Roaring lions and chirruping lemurs: How the brain encodes sound objects in space.

The dual-stream model of auditory processing postulates separate processing streams for sound meaning and for sound location. The present review draws on evidence from human behavioral and activation studies as well as from lesion studies to argue for a position-linked representation of sound objects that is distinct both from the position-independent representation within the ventral/What stream and from the explicit sound localization processing within the dorsal/Where stream.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

The political ecology of weeds : a scalar approach to landscape transformations

How do plants that move and spread across landscapes become branded as weeds and thereby objects of contention and control? We outline a political ecology approach that builds on a Lefebvrian understanding of the production of space, identifying three scalar moments that make plants into 'weeds' in different spatial contexts and landscapes. The three moments are: the operational scale, which relates to empirical phenomena in nature and society; the observational scale, which defines formal concepts of these phenomena and their implicit or explicit 'biopower' across institutional and spatial categories; and the interpretive scale, which is communicated through stories and actions expressing human feelings or concerns regarding the phenomena and processes of socio-spatial change. Together, these three scalar moments interact to produce a political ecology of landscape transformation, where biophysical and socio-cultural processes of daily life encounter formal categories and modes of control as well as emotive and normative expectations in shaping landscapes. Using three exemplar 'weeds' - acacia, lantana and ambrosia - our political ecology approach to landscape transformations shows that weeds do not act alone and that invasives are not inherently bad organisms. Humans and weeds go together; plants take advantage of spaces and opportunities that we create. Human desires for preserving certain social values in landscapes in contradiction to actual transformations is often at the heart of definitions of and conflicts over weeds or invasives.

Facing Structural Disadvantage: The Role of Ingroup Connectedness

This thesis focuses on the social-psychological factors that help coping with structural disadvantage, and specifically on the role of cohesive ingroups and the sense of connectedness and efficacy they entail in this process. It aims to complement existing group-based models of coping that are grounded in a categorization perspective to groups and consequently focus exclusively on the large-scale categories made salient in intergroup contexts of comparisons. The dissertation accomplishes this aim through a reconsideration of between-persons relational interdependence as a sufficient and independent antecedent of a sense of groupness, and the benefits that a sense of group connectedness in one's direct environment, regardless of the categorical or relational basis of groupness, might have in the everyday struggles of disadvantaged group members. The three empirical papers aim to validate this approach, outlined in the first theoretical introduction, by testing derived hypotheses. They are based on data collected with youth populations (15-30) from three institutions in French-speaking Switzerland within the context of a larger project on youth transitions. Methods of data collection are paper-pencil questionnaires and in-depth interviews with a selected sub-sample of participants. The key argument of the first paper is that members of socially disadvantaged categories face higher barriers to their life project and that a general sense of connectedness, either based on categorical identities or other proximal groups and relations, mitigates the feeling of powerlessness associated with this experience. The second paper develops and tests a model that defines individual needs satisfaction as antecedent of self-group bonds and the efficacy beliefs derived from these intragroup bonds as the mechanism underlining the role of ingroups in coping. The third paper highlights the complexities that might be associated with the construction of a sense of groupness directly from intergroup comparisons and categorization-based disadvantage, and points out a more subtle understanding of the processes underling the emergence of groupness out of the situation of structural disadvantage. Overall, the findings confirm the central role of ingroups in coping with structural disadvantage and the importance of an understanding of groupness and its role that goes beyond the dominant focus on intergroup contexts and categorization processes.

Unidirectional quantum walks: Evolution and exit times

In this paper we focus our attention on a particle that follows a unidirectional quantum walk, an alternative version of the currently widespread discrete-time quantum walk on a line. Here the walker at each time step can either remain in place or move in a fixed direction, e.g., rightward or upward. While both formulations are essentially equivalent, the present approach leads us to consider discrete Fourier transforms, which eventually results in obtaining explicit expressions for the wave functions in terms of finite sums and allows the use of efficient algorithms based on the fast Fourier transform. The wave functions here obtained govern the probability of finding the particle at any given location but determine as well the exit-time probability of the walker from a fixed interval, which is also analyzed.

Identity, non-identity, and near-identity: Addressing the complexity of coreference

This article examines the mainstream categorical definition of coreference as "identity of reference." It argues that coreference is best handled when identity is treated as a continuum, ranging from full identity to non-identity, with room for near-identity relations to explain currently problematic cases. This middle ground is needed to account for those linguistic expressions in real text that stand in relations that are neither full coreference nor non-coreference, a situation that has led to contradictory treatment of cases in previous coreference annotation efforts. We discuss key issues for coreference such as conceptual categorization, individuation, criteria of identity, and the discourse model construct. We redefine coreference as a scalar relation between two (or more) linguistic expressions that refer to discourse entities considered to be at the same granularity level relevant to the linguistic and pragmatic context. We view coreference relations in terms of mental space theory and discuss a large number of real life examples that show near-identity at different degrees.

Validation of and comparison between a semidistributed rainfall-runoff hydrological model (PREVAH) and a spatially distributed snow-evolution model (SnowModel) for snow cover prediction in mountain ecosystems

Snow cover is an important control in mountain environments and a shift of the snow-free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT-HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long-lasting snow cover and evaluating whether they might survive under climate change.

Beyond contact-based transmission networks: the role of spatial coincidence.

Animal societies rely on interactions between group members to effectively communicate and coordinate their actions. To date, the transmission properties of interaction networks formed by direct physical contacts have been extensively studied for many animal societies and in all cases found to inhibit spreading. Such direct interactions do not, however, represent the only viable pathways. When spreading agents can persist in the environment, indirect transmission via 'same-place, different-time' spatial coincidences becomes possible. Previous studies have neglected these indirect pathways and their role in transmission. Here, we use rock ant colonies, a model social species whose flat nest geometry, coupled with individually tagged workers, allowed us to build temporally and spatially explicit interaction networks in which edges represent either direct physical contacts or indirect spatial coincidences. We show how the addition of indirect pathways allows the network to enhance or inhibit the spreading of different types of agent. This dual-functionality arises from an interplay between the interaction-strength distribution generated by the ants' movement and environmental decay characteristics of the spreading agent. These findings offer a general mechanism for understanding how interaction patterns might be tuned in animal societies to control the simultaneous transmission of harmful and beneficial agents.

On Short Exponential Sums Involving Fourier Coefficients of Holomorphic Cusp Forms

By an exponential sum of the Fourier coefficients of a holomorphic cusp form we mean the sum which is formed by first taking the Fourier series of the said form,then cutting the beginning and the tail away and considering the remaining sum on the real axis. For simplicity’s sake, typically the coefficients are normalized. However, this isn’t so important as the normalization can be done and removed simply by using partial summation. We improve the approximate functional equation for the exponential sums of the Fourier coefficients of the holomorphic cusp forms by giving an explicit upper bound for the error term appearing in the equation. The approximate functional equation is originally due to Jutila [9] and a crucial tool for transforming sums into shorter sums. This transformation changes the point of the real axis on which the sum is to be considered. We also improve known upper bounds for the size estimates of the exponential sums. For very short sums we do not obtain any better estimates than the very easy estimate obtained by multiplying the upper bound estimate for a Fourier coefficient (they are bounded by the divisor function as Deligne [2] showed) by the number of coefficients. This estimate is extremely rough as no possible cancellation is taken into account. However, with small sums, it is unclear whether there happens any remarkable amounts of cancellation.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Yhteistoiminnan ja tiedon jakamisen kehittäminen

Tässä tutkimustyössä on tavoitteena tutkia UPM-Kymmene Wood Oy:n vaneriliiketoimintaorganisaation lähtölogistiikka- ja myyntiosaston tiedon jakamisen ja yhteistoiminnan nykytilaa ja kartoittaa osastojen tiedontarpeita yhteistoimintaan liittyen. Työn tavoitteena on tämän jälkeen antaa kohdeosastoille ehdotuksia toimenpiteistä ja teknologisista ratkaisuista toiminnan parantamiseen. Työssä esitetään aluksi tietoon ja sen jakamiseen liittyviä käsitteitä. Tämän jälkeen tekstissä luodaan katsaus liiketoimintatietoon ja sen hallintaan. Lisäksi tutkimuksessa tarkastellaan tutkimuksen kohteena olevaa organisaatiota ja kohdeosastoja. Haastatteluiden ja havainnoinnin avulla kerätyn tutkimusaineiston perusteella kohdeosastojen välillä vaihdetaan nykytilassa paljon erilaista asiakkaisiin ja markkinoihin liittyvää tietoa. Tiedon jakaminen ja vaihto perustuu kuitenkin nykytilassa hyvin vahvasti säännöllisen yhteistoiminnan sijaan erillisesti ilmaistuihin tietotarpeisiin. Haastatteluiden perusteella kohdeosastoissa on havaittavissa myös parannustarpeita nykyiseen tiedonkulkuun ja tiedon jakamiseen verrattuna. Annetut kehitysehdotukset koskevat yhteistoiminnan muuntamista säännölliseksi ja keskustelevaksi, olemassa olevien tietojen tehokkaammasta jakamisesta ja nykyisten tietohallintajärjestelmien hyödyntämisestä yhteistoiminnassa uusien järjestelmien luomisen sijaan.

Knowing What to Respond in the Future Does Not Cancel the Influence of Past Events

Everyday tasks seldom involve isolate actions but sequences of them. We can see whether previous actions influence the current one by exploring the response time to controlled sequences of stimuli. Specifically, depending on the response-stimulus temporal interval (RSI), different mechanisms have been proposed to explain sequential effects in two-choice serial response tasks. Whereas an automatic facilitation mechanism is thought to produce a benefit for response repetitions at short RSIs, subjective expectancies are considered to replace the automatic facilitation at longer RSIs, producing a cost-benefit pattern: repetitions are faster after other repetitions but they are slower after alternations. However, there is not direct evidence showing the impact of subjective expectancies on sequential effects. By using a fixed sequence, the results of the reported experiment showed that the repetition effect was enhanced in participants who acquired complete knowledge of the order. Nevertheless, a similar cost-benefit pattern was observed in all participants and in all learning blocks. Therefore, results of the experiment suggest that sequential effects, including the cost-benefit pattern, are the consequence of automatic mechanisms which operate independently of (and simultaneously with) explicit knowledge of the sequence or other subjective expectancies.

Sijoitusrahastojen markkinointiviestintä ja myynninjohtaminen: Case Sp-Rahastoyhtiö Oy & Säästöpankkiliitto

Harvemmat sijoitusrahastoja käsitelleet tutkimukset ovat ottaneet huomioon sijoitusrahastojen välittäjien näkökulman. Tämä tutkimus on siinä mielessä ainutlaatuinen. Tutkimuksen tarkoituksena on selvittää erään rahastoyhtiön hallinnoimien sijoitusrahastojen markkinointiviestintää ja myynninjohtamista näitä välittävissä pankeissa. Tämän perusteella tavoitteena on ratkaista tämän tutkimuksen päätavoite, eli löytää ne syyt, jotka selittävät tutkimukseen valittujen pankkien sijoitusrahasto-osuuksien myynnissä havaittuja eroja. Pankeissa toteutetulla sijoitusrahastojen markkinointiviestintä- ja myynninjohtamismenetelmillä havaittiin olevan selkeät vaikutukset siihen, miksi sijoitusrahasto-osuuksien myyntimäärissä on ollut pankkikohtaisia eroja. Sijoitusrahasto-osuuksien myynnissä menestyneiden pankkien havaittiin olevan monin eri tavoin tehokkaampia ja parempia sijoitusrahastojen markkinointiviestinnässä ja niiden myynninjohtamisessa kuin heikommin sijoitusrahasto-osuuksien myynnissä menestyneet pankit.

Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework

Aim The aim of this study was to test different modelling approaches, including a new framework, for predicting the spatial distribution of richness and composition of two insect groups. Location The western Swiss Alps. Methods We compared two community modelling approaches: the classical method of stacking binary prediction obtained fromindividual species distribution models (binary stacked species distribution models, bS-SDMs), and various implementations of a recent framework (spatially explicit species assemblage modelling, SESAM) based on four steps that integrate the different drivers of the assembly process in a unique modelling procedure. We used: (1) five methods to create bS-SDM predictions; (2) two approaches for predicting species richness, by summing individual SDM probabilities or by modelling the number of species (i.e. richness) directly; and (3) five different biotic rules based either on ranking probabilities from SDMs or on community co-occurrence patterns. Combining these various options resulted in 47 implementations for each taxon. Results Species richness of the two taxonomic groups was predicted with good accuracy overall, and in most cases bS-SDM did not produce a biased prediction exceeding the actual number of species in each unit. In the prediction of community composition bS-SDM often also yielded the best evaluation score. In the case of poor performance of bS-SDM (i.e. when bS-SDM overestimated the prediction of richness) the SESAM framework improved predictions of species composition. Main conclusions Our results differed from previous findings using community-level models. First, we show that overprediction of richness by bS-SDM is not a general rule, thus highlighting the relevance of producing good individual SDMs to capture the ecological filters that are important for the assembly process. Second, we confirm the potential of SESAM when richness is overpredicted by bS-SDM; limiting the number of species for each unit and applying biotic rules (here using the ranking of SDM probabilities) can improve predictions of species composition

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.