Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 133)

This special bibliography lists 276 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in September 1974

Different Effects of Static and Vibrating Foam Rollers on Ankle Plantar Flexion Flexibility and Neuromuscular Activation

The relatively new implementation of vibration into foam rollers was developed in response to the positive feedback of whole-body vibration treatment and foam rolling therapy. The purpose of the study is to research the changes in range of motion and myoelectric activity of the ankle dorsiflexors in female NCAA Division I swimmers when using a vibrating foam roller in comparison to a static foam roller. Combining the self-myofascial release with the increased blood flow and muscle temperature exerted from vibration could possibly enhance the effects of foam-rolling treatment. The different effects of ankle flexibility and motor unit activation after static and vibrating foam rolling was measured with a sample size of 15 female collegiate swimmers. Resting flexibility was measured upon arrival and the participant then rolled from their popliteal fossa to the middle of the Achilles tendon for 30 seconds, three times, with a 15-second break in between each trial. Flexibility was measured immediately after the foam rolling procedure. Neuromuscular data was recorded using electromyography (EMG) during both an isokinetic and isometric ankle joint force production test using the Biodex dynamometer. The data was analyzed with a paired, one-tail, T-test for the difference between static and dynamic of the difference between post intervention and pre-intervention. Significant interaction in range of motion was found using a two-way ANOVA with repeated measures with a T-test value of 0.039. No significant interaction and no significant difference were found between the pre and post testing results of EMG data

The dynamics of phototaxis in photosynthetic microorganisms

The motility strategies of M. pusilla are characterised for the first time, establishing a new variant of run-tumble motion: stop, run or reverse. This pattern bears remarkable similarities to the run-reverse motion of marine bacteria, suggesting the size of the organism dominates the choice of motility strategy. The phototaxis of M. pusilla is then described for the first time at both population and single cell scales. The proposed method of phototaxis - an extension of the run length when the cell is orientated towards the light stimulus - is verified with a series of jump-diffusion numerical simulations. The phototactic study was extended by demonstrating the first recorded intensity dependent step-up photophobic response of M. pusilla, followed by a stronger step-down response. During these responses cells switch from typically stationary behaviour to continuous swimming to escape the harmful environment. The step-up response can also be triggered chemically during an apparent cell death, leading to burst events where cells attempt to escape from a spherically diffusing source of pollutant radiating from a single cell. The similarities in these independent responses suggest there is a global avoidance strategy present in the organism to escape from harmful environments. Finally, a new experimental system is proposed to investigate phototaxis in more complicated optical landscapes and channel confinements using the model organism C. reinhardtii. Photoaccumulation to two Gaussian stripes is observed to impede the transport through a channel, opening up the question of what influence phototaxis could have in porous media. In whole, the motility and phototactic behaviour of the most globally dominant pico-eukaryote M. pusilla has been investigated and characterised for the first time, as well the discovery of an apparently universal avoidance strategy from a variety of harmful environments

Report / Institute für Physik

The 2016 Report of the Physics Institutes of the Universität Leipzig presents a hopefully interesting overview of our research activities in the past year. It is also testimony of our scientific interaction with colleagues and partners worldwide. We are grateful to our guests for enriching our academic year with their contributions in the colloquium and within our work groups

A Two-Stage Learning Approach for Goalie, Net and Stick Pose Estimation in Ice Hockey

Accurate pose estimation of ice hockey goaltenders presents a unique challenge due to the dynamic nature of the sport and the intricate interactions among the goalie, equipment, and net. This study introduces a comprehensive investigation into goalie pose estimation using both One-Stage and Two-Stage Learning GoalieNet architectures. The One-Stage Learning GoalieNet predicts all keypoints simultaneously, while the Two-Stage Learning GoalieNet employs a Keypoint Predictor Network (KPN) to predict 26 out of 29 keypoints and a Keyheatmap Fusion Network (KFN) to predict 3 stick-related keypoints. Evaluation on a NHL dataset underscores the effectiveness of both approaches in accurately predicting keypoints. Results on the test data reveal a median percentage of detected keypoints of 71% for the Two-Stage approach and 70% for the One-Stage approach, along with normalized localization errors on detected keypoints of 0.0187 for the Two-Stage and 0.0194 for the One-Stage approach. This work introduces the first-ever goalie pose estimation technique designed specifically for ice hockey, accompanied by a thorough analysis of the obtained results

Electrogenerated chemiluminescence : from mechanistic insights to bioanalytical applications

Electrogenerated chemiluminescence (ECL) is a powerful analytical technique exploited for clinical, industrial and research applications. The high sensitivity and good selectivity, makes ECL a tool-of-choice analytical method for a broad range of assays, most importantly for a large number of commercialized bead-based immunoassays. In the present thesis, we aimed to study the ECL phenomenon and its application in development of new analytical methods.In the first part of this work, we used an imaging technique to investigate the ECL mechanisms operating in bead-based assays. Spatial reactivity mapping at the level of a single functionalised bead provides a new strategy to test the co-reactant efficiency and shows associated optical focusing effects.In the second part, the design of a novel anti-transglutaminase ECL immunoassay for celiac disease diagnostic is shown using nanoelectrode ensembles as bioelectroanalytical platforms. We also studied the characteristics of ECL generated by arrays of boron-doped-diamond nanoelectrodes (BDD NEAs) as a promising materials for bioapplications. The ECL efficiency of two co-reactants at BDD NEAs was investigated.Finally, bipolar electrochemistry is a ‘‘wireless’’ process that was exploited for the controlled motion of conductive objects exposed to an electric field in the absence of direct ohmic contact. In the third part of the thesis, we report ECL coupled to bipolar electrochemistry for tracking the autonomous trajectories of swimmers by light emission. We further expanded this concept for dynamic enzymatic sensing of glucose concentration gradient using ECL light emission as an analytical readout.La chimiluminescence électrogénérée (ECL) est une technique analytique puissante exploitée pour la détection autant au niveau industriel que dans le domaine de la recherche scientifique ou du diagnostic clinique. La sensibilité élevée et la bonne sélectivité de cette technique font de l'ECL une méthode analytique de choix pour un large éventail d'applications, dont la plus importante est son utilisation commerciale dans un grand nombre de tests immunologiques à base de billes fonctionnalisées. Dans cette thèse, nous avons cherché à étudier le phénomène ECL et son application pour le développement de nouvelles techniques analytiques.Dans la première partie de ce travail, nous utilisons les techniques d'imagerie pour étudier les mécanismes ECL se produisant sur les billes utilisées pour les tests immunologiques. La cartographie de la réactivité au niveau d'une seule microparticule fonctionnalisée avec un complexe de ruthénium fournit une nouvelle stratégie visant à tester l'efficacité du co-réactif et montre des effets optiques associés de focalisation.Dans la deuxième partie, la conception d'un test immunologique pour la détection de l'anti-transglutaminase pour le diagnostic de la maladie coeliaque est présentée en utilisant des ensembles de nanoélectrodes comme plates-formes bioélectroanalytiques. Nous avons également étudié les caractéristiques de l'ECL générée par des réseaux de nanoélectrodes dopées au bore-diamant en tant que matériaux prometteurs pour des applications biologiques ainsi que l'efficacité ECL de deux co-réactifs sur ces réseaux.L'électrochimie bipolaire est un processus sans contact que nous avons exploité pour contrôler le mouvement d'objets conducteurs exposés à un champ électrique en l'absence de contact ohmique direct. Dans la troisième partie de ma thèse, nous présentons l'ECL couplée à l'électrochimie bipolaire pour le suivi d’objets autonomes luminescents. Nous avons élargi ce concept à la détection enzymatique dynamique de glucose en utilisant l'émission de lumière ECL comme signal analytique