La région Nord-Pas-de-Calais figure parmi les régions françaises les plus concernées par les dépassements de valeurs limites journalières de concentrations de PM₁₀ (50 µg m-³). Sur le littoral, le niveau de fond atmosphérique particulaire demeure parfois élevé, bien que relativement éloigné des sources principales de particules que sont le trafic routier et l'industrie. Alors que de nombreuses études ont été réalisées sur les émissions en milieu industrialo-portuaire, il ressort un manque de connaissances concernant l'impact des émissions issues du secteur maritime, qu'il s'agisse d'apports naturels (sels marins) ou anthropiques (trafic maritime). Dans ce travail, deux campagnes de mesures ont été menées : en 2013 au Cap Gris-Nez et au premier trimestre 2014, simultanément au Cap Gris-Nez et dans le port de Calais. La concentration en PM₁₀ a été suivie et la composition chimique (métaux, ions hydrosolubles, EC, OC, traceurs organiques) en a été déterminée. Sur le site du Cap Gris-Nez en 2013, l'évolution des niveaux de PM₁₀ est similaire à celle observée en région, reflétant la fluctuation du fond atmosphérique. Les espèces majoritairement sont NO₃-, OC, SO₄²-, CI-, Na⁺ et NH₄⁺ et représentent 69% de la masse de PM₁₀. La proportion de ces espèces varie selon la saison et les conditions météorologiques (température, vitesse et direction du vent). Les situations de fortes teneurs de PM₁₀ sont caractérisées par une plus grande proportion de nitrate d'ammonium. Les données recueillies sur le site de Calais ont permis de montrer que les émissions du trafic maritime ont pour effet d'augmenter le nombre de particules ultrafines dans l'atmosphère. Sous cette influence, les concentrations en NOx et SO₂ apparaissent plus élevées, ainsi que celles des espèces V, Ni et Co qui peuvent être proposées comme traceurs du trafic maritime. L'utilisation de la factorisation matricielle nous a permis d'identifier 10 sources de particules et d'en estimer les contributions. Ainsi, en moyenne en 2013 au Cap Gris-Nez, 41% des PM₁₀ sont issus des aérosols inorganiques secondaires, 37% des sels marins et 10% de la combustion de biomasse. Pour cette dernière, la contribution peut atteindre 17% en hiver. Enfin, le trafic maritime (5%) contribue davantage à la concentration de PM₁₀ que le trafic routier (2%). / The Nord-Pas-de-Calais region is one of the most concerned areas in France by exceedance of the PM₁₀ mean daily limit value (50 µg m-³). The particulate atmospheric background level can also be high on the coastal zone, despite the absence of any urban and industrial sources at its vicinity. Numerous studies have been performed regarding those sources, but there is still a lack of knowledge about the impact of emissions resulting from the marine compartment, including natural emissions (sea salts) and anthropogenic emissions (maritime traffic). Two measurement campaigns have been achieved, in 2013 at Cape Gris-Nez and in the first trimester 2014, simultaneously at Cape Gris-Nez and in the harbour of Calais. Concentrations of PM₁₀ were recorded and chemical composition was determined (metals, water soluble ions, Ec, OC, organic tracers). In 2013, the evolutions of PM₁₀ levels at Cape Gris-Nez and in the region similar, reflecting the atmospheric background fluctuation. NO₃-, OC, SO₄²-, CI-, Na⁺ and NH₄⁺ were found as the major species and correspond to 69% of PM₁₀ mass. The proportion of these species evolves depending on the season and the meteorological conditions (temperature, wind speed and direction). High PM₁₀ concentration situations are characterized by high proportion of ammonium nitrate. Data collected in Calais show that maritime traffic emissions increase the number of ultrafine particles in the atmosphere. Under this influence, NOx and SO₂ concentrations are higher, as those of V, Ni and Co, species that could be used as maritime traffic tracers. 10 sources were identified and apportioned by matrix factorization. In average, in 2013 at Cape Gris-Nez, 41% of PM₁₀ come from secondary inorganic aerosols, 37% from sea salts and 10% from biomass combustion. This last contribution can reach 17% in winter. Maritime traffic represents a higher contribution to PM₁₀ than road traffic, 5% against 2%.

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We utilize the exact factorization of the electron-nuclear wave function [Abedi et al., PRL 105 123002 (2010)] to illuminate several aspects of laser-driven molecular dynamics in intense femtosecond laser pulses. Above factorization allows for a splitting of the full molecular wave function and leads to a time-dependent Schrödinger equation for the nuclear subsystem alone which is exact in the sense that the absolute square of the corresponding, purely nuclear, wave function yields the exact nuclear N-body density of the full electron-nuclear system. As one remarkable feature, this factorization provides the exact classical force, the force which contains the highest amount of electron-nuclear correlations that can be retained in the quantum-classical limit of the electron-nuclear system. We re-evaluate the classical limit of the nuclear Schrödinger equation from the perspective of the exact factorization, and address the long-standing question of the validity of the popular quantum-classical surface hopping approach in laserdriven cases. In particular, our access to the exact classical force allows for an elaborate evaluation of the various and completely different potential energy surfaces frequently applied in surface hopping calculations. The highlight of this work consists in a generalization of the exact factorization and its application to the laser-driven molecular wave function in the Floquet picture, where the molecule and the laser form an united quantum system exhibiting its own Hilbert space. This particular factorization enables us to establish an analytic connection between the exact nuclear force and Floquet potential energy surfaces. Complementing above topics, we combine different well-known and proven methods to give a systematic study of molecular dissociation mechanisms for the complicated electric fields provided by modern attosecond laser technology.:Contents Introduction 1 The exact factorization of time-dependent wave functions 1.1 Concern and state of the art 1.2 The exact factorization of the electron-nuclear wave function 1.3 The generalized exact factorization 1.4 The exact factorization for coupled harmonic oscillators 1.5 The exact factorization for a single particle with spin 1.6 The exact factorization of the laser-driven electron-nuclear wave function in the Floquet picture 1.7 Summary and conclusion 2 Quantum-classical molecular dynamics from an exact factorization perspective 2.1 Concern and state of the art 2.2 The exact nuclear TDSE 2.3 The Wigner-Moyal equation for the nuclear TDSE and its classical limit 2.4 The Bohmian formulation of the nuclear TDSE and its classical limit 2.5 Comparative calculations 2.5.1 Scenario 1: stationary states 2.5.2 Scenario 2: laser-driven dynamics 2.6 Summary and conclusion 3 Surface hopping in laser-driven molecular dynamics 3.1 Concern and state of the art 3.2 Surface hopping 3.3 Quantum-classical dynamics on the EPES 3.4 The benchmark model and its potential energy surfaces 3.5 Surface hopping in laser-driven molecular dynamics 3.6 Summary and conclusion 4 Beyond the limit of the Floquet picture: molecular dissociation in few-cycle laser pulses 4.1 Concern and state of the art 4.2 Theoretical few-cycle pulses 4.3 Calculation of dissociation probabilities 4.4 Dissociation in few-cycle pulses 4.4.1 Dissociation in half-cycle pulses 4.4.2 Dissociation in few-cycle pulses 4.5 Dissociation in realistic attosecond pulses 4.6 Summary and conclusion Outlook Appendices A List of abbreviations B Numerical details C Calculating electronic observables within quantum-classical molecular dynamics D Ionization in few-cycle pulses E Modeling an optical attosecond pulse Bibliography

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In this dissertation, some new algorithms are developed for hyperspectral imaging analysis enhancement. Tensor data format is applied in hyperspectral dataset sparse and low-rank decomposition, which could enhance the classification and detection performance. And multi-view learning technique is applied in hyperspectral imaging clustering. Furthermore, kernel version of multi-view learning technique has been proposed, which could improve clustering performance. Most of low-rank and sparse decomposition algorithms are based on matrix data format for HSI analysis. As HSI contains high spectral dimensions, tensor based extended low-rank and sparse decomposition (TELRSD) is proposed in this dissertation for better performance of HSI classification with low-rank tensor part, and HSI detection with sparse tensor part. With this tensor based method, HSI is processed in 3D data format, and information between spectral bands and pixels maintain integrated during decomposition process. This proposed algorithm is compared with other state-of-art methods. And the experiment results show that TELRSD has the best performance among all those comparison algorithms. HSI clustering is an unsupervised task, which aims to group pixels into different groups without labeled information. Low-rank sparse subspace clustering (LRSSC) is the most popular algorithms for this clustering task. The spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC) algorithms is proposed in this dissertation, which extended LRSSC with multi-view learning technique. In this algorithm, spectral and spatial views are created to generate multi-view dataset of HSI, where spectral partition, morphological component analysis (MCA) and principle component analysis (PCA) are applied to create others views. Furthermore, kernel version of SSMLC (k-SSMLC) also has been investigated. The performance of SSMLC and k-SSMLC are compared with sparse subspace clustering (SSC), low-rank sparse subspace clustering (LRSSC), and spectral-spatial sparse subspace clustering (S4C). It has shown that SSMLC could improve the performance of LRSSC, and k-SSMLC has the best performance. The spectral clustering has been proved that it equivalent to non-negative matrix factorization (NMF) problem. In this case, NMF could be applied to the clustering problem. In order to include local and nonlinear features in data source, orthogonal NMF (ONMF), graph-regularized NMF (GNMF) and kernel NMF (k-NMF) has been proposed for better clustering performance. The non-linear orthogonal graph NMF combine both kernel, orthogonal and graph constraints in NMF (k-OGNMF), which push up the clustering performance further. In the HSI domain, kernel multi-view based orthogonal graph NMF (k-MOGNMF) is applied for subspace clustering, where k-OGNMF is extended with multi-view algorithm, and it has better performance and computation efficiency.

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We have measured the flavor dependence of multiplicities for $\\pi^+$ and $\\pi^-$ production in semi-inclusive deep-inelastic scattering (SIDIS) on proton and deuteron targets. We used a 10.6 GeV electron beam at Jefferson Lab, and 4 msr solid angle spectrometers (HMS for electrons, SHMS for pions), the lepton vertex spanned the kinematic range $0.3 < x < 0.6$, $2 < Q^2 < 5$ GeV$^2$, and $4 < W^2 < 11$ GeV$^2$. The pion fractional momentum range was $0.3 < z < 0.7$ and the small transverse momentum range was $0 < P_t < .25$ GeV. We used the multiplicities to form sum-and-difference ratios, testing the validity of factorization. We extracted two favored and two unfavored $W$ dependent fragmentation functions (FFs) from these multiplicities. Assuming factorization at low $P_t$, we find that the two ``favored" FFs allow for isospin breaking (charge symmetry violation) at low $W$, while converging to a common value at the highest $W$ of this experiment. The two unfavored FFs are consistent with each other within the experimental uncertainties.

This dissertation explores an analogue of the prime number theorem for polynomials over finite fields as well as its connection to the necklace factorization algorithm T-transform and the string complexity measure T-complexity. Specifically, a precise asymptotic expansion for the prime polynomial counting function is derived. The approximation given is more accurate than previous results in the literature while requiring very little computational effort. In this context asymptotic series expansions for Lerch transcendent, Eulerian polynomials, truncated polylogarithm, and polylogarithms of negative integer order are also provided. The expansion formulas developed are general and have applications in numerous areas other than the enumeration of prime polynomials. A bijection between the equivalence classes of aperiodic necklaces and monic prime polynomials is utilized to derive an asymptotic bound on the maximal T-complexity value of a string. Furthermore, the statistical behaviour of uniform random sequences that are factored via the T-transform are investigated, and an accurate probabilistic model for short necklace factors is presented. Finally, a T-complexity based conditional string complexity measure is proposed and used to define the normalized T-complexity distance that measures similarity between strings. The T-complexity distance is proven to not be a metric. However, the measure can be computed in linear time and space making it a suitable choice for large data sets. / Graduate / 0544 0984 0405 / nrebenich@gmail.com

Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.