Miracle-dz/UniFAR-FTUs huggingface dataset:
Halo stochasticity in global clustering analysis

Galaxy clustering and cosmic magnification can be used to estimate the dark matter power spectrum if the theoretical relation between the distribution of galaxies and the distribution of dark matter is precisely known. In the present work, we study the statistics of haloes, which in the halo model determines the distribution of galaxies. Haloes are known to be biased tracer of dark matter, and at large scales it is usually assumed there is no intrinsic stochasticity between the two field (i.e. r = 1). Following the work of Seljak & Warren, we explore how correct this assumption is and, moving a step further, we try to qualify the nature of stochasticity. We use principal component analysis applied to the outputs of a cosmological N-body simulation as a function of mass to: (i) explore the behaviour of stochasticity in the correlation between haloes of different masses; and (ii) explore the behaviour of stochasticity in the correlation between haloes and dark matter. We show results obtained using a catalogue with 2.1 million haloes, from a PMFAST simulation with box size of 1000 h ―1 Mpc and with about four billion particles. In the relation between different populations of haloes, we find that stochasticity is not negligible even at large scales. In agreement with the conclusions of Tegmark & Bromley, who studied the correlations of different galaxy populations, we found that the shot noise subtracted stochasticity is qualitatively different from 'enhanced' shot noise and, specifically, it is dominated by a single stochastic eigenvalue. We call this the 'minimally stochastic' scenario, as opposed to shot noise-like stochasticity which is 'maximally stochastic'. In the correlation between haloes and dark matter, we find that the stochasticity is minimized, as expected, near the dark matter peak (k ∼ 0.02 h Mpc ―1 for a A cold dark matter cosmology), and, even at large scales, it is of the order of 15 per cent above the shot noise. Moreover, we find that the reconstruction of the dark matter distribution is improved when we use the principal component eigenvectors as tracers of the bias, but still the reconstruction is not perfect, due to stochasticity.
A Silent Revolution in Mathematics

F ifty years ago there has been still a clear cut separation between pure and applied mathematics: pure mathematics had its home in the Olympus of academic sciences, whereas applied mathematics was left to the technical universities for teaching and research. Some time ago a colleague of mine from the department of mechanics of the Technical University of Vienna said partly amused partly offended: ‘‘Our people in the math department do pure mathematics, is it then dirty mathematics what I am doing? ’’ Mathematics has undergone a true silent revolution within the last few decades. The difference between pure and applied mathematics—thought to be fundamental in the past—has almost completely disappeared. Computer modeling in science is in widespread use and almost every university houses a department for computational science but this is only one usage of electronic computers in present day mathematics although a very powerful one. As most people are familiar with the achievements and problems of numerical modeling and computer simulations, we shall focus here more on other issues that are not so much commonplace.
Forerunners Of Modern Financial Economics: A Random Walk In The History Of Economic Thought

Preface 1. Introduction: Finance, Risk and Statistics 2. The Highs and Lows of Bayesian Economic Statistics 3. Irving Fisher and the Mathematics of Risk 4. Thorstein Veblen and Credit Risk 5. Risk and Diversification in the Boom of the 1920s 6. Evaluating Market Forecasts: Financial Economics in the 1930s 7. Statistics and the Theory of Value Investing 8. Finance in a Period of War, Debt, and Taxes 9. The Forerunners in Relation to Modern Financial Economics Bibliography Index
Preliminary and Incomplete Please Do Not Cite or Circulate Nothing for Something: Paying Twice for Medicare Drug Benefits

The Medicare Modernization Act of 2003 added a new outpatient prescription drug benefit to Medicare and increased payments to private plans to induce more plan participation in Medicare. We formalize the benefits from these two expansion paths and then estimate a statistical model of plan choice that enables us to quantify these benefits. We find that expansion of drug benefits produced nearly six times as much value per government dollar as expansion of plan choices. Approximately $43 billion will be spent over the next ten years to expand plan choices with little value to beneficiaries. We suggest that modified bidding procedures could reduce this spending.
Alewife Systems Memo #4 Special Mechanisms for Multi-model Support

A number of out-of-band mechanisms are being proposed for the rst-round implementation of ALEWIFE, a large-scale multiprocessor being designed at MIT. This document details these mechanisms. Note that this is merely a concept document. Not all of these mechanisms will necessarily be implemented in the ALEWIFE prototype.
Modeling of Emission Allowance Markets: A Literature Review

This paper reviews the development of emission trading models from the earliest to recent contributions. First, we introduce the economics of pollution control and the origins of emission trading. We give a brief description of policy instruments for the control of pollution, and explain why economic instruments (Pigouvian tax and emission trading) produce better results than “command-and-control” approaches. Second, we review several papers on modeling of emission trading systems, with a focus on dynamic models in case of perfect competition. We begin with the earliest static models, investigating a number of factor that can affect the effectiveness of emission trading (e.g. market power, transaction-costs, political pressures, etc). Next, we present dynamic models of permit markets, analysing questions such as banking/borrowing, relationship between spot and future markets, exogenous factors influencing the marginal abatement cost, etc. Finally, we end the paper with recent studies that model the main features of the European Emission Trading Scheme (EU ETS) in a dynamic framework with stochastic emissions.
Parallel divide‐and‐conquer scheme for 2D Delaunay triangulation

This work describes a parallel divide‐and‐conquer Delaunay triangulation scheme. This algorithm finds the affected zone, which covers the triangulation and may be modified when two sub‐block triangulations are merged. Finding the affected zone can reduce the amount of data required to be transmitted between processors. The time complexity of the divide‐and‐conquer scheme remains O(n log n), and the affected region can be located in O(n) time steps, where n denotes the number of points. The code was implemented with C, FORTRAN and MPI, making it portable to many computer systems. Experimental results on an IBM SP2 show that a parallel efficiency of 44–95% for general distributions can be attained on a 16‐node distributed memory system. Copyright © 2006 John Wiley & Sons, Ltd.
The Effect of Air Pollution Related Human Health Risks on Firm Financial Performance

Since the later half of the 1990’s there is a marked increase around the world in socially responsible investment and regulatory action to stimulate disclosure on the environmental, social and ethical performance of firms and pension fund managers. This study explores two keystones of this movement: that investment directed toward social goods, such as environmental quality, can offer financial rewards and improve the quality of life. Prior research finds that both goals are difficult to attain given the limited market power of socially concerned investors and the lack of clear information on the social impact of market externalities, such as the human health consequences of hazardous air pollution. We assess whether there is an association between public health impacts and financial returns, using estimates of toxic chemical cancer risk and particulate matter premature mortality per $1 million value-added associated with 1998 air emissions. Fifteen stock portfolios are constructed at varying levels of environmental performance to assess differences in portfolio returns for stocks traded on U.S. exchanges from 1998-2002. We apply a multifactor model of asset returns and a response surface algorithm used generally to evaluate company characteristics. We find that the level of air pollution related public health risk has a statistically significant negative association with stock returns after controlling for key variables that impact stock value.
The Effect of Alternative Interest Rate Processes on the Value of Mortgage-Backed Securities

Analytical valuation of mortgage-backed securities (MBS) requires a process for generating future interest rates to value the embedded prepayment option. While many single-factor interest rate models are available, no systematic comparison of their effects on MBS values has been published. This article compares valuation results using three such processes—a simple log-normal process; the square-root process of Cox, Ingersoll, and Ross (CIR); and the no-arbitrage process of Black, Derman, and Toy (BDT)—selected for their prominence in the literature and their differences in major characteristics. The study compares how well the models fit actual Treasury yield curves and their valuation of simulated MBS. The simple log-normal process is rejected for its inconsistent performance in fitting yield curves. The CIR and BDT processes value noncallable mortgagelike cash flows similarly, but the CIR process values the call option more and so has lower callable values for the MBS.
Posture and the Compositionof Plasma

Method 1. Method 2 is, however, faster and more convenient. The secondary advantage of either JET method is the decreased technician working time required for the extraction as compared to XAD-2, charcoal, or liquid-liquid extraction protocols. Although any time-study is subject to significant error, our subjective impression of greater technologist efficiency is reinforced by the observation that, after the change from XAD-2 extraction to the longer JET (Method 1) procedure, one technologist, in 6 h, could perform the work previously done by two technologists in 13 h. This more than compensates for the higher material cost of the JET tubes over the XAD-2 resin. Based on these factors, we now use the faster Method 2 for our large-volume routine urine drug-abuse assays and reserve Method 1 for those cases that require extraction at low or very high pH.
A Systems Engineering approach to Occupant Protection System design and optimization through modeling and simulation

Occupant Protection Systems (OPS) have become a very important part of today's automotive systems. The extensive government regulations and consumer information programs make the design and optimization of the OPS a complex and challenging task. A Systems Engineering (SE) approach is implemented throughout the OPS design and optimization process. The SE process includes customer requirements analysis, system concept design, system design, analysis and optimization, and system verification and validation. To fully evaluate the performance of the OPS and ensure its compliance of government regulations, the system needs to be tested in a large number of crash scenarios. Using physical tests alone to develop the OPS is cost‐prohibitive. Therefore, computer modeling and simulation are utilized as one of the primary tools in the process. A frontal impact OPS design and optimization example is presented in the paper to demonstrate the process. © 2004 Wiley Periodicals, Inc. Syst Eng 8: 51–61, 2005
Integrating Color Vector Quantization and Curvelet Transform for Image Retrieval

Since most of image databases today are pooly indexed or annotated, there is a great need for developing automated, content-based image retrieval (CBIR) systems to help users to get images they want. The focus of our research is mining image features which can represent image in the way of human visual perception. Our image retrieval approach depends on the extracted color and shape features. Vector Quantization (VQ) can provide a way of better exploiting the spatial information to generate different color histograms than scalar color quantization, thus VQ is employed in this work to extract color pattern of images. The shape feature of images is extracted by curvelet transform, as it has been proved that the curvelet transform is an almost optimal sparse representation of objects with edges. The extracted color and shape features are combined and weighted by using Genetic Algorithm (GA), then used for image similarity measurement. Experimental results show that the GA combined features can bring about good retrieval precision and speed simultaneously.
A novel electrolytic capacitor-less multi-input DC/DC converter with soft-switching capability for hybrid renewable energy system

A new multi-input DC/DC converter with soft switching capability and does not require any electrolytic capacitors is proposed in this paper for hybrid renewable energy systems. The proposed converter is applicable to power individual homes that are on or off the grid. There are several features in the proposed converter. First of all, soft-switching capability in the switches is achieved by utilizing an input tapped series inductor and a parallel capacitor across the switch. Secondly, by eliminating the electrolytic capacitors that would be required in the conventional topologies, the reliability of the electronic interface in the renewable energy system is greatly enhanced. The detailed description of the proposed multi-input converter and the circuit's operating principles are discussed in this paper. Results are given to highlight the merits of this circuit through a 1.2kW system.
Software Development as Music Education Research

This paper discusses how software development can be used as a method for music education research. It explains how software development can externalize ideas, stimulate action and reflection, and provide evidence to support the educative value of new software-based experiences. Parallels between the interactive software development process and established research methods are drawn, with particular focus on action research, case study, and activity theory. A new approach to arts educational research called Software Development as Research (SoDaR) is proposed. The paper includes examples from the author’s use of this approach when developing the jam2jam software to facilitate networked music improvisation experiences for young children.
On detection of median filtering in digital images

In digital image forensics, it is generally accepted that intentional manipulations of the image content are most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. However, it is also beneficial to know as much as possible about the general processing history of an image, including content-preserving operations, since they can affect the reliability of forensic methods in various ways. In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method is backed with experimental evidence on a large image database.
Doubly Robust Policy Evaluation and Optimization

We study sequential decision making in environments where rewards are only partially observed, but can be modeled as a function of observed contexts and the chosen action by the decision maker. This setting, known as contextual bandits, encompasses a wide variety of applications such as health care, content recommendation and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions and received rewards. The key challenge is that the past data typically does not faithfully represent proportions of actions taken by a new policy. Previous approaches rely either on models of rewards or models of the past policy. The former are plagued by a large bias whereas the latter have a large variance. In this work, we leverage the strengths and overcome the weaknesses of the two approaches by applying the doubly robust estimation technique to the problems of policy evaluation and optimization. We prove that this approach yields accurate value estimates when we have either a good (but not necessarily consistent) model of rewards or a good (but not necessarily consistent) model of past policy. Extensive empirical comparison demonstrates that the doubly robust estimation uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies. As such, we expect the doubly robust approach to become common practice in policy evaluation and optimization.
Breast cancer risk in relation to different types of hormone replacement therapy in the E3N‐EPIC cohort

Most epidemiological studies have shown an increase in breast cancer risk related to hormone replacement therapy (HRT) use. A recent large cohort study showed effects of similar magnitude for different types of progestogens and for different routes of administration of estrogens evaluated. Further investigation of these issues is of importance. We assessed the risk of breast cancer associated with HRT use in 54,548 postmenopausal women who had never taken any HRT 1 year before entering the E3N‐EPIC cohort study (mean age at inclusion: 52.8 years); 948 primary invasive breast cancers were diagnosed during follow‐up (mean duration: 5.8 years). Data were analyzed using multivariate Cox proportional hazards models. In this cohort where the mean duration of HRT use was 2.8 years, an increased risk in HRT users compared to nonusers was found (relative risk (RR) 1.2 [95% confidence interval 1.1–1.4]). The RR was 1.1 [0.8–1.6] for estrogens used alone and 1.3 [1.1–1.5] when used in combination with oral progestogens. The risk was significantly greater (p <0.001) with HRT containing synthetic progestins than with HRT containing micronized progesterone, the RRs being 1.4 [1.2–1.7] and 0.9 [0.7–1.2], respectively. When combined with synthetic progestins, both oral and transdermal/percutaneous estrogens use were associated with a significantly increased risk; for transdermal/percutaneous estrogens, this was the case even when exposure was less than 2 years. Our results suggest that, when combined with synthetic progestins, even short‐term use of estrogens may increase breast cancer risk. Micronized progesterone may be preferred to synthetic progestins in short‐term HRT. This finding needs further investigation. © 2004 Wiley‐Liss, Inc.
Interactive multi-image blending for data visualisation and interpretation

The ability to integrate data from a range of different images is often a crucial requirement for successful interpretation. Interactive multi-image blending is presented as a tool for facilitating the interpretation of complex information from multiple data sources. Traditionally, image blending has only been considered for cross-dissolving effects between two images. However, it is common for there to be more than just two images of interest in an interpretation task. We have developed a family of different multi-image blending tools to fill this need. These have been designed to support a number of different interpretation tasks and image types. For image blending to be a useful tool for multiple image interpretation it is important that the association between features and individual input images remain identifiable and distinct within the blend. We argue that interactivity of the blend is an important component for achieving this. Blending can also be usefully employed to interactively explore parameter variations for enhancement techniques. Often the best parameter values to use cannot be known beforehand, and it is common for different regions of an image to require different parameter values for best enhancement. By preparing a set of images processed over a sequence of scales and parameter values, and then interactively blending between these images, the interpretation of a data set can be greatly facilitated.
A system architecture for real time traffic monitoring in foggy video

In foggy video, the visibility and contrast of objects are decreased dramatically, which causes the performance degradation of traffic monitoring systems. In this paper, an architecture for real-time traffic monitoring system is presented for foggy video. For the real-time traffic monitoring, it is required to satisfy two major constraints. First, the quality of an image after fog removal is good enough for further processing such as object detection and tracking. Second, it has to be computationally cheap for real-time processing. In this paper, a parallel architecture is proposed, consisting of N threads, for a real-time traffic monitoring system. The proposed parallel architecture shows the significant reduction of processing time for the development of real-time traffic monitoring systems.
Online teaching of humanoid robots

In this paper we present an on-line course on programming humanoids robots integrated in the Robotic Programming Network (RPN). Our aim is to create an open course which allows students (from high school age onwards) to access real humanoid robots and program them in a realistic human environment, learning how to use them as extensions of their own bodies to solve problems. This course can give a bigger number of students the opportunity to learn how to program humanoid robots, solve real problems and enter into the complex world of remote interaction, telerobotics and telepresence. In the course the students can advance at their own velocity, outside the traditional teacher talks-students listen environment. For the course we adopted a problem solving organization, which have demonstrated its advantages when teaching STEM. They are presented a series of real problems of increasing difficulty that they have to solve through programming.
Using the Wavelet Transform for Multivariate Data Analysis and Time Series Forecasting

We discuss the use of orthogonal wavelet transforms in multivariate data analysis methods such as clustering and dimensionality reduction. Wavelet transforms allow us to introduce multiresolution approximation, and multiscale nonparametric regression or smoothing, in a natural and integrated way into the data analysis. Applications illustrate the powerfulness of this new perspective on data analysis.
An Alignable User Profile Ontology for Ambient Intelligence Environments

One of the principal objectives of today's ambient intelligence environments is to provide users with services according to their activity, i.e. preferences in order to accomplisha specific user goal. For this purpose, miscellaneous user information must be collected and structured into user profiles. These profiles offer the advantage of being easily enriched and exploitable by the environment, in order to deliver to the user, at any moment and at any place, the best fitted service, with regard to his activity. In this paper we present an ontology-based user profile model which provides the static data about a user but also the context of the user in a given situation in real time.