Abstrakt  Detta kandidatarbete tar upp berättartekniker som long take och one shot. Undersökningen handlar om att kombinera dessa två filmtekniker som inte används lika ofta i dagens filmer genom att skapa en film där handlingen och ljudets betydelse samverkar med teknikerna, som leder till en fängslande filmupplevelse.  Vi vill använda dessa tekniker för visa och kritisera berättarteknikernas betydelse i film, att det blir en del av handlingen än bara ett objekt. För att driva handlingen framåt har vi studerat sjukdomen schizofreni för att skapa en berättelse. Vi utgick från Mats Ödeens manus metoder för att bygga en grund till gestaltningen och Natalie Ednell kunskap om schizofreni. Utförandet bestod av många tester av teknikerna och ljudet. En av testerna bestod av skådespelare på plats. Resultatet av undersökningen blev en filmupplevelse där alla beståndsdelar har en betydelse och ger ett annat berättarperspektiv angående schizofreni. Det finns betydligt fler berättartekniker men vi valde att fokusera på long take och one shot för att visa deras potential inom berättande. / Abstract This Bachelor thesis deals with storytelling techniques such as long take and one shot. The study is about combining these two film techniques that are not used as often in today's films by creating a film in which the action and the meaning of the sound interact with the techniques, which lead to a captivating film experience.  We want to use these techniques to show and criticize the importance of storytelling techniques in film, that it becomes a part of the action rather than just an object. To drive the action forward, we have studied the disease schizophrenia to create a story.  We used Mats Ödeen’s script methods to build a foundation for the design and Nathalie Ednell's knowledge of schizophrenia. The design consisted of many tests of the techniques and the sound. One of the tests had an actor on site.  The result of the study was a cinematic experience where all the elements have a meaning and gives a different narrative perspective regarding schizophrenia. There are significantly more storytelling techniques, but we chose to focus on long take and one shot to show their potential in storytelling.

This paper will provide a far-ranging analysis of the relevant aspects of the filmmaking process as it pertains to the development and production of the thesis project, The Good Killing. This analysis will include both a detailed, biographic overview of the making of the film, as well as an in-depth critique of the creative decision-making and practical methodology that guided the production. In this regard, special attention will be first be given to how the project was initially conceived, and, broadly speaking, what was originally intended. Secondly, proceeding sections will examine key elements of the filmmaker’s technical planning, performance, and working philosophy, specifically citing directing style, cinematography, sound and editing. Through this evaluative process, the film will be judged from the standpoint of both concept and execution in order to determine overall success.

Designing generalized data-driven distance measures for both ordered and unordered set data is the core focus of the proposed work. An ordered set is a set where time-linear property is maintained when distance between pair of temporal segments. One application in the ordered set is the human gesture analysis from RGBD data. Human gestures are fast becoming the natural form of human computer interaction. This serves as a motivation to modeling, analyzing, and recognition of gestures. The large number of gesture categories such as sign language, traffic signals, everyday actions and also subtle cultural variations in gesture classes makes gesture recognition a challenging problem. As part of generalization, an algorithm is proposed as part of an overlap speech detection application for unordered set. Any gesture recognition task involves comparing an incoming or a query gesture against a training set of gestures. Having one or few samples deters any class statistic learning approaches to classification, as the full range of variation is not covered. Due to the large variability in gesture classes, temporally segmenting individual gestures also becomes hard. A matching algorithm in such scenarios needs to be able to handle single sample classes and have the ability to label multiple gestures without temporal segmentation. Each gesture sequence is considered as a class and each class is a data point on an input space. A pair-wise distances pattern between to gesture frame sequences conditioned on a third (anchor) sequence is considered and is referred to as warp vectors. Such a process is defined as conditional distances. At the algorithmic core we have two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. We show that having class dependent distance function can disambiguate classification process where the samples of classes are close to each other. Given a situation where the model base is large (number of classes is also large); the disadvantage of such a distance would be the computational cost. A distributed version combined with sub-sampling anchor gestures is proposed as speedup strategy. In order to label multiple connected gestures in query we use a simultaneous segmentation and recognition matching algorithm called level building algorithm. We use the dynamic programming implementation of the level building algorithm. The core of this algorithm depends on a distance function that compares two gesture sequences. We propose that, we replace this distance function, with the proposed distances. Hence, this version of level building is called as conditional level building (clb). We present results on a large dataset of 8000 RGBD sequences spanning over 200 gesture classes, extracted from the ChaLearn Gesture Challenge dataset. The result is that there is significant improvement over the underlying distance used to compute conditional distance when compared to conditional distance. As an application of unordered set and non-visual data, overlap speech segment detection algorithm is proposed. Speech recognition systems have a vast variety of application, but fail when there is overlap speech involved. This is especially true in a meeting-room setting. The ability to recognize speaker and localize him/her in the room is an important step towards a higher-level representation of the meeting dynamics. Similar to gesture recognition, a new distance function is defined and it serves as the core of the algorithm to distinguish between individual speech and overlap speech temporal segments. The overlap speech detection problem is framed as outlier detection problem. An incoming audio is broken into temporal segments based on Bayesian Information Criterion (BIC). Each of these segments is considered as node and conditional distance between the nodes are determined. The underlying distances for triples used in conditional distances is the symmetric KL distance. As each node is modeled as a Gaussian, the distance between the two segments or nodes is given by Monte-Carlo estimation of the KL distance. An MDS based global embedding is created based on the pairwise distance between the nodes and RANSAC is applied to compute the outliers. NIST meeting room data set is used to perform experiments on the overlap speech detection. An improvement of more than 20% is achieved with conditional distance based approach when compared to a KL distance based approach.

Notre contribution concerne les trois domaines complémentaires suivants: la différentiation automatique de programmes, l'optimisation de formes pour de grands systèmes, l'adaptation de maillages. Dans le chapitre 1 de la partie 1, nous exposons une méthode de calcul de gradients par Différentiation Automatique pour un problème classique d'optimisation de formes. Nous expliquons comment déduire un gradient exact basé sur un état adjoint sans stocker explicitement le jacobien. Le mode adjoint de la DA que nous proposons utilise beaucoup moins d'espace mémoire. Dans le chapitre 2 de la partie 2, nous proposons une méthode de type SQP pour résoudre une classe de problèmes d'optimisation avec contraintes égalités. Le nouvel algorithme permet une résolution simultanée du système d'optimalité. Cette méthode one shot combine efficacité et robustesse. Dans le chapitre 3 de la partie 2, nous étudions une nouvelle stratégie de préconditionnement pour l'optimisation de formes. Nous construisons un préconditionnement multiniveau additif à partir du principe classique de Bramble-Pasciak-Xu et du principe d'agglomération. Nous spécifions aisément le gain en régularité de notre préconditionneur avec un seul paramètre réel. Dans le chapitre 1 de la partie 3, nous étudions le problème du meilleur maillage adapté pour de l'interpolation pure. La résolution du système d'optimalité donne une expression complètement explicite de la métrique optimale en fonction de la fonction à adapter. Dans le chapitre 2 de la partie 3, nous étendons la méthode du chapitre précédent au problème de l'adaptation de maillage pour EDP. Notre méthode repose sur une analyse a priori rigoureuse puis sur une modélisation.

One common shortcoming of modern computer vision is the inability of most models to generalize to new classes—one/few shot image recognition. We propose a new problem formulation for this task and present a network architecture and training methodology to solve this task. Further, we provide insights into how careful focus on how not just the data, but the way data presented to the model can have significant impact on performance. Using these method, we achieve high accuracy in few-shot image recognition tasks.

Contemporary cellular models of learning and memory are articulated around the idea that synapses undergo activity-dependent weight changes. However, conventional forms of Hebbian plasticity do not adequately address certain features inherent to behavioral learning. First, associative learning driven by delayed behavioral outcomes introduces a temporal credit assignment problem, whereby one must remember which action corresponds to which outcome. Yet, current models of associative synaptic plasticity, such as spike-timing-dependent plasticity, require near coincident activation of pre- and postsynaptic neurons (i.e., within ~ 10 ms), a time delay that is orders of magnitude smaller than that required for behavioral associations. For individual neurons to associate two cues, a biological mechanism capable of potentiating synaptic weights must be able to bind events that are separated in time. Theoretical work has suggested that a synaptic eligibility trace, a time-limited process that momentarily renders synapses eligible for weight updates via delayed instructive signals, can solve this problem. However, no material substrate of eligibility traces has been identified in the brain. Second, under certain conditions, neurons need to swiftly update their weights to reflect rapid learning. Current plasticity experiments require the repetition of multiple pairings to induce long-term synaptic plasticity. In this thesis, I addressed these problems using a combination of whole-cell recordings, two-photon uncaging, calcium imaging, and mechanistic modeling. I uncovered a form of synaptic plasticity known as behavioral timescale synaptic plasticity (BTSP) in layer 5 pyramidal neurons in the prefrontal cortex of mice. BTSP induced synaptic potentiation by pairing temporally separated pre- and postsynaptic events (0.5 s - 1 s), regardless of their order. The temporal window for BTSP induction offers a line of solution to the temporal credit assignment problem by highlighting the presence of a synaptic mechanism that expands the time for the induction of activity-dependent long-term synaptic plasticity, spanning hundreds of milliseconds. We further found that BTSP can be induced following a single pairing, enabling rapid weight updates required for one-shot learning. Using two-photon calcium imaging in apical oblique dendrites, I discovered a novel short-term and associative plasticity of calcium dynamics (STAPCD) that exhibited temporal characteristics mirroring the induction rules of BTSP. I identified a core set of molecular components crucial for both STAPCD and BTSP and developed a computational simulation that models the calcium dynamics as a latent memory trace of neural activity (i.e., eligibility traces). Together, we find that calcium handling by the endoplasmic reticulum enables synaptic weight updates upon receipt of delayed instructive signals, obeys rules of burst-dependent one-shot learning, and thus provides a mechanism that satisfies the requirements anticipated of eligibility traces. Collectively, these findings offer a neural mechanism for the binding of cellular events occurring in single shot and separated by behaviorally relevant temporal delays to induce potentiation at synapses, providing a cellular model of associative learning.

Face recognition is often described as the process of identifying and verifying people in a photograph by their face. Researchers have recently given this field increased attention, continuously improving the underlying models. The objective of this study is to implement a real-time face recognition system using one-shot learning. “One shot” means learning from one or few training samples. This paper evaluates different methods to solve this problem. Convolutional neural networks are known to require large datasets to reach an acceptable accuracy. This project proposes a method to solve this problem by reducing the number of training instances to one and still achieving an accuracy close to 100%, utilizing the concept of transfer learning.

Indiana University-Purdue University Indianapolis (IUPUI) / The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency. This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered. A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of generation events from transient swings caused by other events. This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events. This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.

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The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of<br><div>determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency.</div><br>This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered.<br><div><br></div><div>A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative<br></div>threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of<br>generation events from transient swings caused by other events.<br><br><div>This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events.</div><br>This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.