Une estimation précise de la magnitude est primordiale pour établir des catalogues de sismicité fiables. L’objectif de cette thèse est de développer une méthode d’estimation de la magnitude de moment à partir de la coda des ondes crustales applicable sur les sismogrammes numériques et analogiques. Afin d’étudier les propriétés de la coda en France, nous avons réalisé une analyse fréquentielle et régionale des enveloppes de coda. Nous avons pu estimer le moment sismique M0 et la magnitude de moment Mw pour des sismogrammes numériques, mais pas pour des enregistrements sur papier pour lesquels le filtrage n’est pas possible. Nous avons donc observé les propriétés de la coda dans le domaine temporel. Nous avons déterminé un modèle empirique afin de représenter les enveloppes de coda du signal brut et obtenir une magnitude de coda Mcoda. A partir de la relation linéaire Mcoda/Mw, nous avons déterminé la magnitude de moment directement sur les enregistrements analogiques en France. / Accurate magnitude determination is necessary to establish reliable seismicity catalogs in order to assess seismic hazard. The main goal of this PhD is to develop a method for estimating moment magnitude Mw from coda waves applicable to new digital seismograms as well as to old paper records in France. In order to analyze coda waves properties in France, a study of the regional and frequency properties of coda-wave envelopes has been performed. From this spectral analysis of coda waves, we can estimate seismic moment M0 and moment magnitude Mw from digital seismograms but not from paper records for which no frequency filtering is possible. Therefore, in a second step, we have analyzed the coda-waves properties directly in the time domain. We develop an empirical model which fits the coda envelopes of the raw signal and permits to obtain a coda magnitude Mcoda. From the Mcoda/Mw relationship, we determined the moment magnitude directly from the old paper records in France.

Chemical reactions involving small number of reacting molecules are noisy processes. They are simulated using stochastic simulation algorithms like the Gillespie SSA, which are valid when the reaction environment is well-mixed. This is not the case in reactions occuring on biological media like cell membranes, where alternative simulation methods have to be used to account for the crowded nature of the reacting environment. Ion channels, which are membrane proteins controlling the flow of ions into and out of the cell, offer excellent single molecule conditions to test stochastic simulation schemes in crowded biological media. Single molecule reactions are of great importance in determining the functions of biological molecules. Access to their experimental data have increased the scope of com-putational modeling of biological processes. Recently, single molecule experiments have revealed the non-Markovian nature of chemical reactions, due to a phenomenon called `dynamic disorder', which makes the rate constants a deterministic function of time or a random process. This happens when there are additional slow scale conformational transitions, giving the molecule a memory of its previous states. In a previous work, the hTREK1 two pore domain potassium channel was revealed to have long term memory in its kinetics, prompting alternate non-Markovian schemes to analyze its gating. Traditionally, ion channel gating is modeled as Markovian transitions between fixed states. In this work, we have used single channel data from hTREK1 ion channel and have provided a simple diffusion model for its gating. The main assumption of this model is that the ion channel diffuses through a continuum of states on its potential energy landscape, which is derived from the steady state probability distribution of ionic current recorded from patch clamp experiments. A stochastic differential equation (SDE) driven by Gaussian white noise is proposed to model this motion in an asymmetric double well potential. The method is computationally very simple and efficient and reproduces the amplitude histogram very well. For the case when ligands are added, leading to incorporation of long term memory in the kinetics, the SDE is modified to run on coloured noise. This has been done by introducing an auxiliary variable into the equation. It has been shown that increasing the noise correlation with ligand concentration improves the fits to the experimental data. This has been validated for several datasets. These methods are more advantageous for simulation than the Markovian models as they are true to the physical picture of gating and also computationally very efficient. Reproducing the whole raw data trace takes no more than a few seconds with our scheme, with the only input being the amplitude histogram and four parameters. Finally a quantitative model based on a modified version of the Chemical Langevin equation is given, which works on random rate parameters. This model is computationally simple to implement and reproduces the catalytic activity of the channel as a function of time. From the computational analysis undertaken in this work, we can infer that ion channel activity can be modeled using the framework of non-Markovian processes, lending credence to the recent understanding that single molecule reactions are basically processes with long-term memory. Since the ion channel is basically a protein, we can also hypothesize that the some of the properties that make proteins so vital to living organ-isms could be attributed to long-term memory in their folding kinetics, giving them the ability to sample specific regions of their conformation space, which are of interest to biological functions.

Les études neurophysiologiques de la prise de décision, traditionnellement ancrées dans des principes neuro-économiques, ont évoluées pour inclure une variété d’aires du cerveau. Partant d’abord du lobe frontal associé aux jugements de valeur, le champ s’est élargi pour inclure d’autres types de décisions incluant les décisions perceptuelles et les décisions incarnées qui impliquent notamment les aires sensorimotrices du cerveau. La théorie moderne de la prise de décision modèle l’activité neurale dans ces régions comme une compétition entre les différents stimuli et actions considérés par un individu. Cette compétition est résolue lorsque l’activité neurale associée à un stimulus ou une action choisie atteint un seuil critique. Toutefois, il reste à éclaircir comment ce modèle s’applique aux décisions effectuées alors que l’individu est déjà engagé dans une activité. Dans ce mémoire nous examinons ce type de décision chez des sujets humains dans une tâche de suivi continu. Des cibles « choix » apparaissaient sur un écran pendant que le sujet suivait de la main une cible qui se déplaçait doucement en continu. Le sujet pouvait ignorer ces cibles choix, ou abandonner la cible suivie pour toucher une cible choix, dans quel cas la cible sélectionnée devenait la nouvelle cible à suivre du doigt. Tel qu’attendu, nous avons observé que les sujets favorisaient les cibles plus proches, plus grandes, et les cibles alignées avec l’axe du mouvement. Toutefois nous avons été surpris de constater que les sujets ignoraient les coûts énergétiques du mouvement, tel que modélisés. Un biais pour minimiser les coûts du mouvement fut réintroduis lorsque la tâche fut divisée en séries de mouvements point-à-point, plutôt qu’un mouvement continu. Même si nous ne pouvons expliquer ce résultat surprenant, nous espérons qu’il inspire de futures études utilisant le paradigme expérimental de décision durant l’action. / Neurophysiological studies of decision-making have expanded over decades to involve many brain areas. The field broadened from neuroeconomics, mainly concerned with frontal regions, to perceptual or embodied decision-making involving several sensorimotor areas where neural activity is linked to the stimuli and actions necessary for the decision process. Current models of decision-making envision this neural activity as a competition between different actions that is resolved when enough activity favors one over the other. However, it is unclear how such models can explain decisions often present in natural behavior, where deliberation takes place while already engaged in an action. In this thesis, we examined the choices human subjects made as they were engaged in a continuous tracking task. While they were manually tracking a target on a flat screen, subjects were occasionally presented with a new target to which they could freely choose to switch, whereupon it became the new tracked target. As expected, we found that subjects were more likely to move to closer targets, bigger targets, or targets that were aligned to the direction of movement. However, we were surprised that subjects did not choose targets that minimized energetic cost, as calculated by a biomechanical model of the arm. A biomechanical bias was restored when the continuous movement was broken up into a series of point to point movements. While we cannot yet explain these findings with certainty, we hope they will inspire further studies using decide-while-acting paradigms.

<p dir="ltr">Both diet and physical activity are known to be risk factors for obesity and chronic diseases such as diabetes and metabolic syndrome. We explore a distance-based approach for clustering daily physical activity time series to find temporal physical activity patterns among U.S. adults (ages 20-65). We further extend this approach to integrate both diet and physical activity, and find joint temporal diet and physical activity patterns. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint patterns with association to health. </p><p dir="ltr">Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. We propose a deep learning architecture called Synthesis Model Bank (SMB) to deal with illumination variation in unsupervised person re-ID. From our experiments, the proposed SMB outperforms other synthesis methods on several re-ID benchmarks. </p><p dir="ltr">Recent technology advancement introduced modern high-throughput methodologies such as Unmanned Aerial Vehicles (UAVs) to replace the traditional, labor-intensive phenotyping. For many UAV phenotyping analysis, the first step is to extract the smallest groups of plants called “plots” that have the same genotype. We propose an optimization-based, rotation-adaptive approach for extracting plots in a UAV RGB orthomosaic image. From our experiments, the proposed method achieves better plot extraction accuracy compared to existing approaches, and does not require training data.</p>

No description available.

In the study of perceptual decision making, it has been widely assumed that random fluctuations of motion stimuli are irrelevant for a participant’s choice. Recently, evidence was presented that these random fluctuations have a measurable effect on the relationship between neuronal and behavioral variability, the so-called choice probability. Here, we test, in a behavioral experiment, whether stochastic motion stimuli influence the choices of human participants. Our results show that for specific stochastic motion stimuli, participants indeed make biased choices, where the bias is consistent over participants. Using a computational model, we show that this consistent choice bias is caused by subtle motion information contained in the motion noise. We discuss the implications of this finding for future studies of perceptual decision making. Specifically, we suggest that future experiments should be complemented with a stimulus-informed modeling approach to control for the effects of apparent decision evidence in random stimuli.

為了改進Black-Scholes模式的實證現象，許多其他的模型被建議有leptokurtic特性以及波動度聚集的現象。然而對於其他的模型分析的處理依然是一個問題。在本論文中，我們建議使用馬可夫跳躍擴散過程，不僅能整合leptokurtic與波動度微笑特性，而且能產生波動度聚集的與長記憶的現象。然後，我們應用Lucas的一般均衡架構計算選擇權價格，提供均衡下當跳躍的大小服從一些特別的分配時則選擇權價格的解析解。特別地，考慮當跳躍的大小服從兩個情況，破產與lognormal分配。當馬可夫跳躍擴散模型的馬可夫鏈有兩個狀態時，稱為轉換跳躍擴散模型，當跳躍的大小服從lognormal分配我們得到選擇權公式。使用轉換跳躍擴散模型選擇權公式，我們給定一些參數下研究公式的數值極限分析以及敏感度分析。 / To improve the empirical performance of the Black-Scholes model, many alternative models have been proposed to address the leptokurtic feature of the asset return distribution, and the effects of volatility clustering phenomenon. However, analytical tractability remains a problem for most of the alternative models. In this dissertation, we propose a Markov jump diffusion model, that can not only incorporate both the leptokurtic feature and volatility smile, but also present the economic features of volatility clustering and long memory. Next, we apply Lucas's general equilibrium framework to evaluate option price, and to provide analytical solutions of the equilibrium price for European call options when the jump size follows some specific distributions. In particular, two cases are considered, the defaultable one and the lognormal distribution. When the underlying Markov chain of the Markov jump diffusion model has two states, the so-called switch jump diffusion model, we write an explicit analytic formula under the jump size has a lognormal distribution. Numerical approximations of the option prices as well as sensitivity analysis are also given.

新商品上市後造成流行的機率低於25%（Cooper & Kleinschmidt, 1990；Cooper, 2011），但Rogers（唐錦超，民95）的創新擴散模型實驗卻提出若早期採用者接受新商品並分享他們的創意使用價值，則能影響早期消費大眾較快且容易接受新商品且在市場上誘發流行。因此本研究的動機是希望透過資訊技術，首先確認早期採用者存在於網路族群之中，再萃取被網際網路大量資料包圍的網路早期採用者與其在新商品的創意使用價值資料，希望能夠藉此影響早期消費大眾的接受意願，並跨越他們之間的鴻溝(Moore, 1991)。 本研究目的是探討即使網路早期採用者與早期消費大眾兩者之間無實體的社會關係網絡時，網路早期採用者所創造的新商品創意使用價值，還是能夠影響早期大眾消費者購買的意願，以形成一種新型創新擴散模式。實作上是將網路早期採用者的創意使用價值，設計成廣告宣傳單直接刺激早期消費大眾，觀察新商品是否被他們接受。經過購買意願問卷回收、整理和分析消費者族群與接受程度，證實網路早期採用者的創意使用價值確實能引起早期消費大眾往高購買意願的族群移動。 再者，企業為了能夠在市場生存競爭獲勝，必須比其他同業更早挖掘出稀少且有價值的資訊幫助新商品跨越鴻溝，協助企業獲得更多的利益。雖然本研究提出的新型創新擴散模式，能提高早期消費大眾接受新商品的意願，但是在人機互動的資料分析過程中，耗費過多人力。因此本研究再根據文字探勘的IDF概念提出ICF演算法，藉以縮小決策範圍並且得到效率前緣的機會點，依然能在眾多的資料中也萃取出網路早期採用者的微弱創意使用價值，同時降低人工作業、專家主觀解讀和決策制定的複雜度的問題，得到高品質的效率方案。 / The chance for a new product to be prevalent is lower than 25% (Cooper & Kleinschmidt, 1990; Cooper, 2011). However, in Rogers’ innovative diffusion model (Rogers, 2003), he proposed that if early adopters accepted new products and shared their innovative use value with others, the early majority may accept the new products quickly and readily. First of all, this study intends to use information technology to make sure that the early adopters exist on the Internet world. Then the author tries to extract innovative use information employed by the early adopters who are surrounded by huge amount of information. Hopefully, the early majority can be affected by such measures. That is, in the process of consuming merchandise, consumers tend to build mutual affections, value, and psychological sensation. This may enable the new products to cross over the chasm between the early adopters and early majority (Moore, 1991; 1995). The purpose of this study is to explore the innovative use value created by early adopters existing on the Internet world. It is to affect the purchasing intention of the early majority when there is no social relationship network between early adopters and early majority so as to build a new innovative diffusion model. In order to achieve this, the innovative use value created in the Internet by the early adopters was designed as commercial fliers to directly stimulate the early majority. It is used to observe whether new products are accepted by the early majority. The experimental results proved that this measurement surely can move the early majority toward groups with high intention. Moreover, talking about the continuing existence of a business, one needs to find out the rare and valuable information in the early stage in order to make the new products to cross over the chasm. The new innovative diffusion proposed by this study is able to extract the innovative use value created by the early adopters in Internet. And, for the purpose of reducing manpower and contracting the weak innovative information created by the early adopters, this study was conducted by using ICF algorithm which is derived from IDF concept existing in the text mining. It is to narrow the range of policy making, gain the chance point from the effect frontier, improve the quality and efficiency when making a decision, and reduce subjective judgement made by experts, which may help decision makers to make an effective policy based on their advantage and preference, and in the end, to reduce the complexity of making a policy.

La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche. / Decision‐making is a computational process of fundamental importance to many aspects of animal behavior. The prevailing model in the experimental study of decision‐making is the drift‐diffusion model, which has a long history and accounts for a broad range of behavioral and neurophysiological data. However, an alternative model – called the urgency‐gating model – has been offered which can account equally well for much of the same data in a more parsimonious and theoretically‐sound manner. In what follows, we will first trace the origins and development of the DDM, as well as give a brief overview of the manner in which it has supplied an explanatory framework for a large number of behavioral and physiological studies in the domain of decision‐making. In so doing, we will attempt to build a strong and clear case for its strengths so that it can be fairly and rigorously compared to potential alternative models. We will then re‐examine a number of the implicit and explicit theoretical assumptions made by the drift‐diffusion model, as well as highlight some of its empirical shortcomings. This analysis will serve as the contextual backdrop for our introduction and discussion of the urgency‐gating model. Finally, we present a novel experiment, the methodological design of which uniquely affords a decisive empirical dissociation of the models, the results of which illustrate the empirical and theoretical shortcomings of the drift‐diffusion model and instead offer clear support for the urgency‐gating model. We finish by discussing the potential for the urgency gating model to shed light on a number of clinical disorders, highlighting a number of future directions for research.

現今市場競爭愈來愈激烈，迫使廠商紛紛至海外尋求產品消費市場，在跨國銷售的背景之下，需要有更多可以確定國家選擇、預測銷售及估計需求的方法。而其中可以滿足這些需求的方法之中，就是研究產品跨國擴散型態，藉以瞭解後進國家與領先國家中新產品如何擴散且會如何互相影響 (Douglas and Craig, 1992)。 在眾多的跨國產品中，本研究選擇好萊塢電影做為實證分析的對象。 經由集群分析，本研究發現（一）台灣高首週票房且口碑佳的電影，會遇到假日人潮、有很高的美國總票房、以及很高的美國首週票房；（二）美國影片在美國及台灣映演的每週票房趨勢有差異存在；（三）片商沒有做好影片在台灣映演的檔期歸劃；（四）三群電影中，在影片類型沒有明顯地區別。 經由十二個新產品銷售預測模型的建立：對數線性迴歸模式（LN-Regression Model）（不考慮新產品領先國擴散經驗）（以OLS估計）、卜瓦松迴歸模式(Poisson Regression Model) （不考慮新產品領先國擴散經驗）（以MLE估計）、負二項分配迴歸模式(Negative Binomial Distribution Regression Model) （不考慮新產品領先國擴散經驗）（以MLE估計）、Exponential Decay模式（以OLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、Exponential Decay模式（以OLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）、Exponential Decay模式＋層級貝氏迴歸模式（考慮新產品領先國擴散經驗）、Bass連續型擴散模式（以NLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗（以SUR估計）、Bass連續型擴散模式（以NLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗（以SUR估計）、Bass離散型擴散模式（以OLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、Bass離散型擴散模式（以OLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）、層級貝氏BASS離散型擴散模式＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、層級貝氏BASS離散型擴散模式＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）。本研究發現：（一）在考慮影響後進國的新產品擴散速度時，領先國的擴散經驗為絕對必要的考慮因子；（二）必須使用Bass連續型擴散模式做為建構新產品銷售預測模型的基礎；（三）必須使用Bass連續型擴散模式的NLS估計法估計Bass模型的創新係數p、模仿係數q及市場潛量m。