Nonparametric estimation of mark's distribution of an exponential Shot-noise process

In this paper, we consider a nonlinear inverse problem occurring in nuclear science. Gamma rays randomly hit a semiconductor detector which produces an impulse response of electric current. Because the sampling period of the measured current is larger than the mean inter arrival time of photons, the impulse responses associated to different gamma rays can overlap: this phenomenon is known as pileup. In this work, it is assumed that the impulse response is an exponentially decaying function. We propose a novel method to infer the distribution of gamma photon energies from the indirect measurements obtained from the detector. This technique is based on a formula linking the characteristic function of the photon density to a function involving the characteristic function and its derivative of the observations. We establish that our estimator converges to the mark density in uniform norm at a logarithmic rate. A limited Monte-Carlo experiment is provided to support our findings.Comment: Electronic Journal of Statistics, Institute of Mathematical Statistics and Bernoulli Society, 201

Nash sovremennik 1981-1991: a case study in the politics of Soviet literature with special reference to Russian nationalism

This study of the Moscow-based, Russian-language 'thick' journal, Nash sovremennik, with special reference to Russian nationalism, in the last decade of the Soviet polity (1981-1991), is based on a distinction between popular and statist Russian nationalist tendencies. In the conditions of an 'imperial state', such as the Soviet Union, it is argued, nationalist ideology exhibited a strong polarisation between a 'popular' tendency, oriented towards the idea of the nation; and a 'statist' tendency, oriented towards the state. The exigencies of Soviet politics meant that both popular and statist nationalist tendencies appeared in the journal in 'truncated' form: the popular nationalist tendency lacked an idea of statehood appropriate to its vision of the nation; and the statist tendency was inhibited from advocating a policy of thorough-going cultural Russification, appropriate to its views of the state. In the Gorbachev period, while Westernisng policies tended to make nationalists of both types oppose reform, the issue of the state was fundamental in determining the conservative political orientation of nationalists. There are five conclusions of the study, with regard to the period 1981-1991: 1 Nash sovremennik played an important role in the articulation of Russian nationalist ideology; 2 the publication policy of Nash sovremennik was strongly influenced by the appointments to the key internal posts, not only of chief editor, but also of deputy chief editor; 3 conservative political elites in the Soviet Union sought to use nationalist ideology to control and limit reform; 4 Russian nationalist ideology was characterised by a marked polarity between statist and popular tendencies; 5 the 'imperial' nature of the Soviet state, and the ethnic heterogeneity of Soviet elites and masses alike, made Russian nationalist ideology unsuitable, as an ideological instrument, for Soviet political elites

Tree Stochastic Processes

Stochastic processes play a vital role in understanding the development of many natural and computational systems over time. In this thesis, we will study two settings where stochastic processes on trees play a significant role. The first setting is in the reconstruction of evolutionary trees from biological sequence data. Most previous work done in this area has assumed that different positions in a sequence evolve independently. This independence however is a strong assumption that has been shown to possibly cause inaccuracies in the reconstructed trees \cite{schoniger1994stochastic,tillier1995neighbor}. In our work, we provide a first step toward realizing the effects of dependency in such situations by creating a model in which two positions may evolve dependently. For two characters with transition matrices $M_1$ and $M_2$, their joint transition matrix is the tensor product $M_1 \otimes M_2$. Our dependence model modifies the joint transition matrix by adding an `error matrix,\u27 a matrix with rows summing to 0. We show when such dependence can be detected. The second setting concerns computing in the presence of faults. In pushing the limits of computing hardware, there is tradeoff between the reliability of components and their cost (e.g. \cite{kadric2014energy}). We first examine a method of identifying faulty gates in a read-once formula when our access is limited to providing an input and reading its output. We show that determining \emph{whether} a fault exists can always be done, and that locating these faults can be done efficiently as long as the read-once formula satisfies a certain balance condition. Finally for a fixed topology, we provide a dynamic program which allows us to optimize how to allocate resources to individual gates so as to optimize the reliability of the whole system under a known input product distribution

Missing Data Problems

Missing data problems are often best tackled by taking into consideration specificities of the data structure and data generating process. In this doctoral dissertation, I present a thorough study of two specific problems. The first problem is one of regression analysis with misaligned data; that is, when the geographic location of the dependent variable and that of some independent variable do not coincide. The misaligned independent variable is rainfall, and it can be successfully modeled as a Gaussian random field, which makes identification possible. In the second problem, the missing independent variable a categorical. In that case, I am able to train a machine learning algorithm which predicts the missing variable. A common theme throughout is the tension between efficiency and robustness. Both missing data problems studied herein arise from the merging of separate sources of data.Economic

Babel' in Context

Isaak Babel (1894–1940) is arguably one of the greatest modern short story writers of the early twentieth century. Yet his life and work are shrouded in the mystery of who Babel was—an Odessa Jew who wrote in Russian, who came from one of the most vibrant centers of east European Jewish culture and all his life loved Yiddish and the stories of Sholom Aleichem.This is the first book in English to study the intertextuality of Babel’s work. It looks at Babel’s cultural identity as a case study in the contradictions and tensions of literary influence, personal loyalties, and ideological constraint. The complex and often ambivalent relations between the two cultures inevitably raise controversial issues that touch on the reception of Babel and other Jewish intellectuals in Russian literature, as well as the “Jewishness” of their work

Real-time statistical simulation of dynamic laser speckle

Lasers have several applications in the industry, such as cutting, engraving, and drilling. A specific use of lasers is taking distance measurements, by shining beams of light at objects and observing the hit points with an infrared camera. However, the depth measurements are inevitably inaccurate. This is mainly due to the manufacturing and design errors in beam splitters and dynamic speckle. The goal of this thesis is not to reduce the contribution of these effects, but to introduce them into a computer simulation, to make the virtual model as close as possible to reality. Studying the works of J. W. Goodman, Donald D. Duncan, and others in the field of Fourier Optics gave a solid theoretical foundation of the beam splitter and dynamic speckle. To tailor the general theory to this specific case, various physical experiments were carried out. Based on the theory and the experiment results, a way to extend an already existing physically based rendering engine was proposed. In conclusion, this extension produces similar results in the simulation to what is observable in real life. This is achieved with a small computational overhead on a modern graphics processor. Due to these properties, the technique can be used for more robust testing of depth estimation and reconstruction algorithms. Moreover, it also raises the quality of machine learning data that can be collected in large volumes from a computer simulation of this setup, leading to better downstream performance