Miracle-dz/UniFAR-FTUs huggingface dataset:
Off-Policy Learning Combined with Automatic Feature Expansion for Solving Large MDPs Alborz

Reinforcement learning (RL) techniques with cheap computational complexity and minimal hand-tuning that scale to large problems are highly desired among RL practitioners. Linear function approximation has scaled existing RL techniques to large problems [Lagoudakis and Parr, 2003; Silver et al., 2012], however that technique has two major drawbacks: 1) conventional off-policy techniques such as Q-Learning can be unstable when combined with linear function approximation [Baird, 1995] and 2) finding the “right” set of features for approximation can be challenging. The first drawback has been recently addressed with the introduction of the Greedy-GQ algorithm, a convergent extension of Q-Learning [Maei et al., 2010]. The second drawback led to representation expansion techniques that add new features along the learning process [Geramifard et al., 2011; Keller et al., 2006; Parr et al., 2007]. Amongst these techniques, incremental Feature Dependency Discovery (iFDD) has shown great potential as it scaled to large problems while enjoying convergence results [Geramifard et al., 2011]. Recently, iFDD [Geramifard et al., 2013b] improved the performance of iFDD in the prediction problem while outperforming the previous state-of-the-art batch expansion technique OMP-TD [Painter-Wakefield and Parr, 2012]. This paper connects Greedy-GQ learning with iFDD and, for the first time, introduces an online off-policy learning with automatic feature expansion technique. Given sparse features, the new algorithm has per-time-step complexity independent of the total number of features, while for most existing techniques feature discovery is at least quadratic in the number features [Keller et al., 2006; Parr et al., 2007]. Empirical results across 3 domains with sizes up to 77 billion state-action pairs verify the scalability of our new approach.
Reconstructing Transient Images from Single-Photon Sensors : Supplemental Document

1. Derivation of Joint Denoising and Deconvolution 1.1. Image Formation In many imaging problems, such as low-light photography, microscopy, and also single-photon imaging, Poisson-distributed shot noise dominates the image formation. Note that no engineering effort can mitigate this, because shot noise is an inherent property of the particle nature of light. The image formation can be expressed as h ∼ P (Aτ + d) , (1) where P models a Poisson distribution. Here, we represent the temporal impulse response (i.e., the transient image), the measured histogram, and the dark count as discrete column vectors τ ,h,d ∈ Rnxnynt×1. The matrix A ∈ Rnxnynt×nxnynt encodes the convolution of the transient image with the laser pulse and SPAD jitter (g ∗ f) [t] as well as other scale factors. Each transient image has a resolution of nx × ny pixels and each pixel has nt time bins. The probability of having taken a measurement at one particular SPAD and histogram bin i is thus given as
Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach

This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR’19) (Mille et al., 2019). Based on Ferreira et al. (2017) and on our 2018 submission Ferreira et al. (2018), the approach generates texts by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a rule-based and a statistical machine translation (SMT) model. This year our submission is able to realize texts in the 11 languages proposed for the task, different from our last year submission, which covered only 6 Indo-European languages. The model is publicly available.
Dealing with Outliers and Offsets in Radiocarbon Dating

The wide availability of precise radiocarbon dates has allowed researchers in a number of disciplines to address chronological questions at a resolution which was not possible 10 or 20 years ago. The use of Bayesian statistics for the analysis of groups of dates is becoming a common way to integrate all of the 14C evidence together. However, the models most often used make a number of assumptions that may not always be appropriate. In particular, there is an assumption that all of the 14C measurements are correct in their context and that the original 14C concentration of the sample is properly represented by the calibration curve. In practice, in any analysis of dates some are usually rejected as obvious outliers. However, there are Bayesian statistical methods which can be used to perform this rejection in a more objective way (Christen 1994b), but these are not often used. This paper discusses the underlying statistics and application of these methods, and extensions of them, as they are implemented in OxCal v 4.1. New methods are presented for the treatment of outliers, where the problems lie principally with the context rather than the 14C measurement. There is also a full treatment of outlier analysis for samples that are all of the same age, which takes account of the uncertainty in the calibration curve. All of these Bayesian approaches can be used either for outlier detection and rejection or in a model averaging approach where dates most likely to be outliers are downweighted. Another important subject is the consistent treatment of correlated uncertainties between a set of measurements and the calibration curve. This has already been discussed by Jones and Nicholls (2001) in the case of marine reservoir offsets. In this paper, the use of a similar approach for other kinds of correlated offset (such as overall measurement bias or regional offsets in the calibration curve) is discussed and the implementation of these methods in OxCal v 4.0 is presented.
AN ABSTRACT OF THE THESIS OF Sofean Ahmed Maeouf for the degree of Master of Science in Electrical and Computer Engineering presented on March 15, 2012. Title: End-to-End Network Throughput Enhancement Through Physical-Layer Network Coding Abstract approved:

approved: Bechir Hamdaoui Physical-Layer Network Coding (PNC) is a promising technique that has great potentials for improving the achievable data rates of end-to-end flows through higher packet transmission rates, thereby increasing the overall network throughput. In this thesis, we study the performance of the PNC transmission techniques for unidirectional end-to-end flows in multi-hop wireless networks, and compare it with that of the traditional transmission techniques. We first derive the bit-error rate (BER) that the PNC transmission technique achieves. Then, using the derived BER, we evaluate and quantify the achievable network throughput under both the PNC transmission technique and the traditional technique, where the network throughput is measured as the aggregate/sum of all end-to-end flows’ achievable data rates in the wireless network. Using extensive simulations, we show that PNC increases the overall achievable end-to-end flow throughput in multi-hop wireless networks, especially under medium to high signal-to-noise ratios. c ©Copyright by Sofean Ahmed Maeouf March 15, 2012 All Rights Reserved End-to-End Network Throughput Enhancement Through Physical-Layer Network Coding
Dhalion in Action: Automatic Management of Streaming Applications

In a world where organizations are being inundated with data from various sources, analyzing data and gaining actionable insights in real-time has become a key service differentiator. Over the last few years, several stream processing frameworks have been developed to address the need for large-scale, real-time analytics. A crucial challenge in these environments is the complexity of configuring, managing and deploying long-running streaming applications. Operators must carefully tune these systems to balance competing objectives such as resource utilization and performance. At the same time, they must also account for external shocks such as unexpected load variations and service degradations. In this demonstration, we show how operators can maintain healthy streaming applications without manual intervention while still meeting their performance objectives. We use Dhalion, an open-source library that sits on top of the streaming application, observes its behavior and automatically takes actions to keep the application in a healthy state. In particular, through various Dhalion policies that are configured by the attendees, we demonstrate how a streaming application can meet its performance objective by automatically configuring the amount of resources needed at the various application stages. We also demonstrate Dhalion's modularity and extensibility that greatly simplifies the process of developing new policies which address different application requirements.
Characterization of professional soccer players ’ muscle performance *

Introduction and objective: The association of muscular performance with risk of injury and functional performance in different sports has been reported in numerous studies in the literature. However, there is a paucity of data that characterizes the muscular performance in Brazilian professional soccer athletes. Therefore, the objective of this study was to make a descriptive analysis of parameters related to the muscular performance of this population. Methods: The sample of this study was composed of 117 athletes belonging to professional soccer clubs in Minas Gerais State. In order to evaluate the muscular performance of the athletes, an isokinetic dynamometer was used and the tests involved maximum voluntary contractions of the selected muscles. Hip abductors and adductors were assessed at the speeds of 30o/s, 60o/ s and 120o/s; knee flexors and extensors at 60o/s, 180o/s and 300o/ s; and ankle dorsiflexors, plantarflexors, invertors and evertors at 30o/s, 60o/s and 180o/s. Descriptive statistics were used to present the normative data and paired t-tests were used to identify significant differences between legs considering the parameters evaluated in this study. Results: This study generated normative data to characterize the profile of Brazilian professional soccer players relative to their capacity of producing torque, muscle work and power. Significant differences were observed between legs considering some of the study’s variables. Conclusion: The established normative data can be used as reference values in the prevention, training and rehabilitation of the athletes. In addition, these data may be used as reference for future studies with the objective of testing the association between muscular performance and incidence of injury in soccer practice.
, and Vasile Palade ~ 27 ~ Framework for Plagiarism Detection Using Logical Tree-Structured Features and Multi-Layer Clustering

Different practices of scientific misconduct have appeared recently and that impose the need for more sophisticated solutions. Logical tree-structured features describe the topology of scientific publications in terms of meaningful parts such as title, abstract, background, methods, results, and references. This paper presents the methodology proposed to uncover plagiarism in scientific publications using structural document features and multi-layer clustering. Logical treestructured features are extracted as generic classes. Structural components such as paragraphs are organised under these generic classes. Instead of using traditional flat-based plagiarism detection methods, a layer-based clustering approach is proposed to find similar clusters and perform candidate retrieval using the top layer features. The bottom layer features are used to cluster structural components and to detect plagiarism. The suggested framework can be more efficient and reliable to detect plagiarism in scholarly articles than existing approaches.
Reduced diversity and stability of coral-associated bacterial communities and suppressed immune function precedes disease onset in corals

Disease is an emerging threat to coral reef ecosystems worldwide, highlighting the need to understand how environmental conditions interact with coral immune function and associated microbial communities to affect holobiont health. Increased coral disease incidence on reefs adjacent to permanently moored platforms on Australia's Great Barrier Reef provided a unique case study to investigate environment–host–microbe interactions in situ. Here, we evaluate coral-associated bacterial community (16S rRNA amplicon sequencing), immune function (protein-based prophenoloxidase-activating system), and water quality parameters before, during and after a disease event. Over the course of the study, 31% of tagged colonies adjacent to platforms developed signs of white syndrome (WS), while all control colonies on a platform-free reef remained visually healthy. Corals adjacent to platforms experienced significant reductions in coral immune function. Additionally, the corals at platform sites that remained visually healthy throughout the study had reduced bacterial diversity compared to healthy colonies at the platform-free site. Interestingly, prior to the observation of macroscopic disease, corals that would develop WS had reduced bacterial diversity and significantly greater community heterogeneity between colonies compared to healthy corals at the same location. These results suggest that activities associated with offshore marine infrastructure impacts coral immunocompetence and associated bacterial community, which affects the susceptibility of corals to disease.
Modified HEPES one-pot synthetic strategy for gold nanostars

Gold nanostars are being used more regularly in the biosensing field. Despite their useful attributes, there is still a need to optimize aspects of the synthesis and stability. The seedless, synthetic method comprising 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) is a facile, rapid method; however, it produces heteromorphic nanostars. The modification of a HEPES method resulted in a silver-assisted, seedless gold nanostar synthesis method. The nanostars resulting from this method were monodispersed, multi-branched and approximately 37 ± 2 nm in diameter. It proved to be a repeatable method that produced homogeneous and robust nanostars. Once functionalized with polyvinylpyrrolidone 10 000, the new nanostars were observed to be stable in various environments such as salt, ionic strength and cell culture medium. In conclusion, the addition of the silver nitrate improved the morphology of the reported HEPES nanostars for the purpose of nanobiosensor development.
Comparing species and measures of landscape structure as indicators of conservation importance

1. The use of indicators to identify areas of conservation importance has been challenged on several grounds, but nonetheless retains appeal as no more parsimonious approach exists. Among the many variants, two indicator strategies stand out: the use of indicator species and the use of metrics of landscape structure. While the first has been thoroughly studied, the same cannot be said about the latter. We aimed to contrast the relative efficacy of species-based and landscape-based indicators by: (i) comparing their ability to reflect changes in community integrity at regional and landscape spatial scales, (ii) assessing their sensitivity to changes in data resolution, and (iii) quantifying the degree to which indicators that are generated in one landscape or at one spatial scale can be transferred to additional landscapes or scales. 2. We used data from more than 7000 bird captures in 65 sites from six 10 000-ha landscapes with different proportions of forest cover in the Atlantic Forest of Brazil. Indicator species and landscape-based indicators were tested in terms of how effective they were in reflecting changes in community integrity, defined as deviations in bird community composition from control areas. 3. At the regional scale, indicator species provided more robust depictions of community integrity than landscape-based indicators. At the landscape scale, however, landscape-based indicators performed more effectively, more consistently and were also more transferable among landscapes. The effectiveness of high resolution landscape-based indicators was reduced by just 12% when these were used to explain patterns of community integrity in independent data sets. By contrast, the effectiveness of species-based indicators was reduced by 33%. 4. Synthesis and applications. The use of indicator species proved to be effective; however their results were variable and sensitive to changes in scale and resolution, and their application requires extensive and time-consuming field work. Landscape-based indicators were not only effective but were also much less context-dependent. The use of landscape-based indicators may allow the rapid identification of priority areas for conservation and restoration, and indicate which restoration strategies should be pursued, using remotely sensed imagery. We suggest that landscape-based indicators might often be a better, simpler, and cheaper strategy for informing decisions in conservation.
Think Before Your Click: Data and Models for Adult Content in Arabic Twitter

Given the widespread use of social media and their increasingly impactful role in our lives today, there is a pressing need to ensure their safety of use. In particular, various social groups view the spread of adult content in social networks as undesirable. This content may even pose a serious threat to other vulnerable groups (e.g. children). In this work, we develop a unique, large-scale dataset of adult content in Arabic Twitter and provide in-depth analyses of the data. The dataset enables us to study the scope and distribution of adult content in the Arabic version of the network, thus possibly uncovering target phic locales. In addition, computationally exploit the data to learn a large lexicon specific to the topic and detect spreaders of adult content on the microblogging platform. Our models achieve promising results, reaching 79% accuracy on the task (24% higher than a competitive baseline).
Assessment of curvature approximation methods in the simulation of viscous biological membranes

The dynamics and rest shape of biological membranes are best explained in terms of the minimization of the Canham-Helfrich energy. This energy is directly dependant on the curvature of the membrane itself. On finite-element-based simulations, curvature information must be infered from discrete surfaces. In this work, four of the most popular available methods are assessed in their ability to properly describe the dynamics of viscous biological membranes. 1 Biological Membranes Biological membranes are formed by bilayers of phospholipidic molecules. These molecules are hydrophilic on one side hydrophobic on the other. Because of that, in aqueous solutions, they become a closed surface, called liposome or vesicle. These membranes are known to be inextensible, i.e. the surface area of a vesicle cannot change. The enclosed volume is also constant because the vesicle is usually filled with incompressible fluid. The molecules on the membrane are free to rearrange themselves and change the shape of the vesicle. The rest shape is attained when the elastic bending energy over the whole membrane reaches a local minimum. This energy was first studied by Canham [1], Evans [2] and Helfrich [4]. It has the form:
A Numerical Study of the Antipodal Vivaldi Antenna Design for Ultra Wideband Applications

In this paper, a numerical analysis is invoked to study the performance of a Corrugated-shaped Antipodal Vivaldi (CAV) antenna. The proposed antenna provides a wideband with an acceptable gain in the endfire direction. Nevertheless, the antenna is consistent of two copper layers wings separated by an FR4-Epoxy dielectric substrate with dimension of 50×38 mm 2 . The corrugated antenna is feeding by used the electromagnetic coupling technique. The feeding structure is designed as L-geometry which is located at the middle. It is found that such antenna shows a wideband, S11 < -10, ranges from 5 GHz up to more than 20 GHz and shows a frequency mode at 1.58 GHz. The radiation patterns are found mostly in the endfire with gain varying from 3.7 dB to 6.6 dB over the entire band. The performance of the proposed design is tested using HFSS software package.
Snake states and their symmetries in graphene

Snake states are open trajectories for charged particles propagating in two dimensions under the influence of a spatially varying perpendicular magnetic field. In the quantum limit they are protected edge modes that separate topologically inequivalent ground states and can also occur when the particle density rather than the field is made nonuniform. We examine the correspondence of snake trajectories in single-layer graphene in the quantum limit for two families of domain walls: (a) a uniform doped carrier density in an antisymmetric field profile and (b) antisymmetric carrier distribution in a uniform field. These families support different internal symmetries but the same pattern of boundary and interface currents. We demonstrate that these physically different situations are gauge equivalent when rewritten in a Nambu doubled formulation of the two limiting problems. Using gauge transformations in particle-hole space to connect these problems, we map the protected interfacial modes to the Bogoliubov quasiparticles of an interfacial one-dimensional p-wave paired state. A variational model is introduced to interpret the interfacial solutions of both domain wall problems.
DataStorm-FE: A Data- and Decision-Flow and Coordination Engine for Coupled Simulation Ensembles

Data- and model-driven computer simulations are increasingly critical in many application domains. Yet, several critical data challenges remain in obtaining and leveraging simulations in decision making. Simulations may track 100s of parameters, spanning multiple layers and spatial-temporal frames, affected by complex inter-dependent dynamic processes. Moreover, due to the large numbers of unknowns, decision makers usually need to generate ensembles of stochastic realizations, requiring 10s-1000s of individual simulation instances. The situation on the ground evolves unpredictably, requiring continuously adaptive simulation ensembles. We introduce the DataStorm framework for simulation ensemble management, and demonstrate its DataStorm-FE data- and decision-flow and coordination engine for creating and maintaining coupled, multi-model simulation ensembles. DataStorm-FE enables end-to-end ensemble planning and optimization, including parameter-space sampling, output aggregation and alignment, and state and provenance data management, to improve the overall simulation process. It also aims to work efficiently, producing results while working within a limited simulation budget, and incorporates a multivariate, spatiotemporal data browser to empower decision-making based on these improved results.
Virtualization Tools and Techniques: Survey

Virtualization is a technique in which the user required services run remotely in a ubiquitous environment which gives scalable resources. Virtualization is being used in cloud computing for load balancing and aggregation of cloud resources. Virtualization provides higher hardware utilization. It is also being used for partitioning of computational resources and hence supports sharing of resources. Virtualization has different types such as Native virtualization, Full virtualization, Operating system level virtualization and Para virtualization. Other than these there is Resources virtualization, Desktop virtualization, Server virtualization, Data centres virtualization and application virtualization. The resources virtualization is implemented in different forms such as the Full virtualization, Native virtualization, Para virtualization, Operating system (OS) layer virtualization or Hosted virtualization. Virtual machines and Virtual machine monitors (VMMs) have been developed to offer better energy efficient solutions to the virtualization problems. Virtualization tools like OpenVz, Xen, VmWare etc. are widely used in the computing industry. DOI: http://dx.doi. org/njst.v15i2. 12131 Nepal Journal of Science and Technology Vol. 15, No.2 (2014) 141-150