Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative coefficients in two-factor option pricing models

The importance of positive coefficients in numerical schemes is frequently emphasized in the finance literature. This topic is explored in detail in this paper, in the particular context of two factor models. First, several two factor lattice type methods are derived using a finite difference/finite element methodology. Some of these methods have negative coefficients, but are nevertheless stable and consistent. Second, we outline the conditions under which finite volume/element methods applied to two factor option pricing partial differential equations give rise to discretizations with positive coefficients. Numerical experiments indicate that constructing a mesh which satisfies positive coefficient conditions may not only be unnecessary, but in some cases even detrimental. As well, it is shown that schemes with negative coefficients due to the discretization of the diffusion term satisfy approximate local maximum and minimum conditions as the mesh spacing approaches zero. This finding is of significance since, for arbitrary diffusion tensors, it may not be possible to construct a positive coefficient discretization for a given set of nodes. Acknowledgment: This work was supported by the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, and the Royal Bank of Canada. ∗A previous version of this paper was entitled “Diffusion Operators and Meshes in Option Pricing”. †rzvan@bear.com ‡paforsyt@elora.math.uwaterloo.ca §kvetzal@watarts.uwaterloo.ca
Area and volume calculation of necrotic tissue regions of heart using interpolation

This paper attempts to develop an improved tool, which would read two dimensional(2D) cardiac MRI images and compute areas and volume of the scar tissue. Here the computation would be done on the cardiac MR images to quantify the extent of damage inflicted by myocardial infarction on the cardiac muscle (myocardium) using Interpolation.
Distributed Wavelet Thresholding for Maximum Error Metrics

Modern data analytics involve simple and complex computations over enormous numbers of data records. The volume of data and the increasingly stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. For data reduction, wavelet decomposition has proved to be a very effective tool, as it can successfully approximate sharp discontinuities and provide accurate answers to queries. However, existing polynomial time wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical time and space complexities for large datasets. In order to provide a practical solution to the problem, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present i) a general framework for the parallelization of existing dynamic programming algorithms, ii) a parallel version of one such DP-based algorithm and iii) a new parallel greedy algorithm for the problem. To the best of our knowledge, this is the first attempt to scale algorithms for wavelet thresholding for maximum error metrics via a state-of-the-art distributed runtime. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts. Furthermore, our distributed greedy algorithm outperforms the distributed version of the current state-of-the-art dynamic programming algorithm by 2 to 4 times, without compromising the quality of results.
Regulation of membrane expansion at the nerve growth cone

Exocytotic incorporation of plasmalemmal precursor vesicles (PPVs) into the cell surface is necessary for neurite extension and is known to occur mainly at the growth cone. This report examines whether this is a regulated event controlled by growth factors. The Golgi complex and nascent PPVs of hippocampal neurons in culture were pulse-labeled with fluorescent ceramide. We studied the dynamics of labeled PPVs upon arrival at the axonal growth cone. In controls and cultures stimulated with brain-derived neurotrophic factor (BDNF), PPV clusters persisted in growth cones with a half-life (t1/2) of >14 minutes. Upon challenge with IGF-1, however, fluorescent elements cleared from the growth cones with a t1/2 of only 6 minutes. Plasmalemmal expansion was measured directly as externalization of membrane glycoconjugates in resealed growth cone particles (GCPs) isolated from fetal forebrain. These assays demonstrated that membrane expansion could be stimulated by IGF-1 in a dose-dependent manner but not by BDNF, even though intact, functional BDNF receptor was present on GCPs. Because both BDNF and IGF-1 are known to enhance neurite growth, but BDNF did not stimulate membrane expansion at the growth cone, we studied the effect of BDNF on the IGF-1 receptor. BDNF was found to cause the translocation of the growth-cone-specific IGF-1 receptor subunitβ gc to the distal axon, in a KIF2-dependent manner. We conclude that IGF-1 stimulates axonal assembly at the growth cone, and that this occurs via regulated exocytosis of PPVs. This mechanism is affected by BDNF only indirectly, by regulation of the βgc level at the growth cone.
The Slovene BNSI Broadcast News database and reference speech corpus GOS: Towards the uniform guidelines for future work

The aim of the paper is to search for common guidelines for the future development of speech databases for less resourced languages in order to make them the most useful for both main fields of their use, linguistic research and speech technologies. We compare two standards for creating speech databases, one followed when developing the Slovene speech database for automatic speech recognition ― BNSI Broadcast News, the other followed when developing the Slovene reference speech corpus GOS, and outline possible common guidelines for future work. We also present an add-on for the GOS corpus, which enables its usage for automatic speech recognition.
The DHS wealth index: Approaches for rural and urban areas

The DHS Wealth Index originally was constructed from existing data on household assets, services, and amenities in order to tabulate health, population, nutrition, education, and other indicators according to economic status. The Wealth Index has proved to be one of the most useful background characteristics available from the Demographic and Health Survey (DHS) data. It is now standard in DHS and UNICEF Multiple Indicator Cluster Survey (MICS) final reports and data sets. However, the Index has been criticized as being too urban in its construction and not able to distinguish the poorest of the poor from other poor households. This paper examines the extent of these problems and suggests and evaluates several possible remedies for them. One remedy that has already been taken by the DHS is to include questions in the standard questionnaires that have been specifically designed to ascertain rural stores of wealth and to distinguish among the poor. For example, questions have been added on rural stores of wealth, such as size of landholdings and number of farm animals by type. To better distinguish among the poor, the surveys ask about possession of furniture items, such as tables, chairs, and beds, because the extremely poor may not have such items. The lack of windows and the lack of windows with glass panes may also indicate an extremely poor household. A second remedy that could be applied is the use of urbanand rural-specific indexes, or quintile classifications of a common index. These approaches imply, respectively, the construction of separate indexes for urban and rural areas, or the calculation of wealth quintiles separately by type of area. A third approach would be to more finely divide the national index into deciles (which may distinguish better among the poor). These approaches are applied to data from the 2003 Bolivia DHS and to the 2007 Zambia DHS (in an appendix), and their advantages and drawbacks are discussed in the text. A fourth approach is to construct totally separate indexes for each area and then scale them so that a given score on each index means the same level of wealth. The paper describes two methods of combining separate rural and urban indexes. A method based on regression coefficients is demonstrated using data from the Bolivia 2003 and Zambia 2007 DHS surveys and proves the feasibility of basing urban and rural indexes on differing sets of indicator variables and then scaling these indicators so that a composite index can be calculated. This composite index allows comparability between urban and rural areas.
Wanderer between the Worlds - Self-Organized Network Stability in Attack and Random Failure Scenarios

Many real-world networks show a scale-free degree distribution, a structure that is known to be very stable in case of random failures. Unfortunately, the very same structure makes the network very sensitive against targeted attacks on their high-degree vertices. Under attack it is instead preferable to have a Poisson- or normal degree distribution. The paper addresses the question of whether it is possible to design a network protocol that enables the vertices of a decentralized network to switch its topology according to whether it is attacked or just suffers of random failures. We further require that this protocol is oblivious of the kind of removal scenario, i.e., that is does not contain any kind of attack detection. In this article we show how to design such a protocol that is oblivious, needs only local information, and that is capable of switching between the two structures reasonably fast. The protocol is easy to implement and keeps the average degree in the graph constant. After an analytical and empirical evaluation of the result the paper is concluded by possible applications to internet-based communication networks.
Ellipsoidal Relaxations of the Stable Set Problem: Theory and Algorithms

A new exact approach to the stable set problem is presented, which attempts to avoid the pitfalls of existing approaches based on linear and semidefinite programming. The method begins by constructing an ellipsoid that contains the stable set polytope and has the property that the upper bound obtained by optimizing over it is equal to the Lovasz theta number. This ellipsoid can then be used to construct useful convex relaxations of the stable set problem, which can be embedded within a branch-and-bound framework. Extensive computational results are given, which indicate the potential of the approach.
Quasi-Uniform Codes and Their Applications

Quasi-uniform random vectors have probability distributions that are uniform over their projections. They are of fundamental interest because a linear information inequality is valid if and only if it is satisfied by all quasi-uniform random vectors. In this paper, we investigate properties of codes induced by quasi-uniform random vectors. We prove that quasi-uniform codes (which include linear and almost affine codes as special cases) are distance-invariant and that Greene's Theorem and the Critical Theorem of Crapo and Rota hold in the setting of quasi-uniform codes. We show that both theorems are essentially combinatorial but not algebraical in nature. Linear programming bounds proposed by Delsarte are extended for quasi-uniform codes.
Navigating Care Transitions

As reforms push for improved integration across the care continuum, managers and policy makers are increasingly concerned about care transitions, such as during shift changes or when moving patients between units or institutions. The authors examined transitions from an emergency department to inpatient units through a 2-year ethnographic study of an academic medical center. Data include 48 semistructured interviews with doctors and administrators and 349 hr of observations of doctors. The authors show that organizational design poses challenges to doctors attempting between-unit care transitions, including heavy reliance on technology, separation of responsibility and control, and misalignment of routines and temporal rhythms. Each challenge threatened doctors’ awareness of the current state of other units and processes. To recover awareness, doctors engaged in time-consuming workarounds. Improved awareness will likely require a mix of interventions, including standardized protocols, work redesign, advanced information technologies specifically designed to enhance awareness, and high-reliability practices, such as safety organizing.
On the Broadcast Capacity of Wireless Networks

A fundamental problem in wireless networks is determining the broadcast capacity, i.e. the maximum data transfer rate from a given node to every other node in a relay network. This problem becomes more important as many network protocols rely on broadcast of certain control messages. In this paper, the scaling of the broadcast capacity with the number of nodes (N ) in the network is studied. In the highdensity regime (i.e. the node density goes to infinity; the network area is fixed), it is shown that the broadcast capacity is upper bounded by Θ(log N). Schemes are provided that achieve i) Θ(log N) throughput if the channel fading is spatially continuous; ii) Θ(log log N) throughput if the channel fading is spatially i.i. d. The reasons for this drastic reduction in throughput and the connections with multiuser diversity are discussed. Analogous results are provided for the extended-network model (i.e. the node density is fixed; the network area goes to infinity).
A RSS-based PU localization scheme using the Kalman filter

Cognitive radio sensor networks (CRSNs), is a fusion network combining wireless sensor network (WSN) with cognitive radio, undergo radio resource starvation under condition where it is densely deployed and primary user (PU) communications are concentrated. To overcome this problem, we propose a received signal strength (RSS)-based PU localization scheme using the Kalman filter to reduce noise portion of RSS from a PU. The experimental results shown in this paper prove the proposed scheme outperforms path loss exponent calibration scheme and a fixed path loss based scheme for the performance of average localization error.
Do Organizational Competencies Influence How Enterprise Systems Foster Organizational Agility?

Recent research indicates that higher ERP assimilation in organizations positively influences organizational agility, and that the dynamic capability of systems agility moderates the effects of ERP assimilation on agility. In this paper, we extend this research, to investigate the organizational competencies under which enterprise systems influence organizational agility. We propose a theoretical framework incorporating two specific organizational competencies – IT Competence in Business (ITCB) and Business Competence in IT (BCIT) – and theorize that both ITCB and BCIT have dual effects - mediating and moderating - on organizational agility. To empirically validate the theoretical framework, we conducted a quantitative cross-sectional survey across 215 organizations. The results validate our key theorizing on the dual effects of ITCB and BCIT on organizational agility. In addition, when organizations have high levels of ITCB and high ERP assimilation, we see surprising results in the negative moderating effects of ITCB on the influence of ERP assimilation on organizational agility.
Parallel Text Clustering Based on MapReduce

This paper analyzes challenges of ordinary text clustering algorithms and proposes cloud computing can be a feasible solution. The classical Jarvis-Patrick (JP) algorithm was adapted as a study case. It was implemented using MapReduce programming mode and was testified on the cloud computing platform-Hadoop with Sogou corpus provided by Sogou laboratory. The experiment results demonstrate that text clustering algorithm can be paralleled in MapReduce framework and parallel algorithm can handle massive textual data and get a better time performance.
A Transnational Black Feminist Framework: Rooting in Feminist Scholarship, Framing Contemporary Black Activism

Abstract:What is the role of feminist scholar-activists in contemporary Black freedom movements such as Black Lives Matter? This article proposes a Transnational Black Feminist framework as a theoretical complement to grassroots activism in Black communities. The proposed framework is rooted in Black feminist and transnational feminist traditions, and has as its core the guiding principles of intersectionality, scholar-activism, solidarity building, and attention to borders and boundaries.
User Engagement as Evaluation: a Ranking or a Regression Problem?

In this paper, we describe the approach used on the RecSys Challenge 2014 which focuses on employing user engagement as evaluation of recommendations. On one hand, we regard the challenge as a ranking problem and apply the LambdaMART algorithm, which is a listwise model specialized in a Learning To Rank approach. On the other hand, after noticing some specific characteristics of this challenge, we also consider it as a regression problem and use pointwise regression models such as Random Forests. We compare how these different methods can be modified or combined to improve the accuracy and robustness of our model and we draw the advantages or disadvantages of each approach.
Time and memory efficient viterbi decoding for LVCSR using a precompiled search network

In this paper, we present our recently developed time- synchronous speech recognition decoder, which adopts the idea of representing the search space of Large Vocabu- lary Continuous Speech Recognition (LVCSR) in a single precompiled network. In particular, we outline our ap- proaches for time and memory eÆcient Viterbi decoding in this scenario. This includes reducing the fast memory needs by keeping the search network on disk and only loading the required parts on demand. Evaluations are car- ried out on a diÆcult Japanese LVCSR task which involves a back-o(cid:11) trigram language model and full cross-word de- pendent triphone acoustic models. Time and memory ef-(cid:12)ciency enables the real-time Viterbi decoding of entire lecture speeches in a single time-synchronous pass with a search error of less than 1%.
Graphene–Gold Nanoparticles Hybrid—Synthesis, Functionalization, and Application in a Electrochemical and Surface-Enhanced Raman Scattering Biosensor

Graphene is a single-atom-thick two-dimensional carbon nanosheet with outstanding chemical, electrical, material, optical, and physical properties due to its large surface area, high electron mobility, thermal conductivity, and stability. These extraordinary features of graphene make it a key component for different applications in the biosensing and imaging arena. However, the use of graphene alone is correlated with certain limitations, such as irreversible self-agglomerations, less colloidal stability, poor reliability/repeatability, and non-specificity. The addition of gold nanostructures (AuNS) with graphene produces the graphene–AuNS hybrid nanocomposite which minimizes the limitations as well as providing additional synergistic properties, that is, higher effective surface area, catalytic activity, electrical conductivity, water solubility, and biocompatibility. This review focuses on the fundamental features of graphene, the multidimensional synthesis, and multipurpose applications of graphene–Au nanocomposites. The paper highlights the graphene–gold nanoparticle (AuNP) as the platform substrate for the fabrication of electrochemical and surface-enhanced Raman scattering (SERS)-based biosensors in diverse applications as well as SERS-directed bio-imaging, which is considered as an emerging sector for monitoring stem cell differentiation, and detection and treatment of cancer.
0 30 70 31 v 3 6 O ct 2 00 4 Equilibrium states for the Bose gas

The generating functional of the cyclic representation of the CCR (Canonical Commutation Relations) representation for the thermodynamic limit of the grand canonical ensemble of the free Bose gas with attractive boundary conditions is rigorously computed. We use it to study the condensate localization as a function of the homothety point for the thermodynamic limit using a sequence of growing convex containers. The Kac function is explicitly obtained proving non-equivalence of ensembles in the condensate region in spite of the condensate density being zero locally.
Bilateral Symmetry Detection for Real-time Robotics Applications

Bilateral symmetry is a salient visual feature of many man-made objects. In this paper, we present research that uses bilateral symmetry to identify, segment and track objects in real time using vision. Apart from the assumption of symmetry, the algorithms presented do not require any object models, such as color, shape or three-dimensional primitives. In order to counter the high computational cost of traditional symmetry detection methods, a novel computationally efficient algorithm is proposed. To investigate symmetry as an object feature, our fast detection scheme is applied to the tasks of object detection, segmentation and tracking. We find that objects with a line of symmetry can be segmented without relying on color or shape models by using a dynamic programming approach. Object tracking is achieved by estimating symmetry line parameters using a Kalman filter. The tracker operates at 40 frames per second on 640 x 480 video while running on a standard laptop PC. We use 10 difficult real-world tracking sequences to test our approach. We also quantitatively analyze symmetry as a tracking feature by comparing detected symmetry lines against ground truth. Color tracking is also performed to provide a qualitative comparison.
Improving controllability and predictability of an interactive user model driven search interface

In this extended abstract, we discuss user controllability and system predictability in the context of exploratory search. When a user is directing a search engine by interactively refining a probabilistic user model using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. By interpreting user’s actions as setting a goal for an optimization problem instead of passive relevance feedback, and by allowing the user to see the predicted effects of an action before committing to it, these problems can be reduced. A preliminary user study with an exploratory search interface indicates small improvements in user acceptance, perceived usefulness and task performance.
Design of a Metacrawler for web document retrieval

Web Crawlers `browse' the World Wide Web (WWW) on behalf of search engine, to collect web pages from numerous collections of billions of documents. Metacrawler is similar to that of a meta search engine that combines the top web search results from popular search engines. World Wide Web is growing rapidly. This possesses great challenges to general purpose crawlers. This paper introduces an architectural framework of a Metacrawler. This crawler enables the user to retrieve information that is relevant to the topic from more than one traditional web search engines. The crawler works in such a way that it fetches only the pages that are relevant to the topic. The PageRank algorithm is often used in ranking web pages. But, the ranking causes the problem of topic-drift. So, modified PageRank algorithm is used to rank the retrieved web pages in such a way that it reduces this problem. The clustering method is used to combine the search results so that the user can easily select web pages from the clustered results based upon the requirement. Experimental results show the effectiveness of the Metacrawler.