A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.National Science Foundation (IRI 90-24877, IRI 87-16960); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (90-0083

Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models - usually biophysical models of neuronal activity

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974

Neurology

Contains reports on eleven research projects.U.S. Air Force (AF49(638)-1130)Army Chemical Corps (DA-18-108-405-Cml-942)U.S. Public Health Service (B-3055)National Science Foundation (Grant G-16526)U.S. Public Health Service (B-3090)U.S. Air Force (AF33(616)-7588)Office of Naval Research (Nonr-1841(70)

ChiroDiff: Modelling chirographic data with Diffusion Models

Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality). Consequently, temporal data has been modelled as discrete token sequences of fixed sampling rate instead of capturing the true underlying concept. In this paper, we introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data that specifically addresses these flaws. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate up to a good extent. Moreover, we show that many important downstream utilities (e.g. conditional sampling, creative mixing) can be flexibly implemented using ChiroDiff. We further show some unique use-cases like stochastic vectorization, de-noising/healing, abstraction are also possible with this model-class. We perform quantitative and qualitative evaluation of our framework on relevant datasets and found it to be better or on par with competing approaches.Comment: Accepted at ICLR '2

Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control

Origins and Early Development of the Nonlinear Endogenous Mathematical Theory of the Business Cycle: Part I - The Setting

We study the emergence of the nonlinear, endogenous, theory of the business cycle, in mathematical modes, within the framework of a macroeconomic theory, which was itself going through its own formal 'birth pangs' at the same time, in the same years. The first part of the story begins in 1928 and ends, with the publication of Yasui's classic on Kaldor, Hicks and Goodwin, in 1953, and Hudson's classic of 1957. But there were other classics in the 1930s, even within some theories of the business cycles of the time - particularly the Austrian and that which may now be called the 'time-to-build' tradition, which originates in Marx and Aftalion, independently, and reaches its nonlinear formalization origins in Tinbergenís work of 1931, followed by Kalecki's theories of the business cycle, substantially influenced also by Tinbergen's classic for mathematical method. There is also what may, for want of a better name, be called the 'cobweb' tradition, on the one hand, and the tradition of Swedish Sequence Analysis, on the other (especially in the 1937 classic work of Lundberg, summarising the Swedish discussion on business cycle theory). The former having its origins, partly, in Austrian inspired search for an integration of dynamic method with equilibrium economic theory (especially represented by a series of classics by Rosenstein-Rodan, from about 1929); and partly in the well known phenomenon of lagged responses in the supply-demand interactions in agricultural and commodity markets, particularly elegantly formalised by Leontief in 1934. From the point of view of economic theory, they were all part of the emerging consensus on the need to incorporate money and áuctuations in nontrivial ways as intrinsic components of orthodox equilibrium economic theory which was characterised as static theory. The implication was that the search was for a synthesis of dynamic method with traditional static equilibrium economic theory. The origins of macroeconomic theory, generally attributed to the post-depression development of monetary theory, business cycle theory and the theory of policy, could be traced to this particular search for a synthesis and was brilliantly summarised by Kuznets in a series of pioneering contributions in 1929/30. The story we try to tell is of mathematical business cycle theory in its non-linear modes, and how it emerged from one strand of macroeconomic theory, which, as just mentioned, was itself being forged, ab initio, dynamically

Approaching real time dynamic signature verification from a systems and control perspective.

Student Number : 9901877H MSc Dissertation School of Electrical and Information Engineering Faculty of Engineering and the Built Environmentalgorithm. The origins of handwriting idiosyncrasies and habituation are explained using systems theory, and it is shown that the 2/3 power law governing biomechanics motion also applies to handwriting. This leads to the conclusion that it is possible to derive handwriting velocity profiles from a static image, and that a successful forgery of a signature is only possible in the event of the forger being able to generate a signature using natural ballistic motion. It is also shown that significant portion of the underlying dynamic system governing the generation of handwritten signatures can be inferred by deriving time segmented transfer function models of the x and y co-ordinate velocity profiles of a signature. The prototype algorithm consequently developed uses x and y components of pen-tip velocity profiles (vx[n] and vy[n]) to create signature representations based on autoregression-with-exogenous-input (ARX) models. Verification is accomplished using a similarity measure based on the results of a k-step ahead predictor and 5 complementary metrics. Using 350 signatures collected from 21 signers, the system’s false acceptance (FAR) and false rejection (FRR) rates were 2.19% and 27.05% respectively. This high FRR is a result of measurement inadequacies, and it is believed that the algorithm’s FRR is approximately 18%

Detección y evasión de obstáculos usando redes neuronales híbridas convolucionales y recurrentes

[ES] Los términos "detección y evasión" hacen referencia al requerimiento esencial de un piloto para "ver y evitar" colisiones aire-aire. Para introducir UAVs en el día a día, esta funcion del piloto debe ser replicada por el UAV. En pequeños UAVs como pueden ser los destinados a la entrega de pedidos, existen ciertos aspectos limitantes en relación a tamaño, peso y potencia, por lo que sistemas cooperativos como TCAS o ADS-B no pueden ser utilizados y en su lugar otros sistemas como cámaras electro-ópticas son candidatos potenciales para obtener soluciones efectivas. En este tipo de aplicaciones, la solución debe evitar no solo otras aeronaves sino también otros obstáculos que puedan haber cerca de la superficie donde probablemente se operará la mayoría del tiempo. En este proyecto se han utilizado redes neuronales híbridas que incluyen redes neuronales convolucionales como primera etapa para clasificar objetos y redes neuronales recurrentes a continuación para deteminar la secuencia de eventos y actuar consecuentemente. Este tipo de red neuronal es muy actual y no se ha investigado en exceso hasta la fecha, por lo que el principal objetivo del proyecto es estudiar si podrían ser aplicadas en sistemas de "detección y evasión". Algoritmos de acceso libre han sido fusionados y mejorados para crear un nuevo modelo capaz de funcionar en este tipo de aplicaciones. A parte del algoritmo de detección y seguimiento, la parte correspondiente a la evasión de colisiones también fue desarrollada. Un filtro Kalman extendido se utilizó para estimar el rango relativo entre un obstáculo y el UAV. Para obtener una resolución sobre la posibilidad de conflicto, una aproximación estocástica fue considerada. Finalmente, una maniobra de evasión geométrica fue diseñada para utilizar si fuera necesario. Esta segunda parte fue evaluada mediante una simulación que también fue creada para el proyecto. Adicionalmente, un ensayo experimental se llevó a cabo para integrar las dos partes del algoritmo. Datos del ruido de la medida fueron experimentalmente obtenidos y se comprobó que las colisiones se podían evitar satisfactoriamente con dicho valor. Las principales conclusiones fueron que este nuevo tipo funciona más rápido que los métodos basados en redes neuronales más comunes, por lo que se recomiendo seguir investigando en ellas. Con la técnica diseñada, se encuentran disponibles multiples parámetros de diseño que pueden ser adaptados a diferentes circumstancias y factores. Las limitaciones principales encontradas se centran en la detección de obstáculos y en la estimación del rango relativo, por lo que se sugiere que la futura investigación se dirija en estas direcciones.[EN] A Sense and Avoid technique has been developed in this master thesis. A special method for small UAVs which use only an electro-optical camera as the sensor has been considered. This method is based on a sophisticated processing solution using hybrid Convolutional and Recurrent Neural Networks. The aim is to study the feasibility of this kind of neural networks in Sense and Avoid applications. First, the detection and tracking part of the algorithm is presented. Two models were used for this purpose: a Convolutional Neural Network called YOLO and a hybrid Convolutional and Recurrent Neural Network called Re3. After that, the collision avoidance part was designed. This consisted of the obstacle relative range estimation using an Extended Kalman Filter, the conflict probability calculation using an analytical approach and the geometric avoidance manoeuvre generation. Both parts were assessed separately by videos and simulations respectively, and then an experimental test was carried out to integrate them. Measurement noise was experimentally tested and simulations were performed again to check that collisions were avoided with the considered detection and tracking approach. Results showed that the considered approach can track objects faster than the most common computer vision methods based on neural networks. Furthermore, the conflict was successfully avoided with the proposed technique. Design parameters were allowed to adjust speed and maneuvers accordingly to the expected environment or the required level of safety. The main conclusion was that this kind of neural network could be successfully applied to Sense and Avoid systems.Vidal Navarro, D. (2018). Sense and avoid using hybrid convolutional and recurrent neural networks. Universitat Politècnica de València. http://hdl.handle.net/10251/142606TFG

空中ジェスチャ・インタラクションにおける触覚提示の高臨場感化および安定化に関する研究

Tohoku University昆陽雅司課