence imaging. Effects of LMWH and hirudin on metastasis of the three cell lines were correlated with the fibrin/fibrinogen content in the tumors, expression of tissue factor (TF), protease activated receptor (PAR)-1 and -4 and CD24, a ligand of Land P-selectins. Results: LMWH and hirudin did not have effect on K1735 melanoma metastasis in the lungs. Hirudin inhibited metastasis of B16 cells completely but did not affect metastasis of CT26 cells. TF and PAR-4 expression was similar in all three cell lines. PAR-1 and CD24 were hardly expressed by K1735, whereas CT26 cells expressed low levels and B16 high levels of PAR-1 and CD24. Fibrin content of the tumors was not affected by LMWH. Conclusion: The antimetastatic effects of anticoagulants are dependent on the cancer cell type rather than the anticoagulant type. CD24 expression on cancer cells seems required and possibly PAR-1 expression. The molecular mechanisms are important to understand the lifeprolonging effects of anticoagulants in cancer patients.