Crowdfunding campaigns: Perceived quality and success

Research in Crowdfunding is an emerging priority within the field of Entrepreneurship. Hundreds of platforms provide nowadays multiple Crowdfunding schemes which are intended to make it easier for entrepreneurs and others to collect money from the crowd. However, only a few campaigns become successful as others don’t reach the pre-established funding goal. It is thus necessary to keep on understanding the dynamics of these platforms and the factors which justify success. The asymmetry of information has been shown to be a delicate issue as people perceive quality in different manners. As so, this research aims to understand which components of perceived quality mostly influence investments decisions. Mainly Entrepreneurship and Marketing theories were explored along the way. This is research follows a causal approach where nineteen hypotheses are tested. An experimental survey was conducted and data was collected from 127 people who were asked to evaluate one of the most important pieces of any Crowdfunding campaign – the pitch video – and consequently invest on the presented products.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

Quantitative analysis of image factors in a cultural heritage tourist destination

The tourism consumer’s purchase decision process is, to a great extent, conditioned by the image the tourist has of the different destinations that make up his or her choice set. In a highly competitive international tourist market, those responsible for destinations’ promotion and development policies seek differentiation strategies so that they may position the destinations in the most suitable market segments for their product in order to improve their attractiveness to visitors and increase or consolidate the economic benefits that tourism activity generates in their territory. To this end, the main objective we set ourselves in this paper is the empirical analysis of the factors that determine the image formation of Tarragona city as a cultural heritage destination. Without a doubt, UNESCO’s declaration of Tarragona’s artistic and monumental legacies as World Heritage site in the year 2000 meant important international recognition of the quality of the cultural and patrimonial elements offered by the city to the visitors who choose it as a tourist destination. It also represents a strategic opportunity to boost the city’s promotion of tourism and its consolidation as a unique destination given its cultural and patrimonial characteristics. Our work is based on the use of structured and unstructured techniques to identify the factors that determine Tarragona’s tourist destination image and that have a decisive influence on visitors’ process of choice of destination. In addition to being able to ascertain Tarragona’s global tourist image, we consider that the heterogeneity of its visitors requires a more detailed study that enables us to segment visitor typology. We consider that the information provided by these results may prove of great interest to those responsible for local tourism policy, both when designing products and when promoting the destination.

Compression-based Image Registration

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

Assessment of quality of life of patients under phototherapy for dermatological diseases

The skin and appendages is one of the first things in which people repair their social relationships as an alteration of these can influence the image and in multiple aspects of the subject’s life. Psoriasis is a chronic, relapsing skin disease that produces a marked reduction in the quality of life. Phototherapy, especially in form of narrow-band UVB is an alternative treatment of choice in plaque psoriasis and psoriasis of moderate extent. Also, regarding the impact of such treatment on quality of life of patients with psoriasis, there are few studies analyzing this effect.

Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.

Positioning an underwater vehicle through image mosaicking

Mosaics have been commonly used as visual maps for undersea exploration and navigation. The position and orientation of an underwater vehicle can be calculated by integrating the apparent motion of the images which form the mosaic. A feature-based mosaicking method is proposed in this paper. The creation of the mosaic is accomplished in four stages: feature selection and matching, detection of points describing the dominant motion, homography computation and mosaic construction. In this work we demonstrate that the use of color and textures as discriminative properties of the image can improve, to a large extent, the accuracy of the constructed mosaic. The system is able to provide 3D metric information concerning the vehicle motion using the knowledge of the intrinsic parameters of the camera while integrating the measurements of an ultrasonic sensor. The experimental results of real images have been tested on the GARBI underwater vehicle

Perceived self-image in adolescent idiopathic scoliosis: an integrative review of the literature

Objective: To learn about the experiences of adolescents diagnosed with idiopathic scoliosis. Method: Integrative review of the literature published within a specified time frame. Results: For both sexes, the predominant clinical symptom of this condition appears to be the negative effect that the deformity exerts on perceived self-image. Quantitative studies used numerical scores to assess perceptions of body image but did not analyse emotional aspects. Patients treated surgically were found to have a better self-image than patients treated with a brace. Quality of life was improved by a reduction in the magnitude of the curve. Conclusion: Spinal deformity exerts a psychological effect on adolescent girls.


Management of patient dose and image noise in routine pediatric CT abdominal examinations.

The aim was to propose a strategy for finding reasonable compromises between image noise and dose as a function of patient weight. Weighted CT dose index (CTDI(w)) was measured on a multidetector-row CT unit using CTDI test objects of 16, 24 and 32 cm in diameter at 80, 100, 120 and 140 kV. These test objects were then scanned in helical mode using a wide range of tube currents and voltages with a reconstructed slice thickness of 5 mm. For each set of acquisition parameter image noise was measured and the Rose model observer was used to test two strategies for proposing a reasonable compromise between dose and low-contrast detection performance: (1) the use of a unique noise level for all test object diameters, and (2) the use of a unique dose efficacy level defined as the noise reduction per unit dose. Published data were used to define four weight classes and an acquisition protocol was proposed for each class. The protocols have been applied in clinical routine for more than one year. CTDI(vol) values of 6.7, 9.4, 15.9 and 24.5 mGy were proposed for the following weight classes: 2.5-5, 5-15, 15-30 and 30-50 kg with image noise levels in the range of 10-15 HU. The proposed method allows patient dose and image noise to be controlled in such a way that dose reduction does not impair the detection of low-contrast lesions. The proposed values correspond to high- quality images and can be reduced if only high-contrast organs are assessed.

Reliability of the image evaluation system PGMI

Purpose: Many countries used the PGMI (P=perfect, G=good, M=moderate, I=inadequate) classification system for assessing the quality of mammograms. Limits inherent to the subjectivity of this classification have been shown. Prior to introducing this system in Switzerland, we wanted to better understand the origin of this subjectivity in order to minimize it. Our study aimed at identifying the main determinants of the variability of the PGMI system and which criteria are the most subjected to subjectivity. Methods and Materials: A focus group composed of 2 experienced radiographers and 2 radiologists specified each PGMI criterion. Ten raters (6 radiographers and 4 radiologists) evaluated twice a panel of 40 randomly selected mammograms (20 analogic and 20 digital) according to these specified PGMI criteria. The PGMI classification was assessed and the intra- and inter-rater reliability was tested for each professional group (radiographer vs radiologist), image technology (analogic vs digital) and PGMI criterion. Results: Some 3,200 images were assessed. The intra-rater reliability appears to be weak, particularly in respect to inter-rater variability. Subjectivity appears to be largely independent of the professional group and image technology. Aspects of the PGMI classification criteria most subjected to variability were identified. Conclusion: Post-test discussions enabled to specify more precisely some criteria. This should reduce subjectivity when applying the PGMI classification system. A concomitant, important effort in training radiographers is also necessary.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Quality of assessement of CMR images in the setting of the European cardiovascular magnetic resonance registry : description and validation of standardized criteria

l'imagerie par résonance magnétique (IRMC) est une technologie utilisée depuis les aimées quatre¬-vingts dans le monde de la cardiologie. Cette technique d'imagerie non-invasive permet d'acquérir Ses images du coeur en trois dimensions, dans n'importe quel, plan, sans application de radiation, et en haute résolution. Actuellement, cette technique est devenue un référence dans l'évaluation et 'l'investigation de différentes pathologies cardiaques. La morphologie cardiaque, la fonction des ventricules ainsi que leur contraction, la perfusion tissulaire ainsi que la viabilité tissulaire peuvent être caractérisés en utilisant différentes séquences d'imagerie. Cependant, cette technologie repose sur des principes physiques complexes et la mise en pratique de cette technique se heurte à la difficulté d'évaluer un organe en mouvement permanent. L'IRM cardiaque est donc sujette à différents artefacts qui perturbent l'interprétation des examens et peuvent diminuer la précision diagnostique de cette technique. A notre connaissance, la plupart des images d'IRMC sont analysées et interprétées sans évaluation rigoureuse de la qualité intrinsèque de l'examen. Jusqu'à présent, et à notre connaissance, aucun critère d'évaluation de la qualité des examens d'IRMC n'a été clairement déterminé. L'équipe d'IRMC du CHUV, dirigée par le Prof J. Schwitter, a recensé une liste de 35 critères qualitatifs et 12 critères quantitatifs évaluant la qualité d'un examen d'IRMC et les a introduit dans une grille d'évaluation. L'objet de cette étude est de décrire et de valider la reproductibilité des critères figurant dans cette grille d'évaluation, par l'interprétation simultanée d'examens IRMC par différents observateurs (cardiologues spécialisés en IRM, étudiant en médecine, infirmière spécialisée). Notre étude a permis de démontrer que les critères définis pour l'évaluation des examens d'IRMC sont robustes, et permettent une bonne reproductibilité intra- et inter-observateurs. Cette étude valide ainsi l'utilisation de ces critères de qualité dans le cadre de l'imagerie par résonance magnétique cardiaque. D'autres études sont encore nécessaires afin de déterminer l'impact de la qualité de l'image sur la précision diagnostique de cette technique. Les critères standardisés que nous avons validés seront utilisés pour évaluer la qualité des images dans le cadre d'une étude à échelle européenne relative à l'IRMC : "l'EuroCMR registry". Parmi les autres utilités visées par ces critères de qualité, citons notamment la possibilité d'avoir une référence d'évaluation de la qualité d'examen pour toutes les futures études cliniques utilisant la technologie d'IRMC, de permettre aux centres d'IRMC de quantifier leur niveau de qualité, voire de créer un certificat de standard de qualité pour ces centres, d'évaluer la reproductibilité de l'évaluation des images par différents observateurs d'un même centre, ou encore d'évaluer précisément la qualité des séquences développées à l'avenir dans le monde de l'IRMC.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Enhanced oligonucleotide delivery to mouse retinal cells using iontophoresis.

PURPOSE: To study the combination of oligodeoxynucleotides (ODNs) intravitreous injection and saline transpalpebral iontophoresis on the delivery of ODNs to photoreceptors in the newborn rd1/rd1 mice. METHODS: Cathodal or anodal transpalpebral iontophoresis (1.43 mA/cm(2) for 5 min) was applied to eyes of postnatal day 7 (PN7) rd1/rd1 mice immediately before the intravitreous injection of ODNs. The effect of cathodal iontophoresis after ODNs injection was also evaluated. The influence of current intensity (0.5, 1.5, and 2.5 mA) was assayed with cathodal iontophoresis performed prior to ODNs injection. The duration of current-induced facilitation of ODNs delivery to photoreceptors was evaluated for 6 h following iontophoresis. One group of control eyes received cathodal iontophoresis prior to the intravitreous injection of phosphate buffered saline (PBS) or hexachlorofluorescein (Hex). The second control group received ODN or Hex intravitreous injection without iontophoresis. The penetration of fluorescent ODNs in the outer nuclear layer (ONL) was quantified by image analysis of the ONL fluorescence intensity on cryosection microphotographs. Integrity of ODN was assessed using acrylamide gel migration after its extraction from the retina of treated mice. The integrity of retinal structure, 1 and 24 h after iontophoresis, was analyzed using light and electron microscopy. RESULTS: Transpalpebral anodal or cathodal saline iontophoresis enhanced the penetration of ODNs in all retinal layers. Cathodal iontophoresis was more efficient than anodal iontophoresis in enhancing the tissue penetration of the injected ODN. Photoreceptor delivery of ODN was significantly higher when cathodal saline transpalpebral iontophoresis was applied prior than after the injection. The extent of enhanced tissue penetration decreased in parallel to the increased interval between iontophoresis application and the intravitreous injection. Current of 1.5 mA was safe and optimal for the delivery of ODNs to the ONL. One hour after iontophoresis followed by injection, ODN extracted from the retina of treated eyes remained intact. Histology and electron microscopy observations demonstrated that iontophoresis using the optimal parameters did not induce any permanent tissue alterations or structure damage. CONCLUSIONS: Saline transpalpebral iontophoresis facilitates the penetration of injected ODNs in photoreceptors for at least 3 h. This method may be considered for photoreceptor targeted gene therapy.