Dualities and dual pairs in Heyting algebras

We extract the abstract core of finite homomorphism dualities using the techniques of Heyting algebras and (combinatorial) categories.Comment: 17 pages; v2: minor correction

Alien Registration- Tardif, Guy A. (Van Buren, Aroostook County)

https://digitalmaine.com/alien_docs/32385/thumbnail.jp

Classification, caractérisation et facteurs de variabilité spatiale des régimes hydrologiques naturels au Québec (Canada). Approche éco-géographique

Nous proposons onze nouvelles variables pour classifier, caractériser et analyser les facteurs de variabilité spatiale des régimes hydrologiques des affluents du fleuve Saint-Laurent au Québec. Ces variables se rapportent exclusivement aux débits mensuels et utilisent quatre (volume d’écoulement, période d’occurrence, durée et amplitude de variabilité intra-annuelle des débits) des cinq critères proposés par Richter et al. (1996) pour caractériser écologiquement les régimes hydrologiques.L’analyse en composantes principales de ces onze variables hydrologiques a permis d’extraire trois composantes principales significatives après rotation d’axes par la méthode varimax. La première composante principale est associée aux débits saisonniers hivernaux et aux mois d’occurrence des débits mensuels maximums et minimums. La seconde composante est associée aux débits saisonniers printaniers et au rapport entre ces débits et les débits estivaux. Enfin, la dernière composante est associée au coefficient d’immodération (rapport entre les débits mensuels maximums et minimums) et aux débits mensuels minimums. La variance totale expliquée par ces trois composantes, à part presqu’égale, est d’environ 83%. Sur la base des signes de notes factorielles sur les trois composantes principales, les 72 rivières analysées ont été groupées en huit régimes hydrologiques naturels non contigus dans l’espace. Les caractéristiques de chaque régime hydrologique ont été clairement définies.Quant aux facteurs environnementaux qui influencent la variabilité spatiale des régimes hydrologiques, il est apparu que les six variables hydrologiques associées aux trois composantes principales sont principalement influencées par la température de l’air ainsi que la superficie couverte par les forêts, les lacs et les marais.Several classifications of hydrologic regimes have already been proposed in Quebec. However, these classifications are exclusively based upon the magnitude of discharge (annual and monthly discharge, annual maximum and minimum discharge). This hydrologic parameter isn’t sufficient to describe the ecological hydrologic regime. Thus, Richter et al. (1996) suggested five fundamental characteristics to describe hydrologic regimes that regulate ecological processes in river ecosystems :1. The magnitude of the water condition at any given time. It is a measure of the availability or suitability of a habitat. It defines such habitat attributes as wetted area or habitat volume, or the position of the water table relative to wetland or riparian plant rooting zones.2. The timing of occurrence of particular water conditions can determine whether certain life-cycle requirements can influence the degree of stress or mortality associated with extreme water conditions such as flood or droughts.3. The frequency of occurrence of specific water conditions such as droughts or floods may be tied to reproduction or mortality events for various species, thereby influencing population dynamics.4. The duration of time over which a particular life-cycle phase can be completed or the degree to which stressful effects such as inundation or drought can accumulate.5. The rate of change (range) in water conditions may be related to the stranding of certain organisms along the water’s edge, in ponded depressions, or the ability of plant roots to maintain contact with phreatic water supplies.The application of these characteristics requires a daily discharge time series, but these data are not always available. To overcome this difficulty, we propose eleven new hydrological variables exclusively based upon monthly discharge data. These new variables describe four (magnitude, timing of occurrence, duration of time and the rate of change) of the five characteristics of hydrologic regimes suggested by Richter et al. (1996). The eleven new variables are as follows: seasonal discharge coefficients (%); monthly maximum and minimum discharge coefficients (%); median Julian day of occurrence of maximum monthly discharge; median Julian day of occurrence of monthly minimum discharge; spring and winter seasonal discharge ratios; spring and summer seasonal discharge ratios and monthly maximum and minimum discharge ratios.We have isolated, using principal component analysis (PCA), three significant principal components after varimax rotation. The first principal component was linked with the magnitude of winter discharge and the timing of monthly maximum and minimum discharge. The second principal component was associated with the magnitude of spring seasonal discharge and the spring and summer seasonal discharge ratio. The third component was linked with the coefficient of immoderation (monthly maximum/minimum discharge ratio) and the magnitude of monthly minimum discharge. The three principal components explain, almost weight for weight, about 83% of the total variance. On the basis of signs of loadings for these three components, 72 rivers were analysed and grouped into eight natural hydrologic regimes that are not geographically contiguous. For example, the first hydrologic regime was characterized by high winter discharge (> 12%), timing of monthly maximum discharge in April, high summer discharge (> 54%), high spring and summer seasonal discharge ratios (> 3.5), high monthly maximum and minimum discharge (> 12) and low monthly minimum discharge ( 0ºC. The correlation analysis revealed the following mean results:- The winter seasonal discharge was influenced by the forest surface area (negative correlation) and both annual and seasonal temperature (positive correlation).- The timing of the monthly maximum discharge was influenced by the length of rivers (positive correlation), the forest and lake surface area (positive correlation) and both annual and seasonal temperatures (negative correlation).- The spring seasonal discharge was influenced by the length of rivers (negative correlation), the mean basin slope (positive correlation), the forest surface area (positive correlation), the lake surface area (negative correlation), the annual precipitation (negative correlation) and the winter and summer seasonal temperature (negative correlation).- The spring and summer seasonal discharge ratio was negatively correlated with the drainage basin, the length of rivers, the mean basin drainage, the annual precipitation and the number of winter days with temperature > 0ºC, but was positively correlated with annual and seasonal temperature.- The monthly maximum and minimum discharge was positively correlated with forest surface area but negatively correlated with lake surface area, annual and seasonal temperature.- The monthly minimum discharge was negatively correlated with forest surface area but positively correlated with annual and seasonal discharge.From this correlation analysis, it appeared that temperature was the only factor that influenced the spatial variability of all hydrological variables, followed by forest and lake surface area. The influence of precipitation on this spatial variability was very weak

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

Gaps and dualities in Heyting categories

summary:We present an algebraic treatment of the correspondence of gaps and dualities in partial ordered classes induced by the morphism structures of certain categories which we call Heyting (such are for instance all cartesian closed categories, but there are other important examples). This allows to extend the results of [14] to a wide range of more general structures. Also, we introduce a notion of combined dualities and discuss the relation of their structure to that of the plain ones

Putting the “Noun Bias” in Context: A Comparison of English and Mandarin

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66101/1/1467-8624.00045.pd

Strain and correlation of self-organized Ge_(1-x)Mn_x nanocolumns embedded in Ge (001)

We report on the structural properties of Ge_(1-x)Mn_x layers grown by molecular beam epitaxy. In these layers, nanocolumns with a high Mn content are embedded in an almost-pure Ge matrix. We have used grazing-incidence X-ray scattering, atomic force and transmission electron microscopy to study the structural properties of the columns. We demonstrate how the elastic deformation of the matrix (as calculated using atomistic simulations) around the columns, as well as the average inter-column distance can account for the shape of the diffusion around Bragg peaks.Comment: 9 pages, 7 figure

Développement d’une nouvelle méthode de régionalisation basée sur le concept de « régime des débits naturels » : la méthode éco-géographique

Nous proposons une nouvelle méthode de régionalisation des débits fondée sur le concept de « régime des débits naturels » introduit en écologie aquatique : l’approche éco-géographique. Elle se distingue de deux approches de régionalisation existantes (approches hydrologique et écologique) sur les trois points suivants : le choix des variables hydrologiques, l’échelle d’analyse et la finalité de la régionalisation. En ce qui concerne le choix des variables hydrologiques, la nouvelle méthode est fondée sur le choix des caractéristiques des débits et non sur les variables hydrologiques. Ces caractéristiques des débits sont définies au moyen de l’analyse en composantes principales appliquée sur les variables hydrologiques. Contrairement aux autres approches, l’approche éco-géographique tient compte de toutes les caractéristiques des débits dans la régionalisation conformément au concept de « régime des débits naturels ». Quant à l’échelle d’analyse, à l’instar de l’approche écologique, la nouvelle méthode s’applique aussi à toutes les échelles d’analyse (annuelle, mensuelle et journalière) mais en les considérant séparément afin de tenir compte de toutes les caractéristiques de débits dans la régionalisation. Enfin, la finalité de la nouvelle méthode est de pouvoir déterminer les facteurs de variabilité spatiale des caractéristiques de débits (et non des variables hydrologiques) au moyen de l’analyse canonique des corrélations, notamment afin d’assurer une gestion durable des ressources hydriques dans un contexte de changement de l’environnement. Nous avons appliqué cette nouvelle méthode aux débits moyens annuels au Québec.Flow regionalization has been the subject of numerous hydrologic studies. However, despite the development of regionalization methods, there are still differences in the approaches used amongst hydrologists on the one hand, and between hydrologists and experts in other fields (aquatic ecology and physical geography) on the other hand. Those differences relate to five aspects of the regionalization process: the choice of hydrologic variables, station grouping methods to produce homogeneous hydrologic regions, the choice of appropriate statistical laws to estimate quantiles for non-gauged or partially-gauged sites, the scale of flow analysis, and the ultimate purpose of the regionalization exercise. Depending on the choice of hydrologic variables, the scale of analysis and their ultimate purpose, regionalization studies may thus be divided according to two distinct approaches: the hydrologic approach and the ecologic approach.The ultimate purpose of the hydrologic approach is to estimate flows at non-gauged or partially-gauged sites. For this reason, it has been primarily concerned with methods that allow the grouping of stations into homogeneous hydrologic regions and with the choice of statistical laws to estimate quantiles for non-gauged or partially-gauged sites. However, despite its undeniable interest from a practical point of view, this approach does not address the concerns of ecologists and geographers for three reasons: 1) the choice of hydrologic variables used for regionalization is not based on a scientific concept (this choice is arbitrary, and the variables selected do not constrain all the flow characteristics); 2) the ultimate purpose of the regionalization exercise is limited to estimating flows and is of limited interest to geographers and ecologists; 3) regionalization is performed at a daily scale, without taking into account other scales.To make up for these limitations, ecologists have recently proposed regionalization based on the “natural flow regime” concept (the ecologic approach), which allows all fundamental flow characteristics (magnitude, frequency, duration, timing of occurrence and variability) to be taken into account. The rationale for considering all flow characteristics is that each characteristic has an effect on the behaviour of river ecosystems. Hence, regionalization based on the ecologic approach relies on a large number of hydrologic variables that define the fundamental flow characteristics. Rather than being arbitrary, the choice of variable is based on this new paradigm. Regionalization using the ecologic approach considers all time scales, and its ultimate purpose is to account for differences in the structure and biological composition of aquatic ecosystems.However, one of the limitations of studies based on this approach is that, no matter how numerous they are, the variables used for regionalization do not constrain all flow characteristics, as required by the natural flow regime concept, so that application of this concept is incomplete. In addition, simultaneous analysis of all time scales does not allow consideration of all flow characteristics. To overcome these limitations, we propose a new regionalization approach based on the natural flow regime concept, an “ecogeographic” approach that differs from the ecologic approach in three ways. First, the proposed method is based on the use of flow characteristics rather than hydrologic variables. The reason for this is that there are an infinite number of hydrologic variables to define the five fundamental characteristics, making it impossible to account for all of them in the regionalization process. In contrast, since the number of fundamental flow characteristics is limited, they can all be taken into account, consistent with the “natural flow regime” requirements. Second, the ultimate purpose of the proposed regionalization method is to identify the physiographic and climatic factors that explain the spatial variability of these fundamental characteristics. To achieve this goal, it is necessary to analyze the different time scales (daily, monthly, annual) separately given the fact that it is impossible to constrain the effect of these various physiographic and climatic factors at all time scales. Indeed, some factors may show an effect at some time scales and not at others. This ultimate purpose addresses the concerns of geographers interested in explaining the spatial variability of such phenomena, among other things. Finally, separate analysis of the various time scales makes it possible to define all flow characteristics linked to a given time scale. As such, application of the “natural flow regime” concept to regionalization is complete.Application of the ecogeographical method involves four separate steps: 1) the definition of the flow characteristics for the hydrologic series of interest; 2) the determination of minor and major characteristics using principal component analysis, where a “major” flow characteristic is defined as one which meets the following criterion: TVE ≥ (100% / N), where N is the total number of characteristics that define the analyzed hydrologic series and TVE is the total variance explained; 3) the grouping of stations in homogeneous hydrologic regions based on factorial scores. Homogeneous hydrologic regions are divided in two types based on the presence or absence of stations: effective homogeneous regions contain stations whereas fictive homogenous regions do not; 4) the determination of the factors that affect the spatial variability of flow characteristics. This is achieved using canonical correlation analysis, an approach that we have applied to average annual flows in Quebec watersheds

Exchange bias in GeMn nanocolumns: the role of surface oxidation

We report on the exchange biasing of self-assembled ferromagnetic GeMn nanocolumns by GeMn-oxide caps. The x-ray absorption spectroscopy analysis of this surface oxide shows a multiplet fine structure that is typical of the Mn2+ valence state in MnO. A magnetization hysteresis shift |HE|~100 Oe and a coercivity enhancement of about 70 Oe have been obtained upon cooling (300-5 K) in a magnetic field as low as 0.25 T. This exchange bias is attributed to the interface coupling between the ferromagnetic nanocolumns and the antiferromagnetic MnO-like caps. The effect enhancement is achieved by depositing a MnO layer on the GeMn nanocolumns.Comment: 7 pages, 5 figure