Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Greeks using multilevel path simulation Technical report , January 2011

We investigate the extension of the multilevel Monte Carlo method [2, 3] to the calculation of Greeks. The pathwise sensitivity analysis [5] differentiates the path evolution and effectively reduces the smoothness of the payoff. This leads to new challenges: the use of naive algorithms is often impossible because of the inapplicability of pathwise sensitivities to discontinuous payoffs. These challenges can be addressed in three different ways: payoff smoothing using conditional expectations of the payoff before maturity [5]; an approximation of the above technique using path splitting for the final timestep [1]; the use of a hybrid combination of pathwise sensitivity and the Likelihood Ratio Method [4]. We discuss the strengths and weaknesses of these alternatives in different multilevel Monte Carlo settings.
Automated test-case generation by cloning

Test cases are often similar. A preliminary study of eight open-source projects found that on average at least 8% of all test cases are clones; the maximum found was 42 %. The clones are not identical with their originals - identifiers of classes, methods, attributes and sometimes even order of statements and assertions differ. But the test cases reuse testing logic and are needed for testing. They serve a purpose and cannot be eliminated. We present an approach that generates useful test clones automatically, thereby eliminating some of the “grunt” work of testing. An important advantage over existing automated test case generators is that the clones include the test oracle. Hence, a human decision maker is often not needed to determine whether the output of a test is correct. The approach hinges on pairs of classes that provide analogous functionality, i.e., functions that are tested with the same logic. TestCloner transcribes tests involving analogous functions from one class to the other. Programmers merely need to indicate which methods are analogs. Automatic detection of analogs is currently under investigation. Preliminary results indicate a significant reduction in the number of “boilerplate” tests that need to be written by hand. The transcribed tests do detect defects and can provide hints about missing functionality.
Intranasal Flu Vaccine Protective against Seasonal and H5N1 Avian Influenza Infections

Background Influenza A (flu) virus causes significant morbidity and mortality worldwide, and current vaccines require annual updating to protect against the rapidly arising antigenic variations due to antigenic shift and drift. In fact, current subunit or split flu vaccines rely exclusively on antibody responses for protection and do not induce cytotoxic T (Tc) cell responses, which are broadly cross-reactive between virus strains. We have previously reported that γ-ray inactivated flu virus can induce cross-reactive Tc cell responses. Methodology/Principal Finding Here, we report that intranasal administration of purified γ-ray inactivated human influenza A virus preparations (γ-Flu) effectively induces heterotypic and cross-protective immunity. A single intranasal administration of γ-A/PR8[H1N1] protects mice against lethal H5N1 and other heterotypic infections. Conclusions/Significance Intranasal γ-Flu represents a unique approach for a cross-protective vaccine against both seasonal as well as possible future pandemic influenza A virus infections.
Active Implicit Surface for Animation

This paper introduces a new model of deformable surfaces designed for animation, which we call active implicit surfaces. The underlying idea is to animate a potential field defined by discrete values stored in a grid, rather than directly animating a surface. This surface, defined as an iso-potential of the field, has the ability to follow a given object using a snake-like strategy. Surface tension and other characteristics such as constant surface area or constant volume may be added to this model. The implicit formulation allows the surface to easily experience topology changes during simulation. We present an optimized implementation: computations are restricted to a close neighborhood around the surface. Applications range from the coating of deformablematerials simulated by particle systems (the surface hides the granularity of the underlying model) to the generation of metamorphosis between shapes that may not have the same topology.
Coordination number prediction using learning classifier systems: performance and interpretability

The prediction of the coordination number (CN) of an amino acid in a protein structure has recently received renewed attention. In a recent paper, Kinjo et al. proposed a real-valued definition of CN and a criterion to map it onto a finite set of classes, in order to predict it using classification approaches. The literature reports several kinds of input information used for CN prediction. The aim of this paper is to assess the performance of a state-of-the-art learning method, Learning Classifier Systems (LCS) on this CN definition, with various degrees of precision, based on several combinations of input attributes. Moreover, we will compare the LCS performance to other well-known learning techniques. Our experiments are also intended to determinethe minimum set of input information needed to achieve good predictive performance, so as to generate competent yet simple and interpretable classification rules. Thus, the generated predictors (rule sets) are analyzed for their interpretability.
Stability in Competition? Hotelling in Continuous Time

We study Hotelling’s classic duopoly location model in continuous time with flow payoffs accumulated over time and the price dimension made explicit. In an experimental setting, subjects chose price and location in treatments varying only by the speed of adjustment. We find that the principle of minimum differentiation generally holds, with little distance between subjects’ location decisions. Price decisions, however, tend to be volatile, which is arguably consistent with theory. Our data also support recent literature that the ability to respond quickly increases cooperation. JEL: C72, C92, D21, D43
Clinical and radiographic features of pycnodysostosis: A case report

Pycnodysostosis is a rare disorder that was first described in 1962; however, it was only in 1996 that the defective gene was discovered, which led to a better understanding of this disease. This study reports and discuss a case of pycnodysostosis. In addition, a search of articles published in PubMed-Medline was performed. The case was a 13-year-old girl who was referred to a private clinic for dental treatment. Clinical examination showed midface hypoplasia, prominent cheeks, a high nasal bridge, beaked nose, spoon-shaped fingers, frontal bossing, open fontanelles and dental alterations, findings compatible with pycnodysostosis. Patients with this disease also suffer from fractures because of bone hardness with almost no elasticity, a fact that requires special care particularly in the case of children and adolescents. The diagnosis of pycnodysostosis is made based on clinical and radiographic findings. Clinicians should be aware of this disorder to provide adequate dental treatment. Key words:Pycnodysostosis, developmental bone disease, imaging diagnosis.
Accelerating Distributed Computing Applications Using a Network Offloading Framework

During the last two decades, a considerable amount of academic research has been conducted in the field of distributed computing. Typically, distributed applications require frequent network communication, which becomes a dominate factor in the overall runtime overhead. The recent proliferation of programmable peripheral devices for computer systems may be utilized in order to improve the performance of such applications. Offloading application-specific network functions to peripheral devices can improve performance and reduce host CPU utilization. Due to the peculiarities of each particular device and the difficulty of programming an outboard CPU, the need for an abstracted offloading framework is apparent. This paper proposes a novel offloading framework, called HYDRA that enables utilization of such devices. The framework enables an application developer to design the offloading aspects of the application by specifying an "offloading layout", which is enforced by the runtime during application deployment. The performance of a variety of distributed algorithms can be significantly improved by utilizing such a framework. We demonstrate this claim by evaluating several offloaded applications: a distributed total message ordering algorithm and a packet generator.
Mining of Web Server Logs using Extended Apriori Algorithm

Association rule mining is one of the most significant techniques in the field of data mining. It is very useful in discovering relationships hidden in large transaction datasets such as frequent patterns, associations etc. One of the popular and important algorithms in this category is Apriori algorithm which finds frequent itemsets using an iterative approach. But it suffers from a major limitation that in case of large databases, it requires a large number of passes while searching the frequent itemsets, thus increasing its scanning time. In order to lessen this time, an improved version of Apriori algorithm, called Extended Apriori is proposed in this paper which decreases the number of transactions in the database, hence reducing size of the database so as to minimize the scanning time. This extended algorithm is then used to mine web server logs of an educational web site in order to discover frequently visited pages by the user and also its performance is compared graphically with existing Apriori algorithm. In the end, the paper also outlines some future research directions in the area of web server log mining.
deepschema.org: An Ontology for Typing Entities in the Web of Data

Discovering the appropriate type of an entity in the Web of Data is still considered an open challenge, given the complexity of the many tasks it entails. Among them, the most notable is the definition of a generic and cross-domain ontology. While the ontologies proposed in the past function mostly as schemata for knowledge bases of different sizes, an ontology for entity typing requires a rich, accurate and easily-traversable type hierarchy. Likewise, it is desirable that the hierarchy contains thousands of nodes and multiple levels, contrary to what a manually curated ontology can offer. Such level of detail is required to describe all the possible environments in which an entity exists in. Furthermore, the generation of the ontology must follow an automated fashion, combining the most widely used data sources and following the speed of the Web. In this paper we propose deepschema.org, the first ontology that combines two well-known ontological resources, Wikidata and schema.org, to obtain a highly-accurate, generic type ontology which is at the same time a first-class citizen in the Web of Data. We describe the automated procedure we used for extracting a class hierarchy from Wikidata and analyze the main characteristics of this hierarchy. We also provide a novel technique for integrating the extracted hierarchy with schema.org, which exploits external dictionary corpora and is based on word embeddings. Finally, we present a crowdsourcing evaluation which showcases the three main aspects of our ontology, namely the accuracy, the traversability and the genericity. The outcome of this paper is published under the portal: http://deepschema.github. io.
Low-Loss Paper Substrate for Printed High Efficiency Antennas at 2.45 GHz

In this letter, we present an innovative solution to achieve high efficiency printed antennas made on paper substrate. A corrugated cardboard is introduced for the first time for RF circuits. This substrate has been characterized with small perturbation method. It presents a dielectric constant of 1.41 and loss tangent of 0.042. Subsequently, patch antennas were realized on corrugated cardboard and classical paper substrate. A measured gain of 5.12 dBi is reached with corrugated cardboard compared to -3.24 dBi for classical paper. Simulation results show a good agreement with measurement.
3D MHD modelling of low current–high voltage dc plasma torch under restrike mode

We present in this paper a magnetohydrodynamic (MHD) modelling of the gliding arc behaviour of a dc plasma torch operating with air under low current and high voltage conditions. The low current leads to instabilities and difficulties with simulating the process because the magnetic field is not sufficient to constrict the arc. The model is 3D, time dependent and the MHD equations are solved using CFD software Code_Saturne®. Although the arc is definitively non-local thermodynamic equilibrium (LTE), the LTE assumption is considered as a first approach. The injection of air is tangential. A hot gas channel reattachment model has been used to simulate the restriking process of the arc root. After the description of the model, the most appropriate electrical voltage breakdown parameter has been selected in comparing with experimental results. A typical operating point is then studied in detail and shows the helical shape of the arc discharge in the nozzle. Finally, the mass flow rate and the current have been varied in the range 0.16–0.5 g s−1 and 100–300 mA, respectively, corresponding to typical glidarc operating points of our experimental plasma torch. The model shows good consistency with experimental data in terms of global behaviour, arc length, mean voltage and glidarc frequency.
Efficient trajectory optimization using a sparse model

The “timed elastic band” approach optimizes robot trajectories by subsequent modification of an initial trajectory generated by a global planner. The objectives considered in the trajectory optimization include but are not limited to the overall path length, trajectory execution time, separation from obstacles, passing through intermediate way points and compliance with the robots dynamic, kinematic and geometric constraints. “Timed elastic bands” explicitly consider spatial-temporal aspects of the motion in terms of dynamic constraints such as limited robot velocities and accelerations. The trajectory planning operates in real time such that “timed elastic bands” cope with dynamic obstacles and motion constraints. The “timed elastic band problem” is formulated as a scalarized multi-objective optimization problem. Most objectives are local and relate to only a small subset of parameters as they only depend on a few consecutive robot states. This local structure results in a sparse system matrix, which allows the utilization of fast and efficient optimization techniques such as the open-source framework “g2o” for solving “timed elastic band” problems. The “g2o” sparse system solvers have been successfully applied to VSLAM problems. This contribution describes the application and adaptation of the g2o-framework in the context of trajectory modification with the “timed elastic band”. Results from simulations and experiments with a real robot demonstrate that the implementation is robust and computationally efficient.
Need for Symmetry: Addressing Privacy Risks in Online Social Networks

Private attributes of Online Social Network (OSN) users can be inferred from other information (which is usually from users' friends and group information). To address this, social networking sites allow users to hide their friend lists and group lists, so that general public cannot see them. However, if a user doesn't make his friend list public, but his friends have public friend list where we can find him, we can do reverse lookup to extend the friend lists of the user. Furthermore, many social networks allow non-group members to list the members of public groups (e.g., Face book). These are strong violations of OSN users' privacy, and can be considered as privacy risks caused by the asymmetric configuration of settings in OSNs. In this paper we present the privacy risks due to the lack of symmetric configurations, which exist in most of the OSNs. To make our idea more clear, we propose a inference attack and show that it can be used to infer users' private information, even users already made their friend list private. We theoretically analyze the risk of proposed privacy issues, and evaluate the risk using experiments based on real-world OSN data. We show that it is not sufficient to only disable friend list and group list to guarantee privacy, and propose methods to mitigate these privacy issues.
On-line diagnosis and reconfiguration of FPGA systems

Fault tolerance is becoming an important issue for the effective use of FPGA-based architectures in mission-critical applications. This paper introduces an innovative approach to design FPGA systems with on-line diagnosis and reconfiguration, at a limited cost in terms of FPGA redundant resources and interconnections. The technique is based on high-level synthesis of the self-checking datapath to be mapped on the FPGA. The analysis of the computation flow allows for location of the necessary checking points. Scheduling is performed in order to minimize the circuit complexity, while satisfying the maximum latency allowed by the application. Allocation is performed as a suited trade-off between the circuit complexity and the reconfiguration efficiency. Problems and constraints due to re-use of units in different points of the computation are taken into account. The faulty block replacement policy is discussed, together with its implication in terms of re-use and of interconnection re-routing.
Map-based exploration of intrinsic shape differences and variability

We develop a novel formulation for the notion of shape differences, aimed at providing detailed information about the location and nature of the differences or distortions between the two shapes being compared. Our difference operator, derived from a shape map, is much more informative than just a scalar global shape similarity score, rendering it useful in a variety of applications where more refined shape comparisons are necessary. The approach is intrinsic and is based on a linear algebraic framework, allowing the use of many common linear algebra tools (e.g, SVD, PCA) for studying a matrix representation of the operator. Remarkably, the formulation allows us not only to localize shape differences on the shapes involved, but also to compare shape differences across pairs of shapes, and to analyze the variability in entire shape collections based on the differences between the shapes. Moreover, while we use a map or correspondence to define each shape difference, consistent correspondences between the shapes are not necessary for comparing shape differences, although they can be exploited if available. We give a number of applications of shape differences, including parameterizing the intrinsic variability in a shape collection, exploring shape collections using local variability at different scales, performing shape analogies, and aligning shape collections.
Use of neural networks as decision makers in strategic situations

Intelligence consists of the ability to make right decisions in a given situation in order to achieve a certain goal. Game Theory provides mathematical models of real-world situations for studying intelligent behavior. Most of time, effective decision-making in strategic situations (such as competitive situations) requires nonlinear mapping between stimulus and response. This sort of mapping can be provided by Artificial Neural Networks. This paper describes the use of a human-like Artificial Neural Network to find the optimal strategy in strategic situations without injecting expert knowledge. In order to train such a Neural Network, an unsupervised reinforcement-learning rule using Back-Propagation is introduced. Unlike most of reinforcement learning systems, this learning rule can operate with continuous outputs, what makes it worth for a lot of different applications. Finally, this decision maker is used to find the optimal strategy in the well-known Iterated Prisoner's Dilemma, in order to demonstrate that this human-like Artificial Neural Networks can be used to design machines that are also capable of intelligent behavior.
Efficient and Scalable Compiler-Directed Energy Optimization for Realtime Applications

We present a compilation technique that targets realtime applications running on embedded processors with combined dynamic voltage scaling (DVS) and adaptive body biasing (ABB) capabilities. Considering the delay and energy penalty of switching between operating modes of the processor, our compiler judiciously inserts mode switch instructions in selected locations of the code and generates executable binary that is guaranteed to meet the deadline constraint. More importantly, our algorithm runs very fast and comes reasonably close to the theoretical limit of energy optimization using DVS+ABB. At 65 nm technology, we improve the energy dissipation of the generated code by an average of11.4% under deadline constraints. While our technique's improvement in energy dissipation over conventional DVS is marginal (3%) at 130nm, the average improvement continues to grow to 4.7%, 8.8% and 15.4% for 90nm, 65nm and 45nm technology nodes, respectively. Compared to a recent ILP-based competitor, we improve the runtime by more than three orders of magnitude, while producing improved results
Predictive control for tight group formation of Multi-Agent Systems

Abstract This paper addresses the real-time control problem of a group of agents in the presence of non-convex collision avoidance constraints. The goal is to guarantee the convergence towards a tight formation. Convex (polyhedral) regions will be used to define safety regions around each agent. Then, using the dynamics and constraints, an optimization-based control design can be adopted upon an appropriate receding horizon principle. A single optimal control problem is solved based on a prediction of the future evolution of the system and the resulting control law is implemented in a centralized way. At the supervision level, it is shown that the decision about which agents should take on what role in the desired tight formation is equivalent with a classical pairing (or task assignment) problem. An important contribution is the re-evaluation of the pairing at each iteration. Copyright © IFAC 2011
Fringe Visibility Estimators for the Palomar Testbed Interferometer

Visibility estimators and their performance are presented for use with the Palomar Testbed Interferometer (PTI). One operational mode of PTI is single‐baseline visibility measurement using path length modulation with synchronous readout by a NICMOS‐3 infrared array. Visibility is estimated from the fringe quadratures, either incoherently, or using source phase referencing to provide a longer coherent integration time. The visibility estimators differ those used with photon‐counting detectors in order to account for biases attributable to detector offsets and read noise. The performance of these estimators is affected not only by photon noise, but also by the detector read noise and errors in estimating the bias corrections, which affect the incoherent and coherent estimators differently. Corrections for visibility loss in the coherent estimators using the measured tracking jitter are also presented.