The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time

Semi-autonomous robotic wheelchair controlled with low throughput human- machine interfaces

For a wide range of people with limited upper- and lower-body mobility, interaction with robots remains a challenging problem. Due to various health conditions, they are often unable to use standard joystick interface, most of wheelchairs are equipped with. To accommodate this audience, a number of alternative human-machine interfaces have been designed, such as single switch, sip-and-puff, brain-computer interfaces. They are known as low throughput interfaces referring to the amount of information that an operator can pass into the machine. Using them to control a wheelchair poses a number of challenges. This thesis makes several contributions towards the design of robotic wheelchairs controlled via low throughput human-machine interfaces: (1) To improve wheelchair motion control, an adaptive controller with online parameter estimation is developed for a differentially driven wheelchair. (2) Steering control scheme is designed that provides a unified framework integrating different types of low throughput human-machine interfaces with an obstacle avoidance mechanism. (3) A novel approach to the design of control systems with low throughput human-machine interfaces has been proposed. Based on the approach, position control scheme for a holonomic robot that aims to probabilistically minimize time to destination is developed and tested in simulation. The scheme is adopted for a real differentially driven wheelchair. In contrast to other methods, the proposed scheme allows to use prior information about the user habits, but does not restrict navigation to a set of pre-defined points, and parallelizes the inference and motion reducing the navigation time. (4) To enable the real time operation of the position control, a high-performance algorithm for single-source any-angle path planning on a grid has been developed. By abandoning the graph model and introducing discrete geometric primitives to represent the propagating wave front, we were able to design a planning algorithm that uses only integer addition and bit shifting. Experiments revealed a significant performance advantage. Several modifications, including optimal and multithreaded implementations, are also presented

Co-Speech Gesture in Communication and Cognition

xv, 256 p. : ill.This dissertation stages a reciprocal critique between traditional and marginal philosophical approaches to language on the one hand and interdisciplinary studies of speech-accompanying hand gestures on the other. Gesturing with the hands while speaking is a ubiquitous, cross-cultural human practice. Yet this practice is complex, varied, conventional, nonconventional, and above all under-theorized. In light of the theoretical and empirical treatments of language and gesture that I engage in, I argue that the hand gestures that spontaneously accompany speech are a part of language; more specifically, they are enactments of linguistic meaning. They are simultaneously (acts of) cognition and communication. Human communication and cognition are what they are in part because of this practice of gesturing. This argument has profound implications for philosophy, for gesture studies, and for interdisciplinary work to come. As further, strong proof of the pervasively embodied way that humans make meaning in language, reflection on gestural phenomena calls for a complete re-orientation in traditional analytic philosophy of language. Yet philosophical awareness of intersubjectivity and normativity as conditions of meaning achievement is well-deployed in elaborating and refining the minimal theoretical apparatus of present-day gesture studies. Triangulating between the most social, communicative philosophies of meaning and the most nuanced, reflective treatments of co-speech hand gesture, I articulate a new construal of language as embodied, world-embedded, intersubjectively normative, dynamic, multi-modal enacting of appropriative disclosure. Spontaneous co-speech gestures, while being indeed spontaneous, are nonetheless informed in various ways by conventions that they appropriate and deploy. Through this appropriation and deployment speakers enact, rather than represent, meaning, and they do so in various linguistic modalities. Seen thusly, gestures provide philosophers with a unique new perspective on the paradoxical determined-yet-free nature of all human meaning.Committee in charge: Mark Johnson, Chairperson; Ted Toadvine, Member; Naomi Zack, Member; Eric Pederson, Outside Membe

Vibration attenuation by mass redistribution

A nontraditional approach for active structural vibration attenuation was proposed using mass redistribution. The focus was on pendulum structures where the objective was to examine the effectiveness of mass reconfiguration along or within a structure to attenuate its vibrational energy. The mechanics associated with a translating mass along a rotating structure give rise to a Coriolis inertia force which either opposes or increases angular oscillations, thereby producing positive or negative damping, respectively. A strategy of cycling the mass to maximize attenuation and minimize amplification required the mass be moved at twice the frequency of the structural vibrations and be properly coordinated with the angular oscillations. The desired coordination involved moving the mass away from the pivot as the pendulum nears its vertical position and moving the mass towards the pivot when the pendulum nears its maximum angular excursion. System mass reconfiguration was analyzed by studying various mass displacement profiles including sinusoidal, piece-wise constant velocity and modified proportional and derivative action patterns. These strategies were optimized for various time intervals to maximize the rate of energy attenuation or minimize the final energy state. For small amplitude oscillations with sinusoidal mass motion, the dynamic behavior was modeled by Mathieu-Hill equations to explain the beating phenomenon that occurred when the frequency of the mass motion remained constant. Several control systems were designed to generate aforementioned mass reconfiguration profiles. The methodologies included human operator, modified proportional and derivative action, knowledge or rule based and artificial neural network controllers. The human operator system improved with experience and was the most effective. Other systems depended on the chosen parameterization or the implementation of self-adjusting parameters. Several unique tools were developed during the course of this research, as referenced herein

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

A Cognitive Grammar Analysis of the Semantics of the Russian Verbal Prefix na-

The Russian verbal prefix na- is one of a set of aspectual prefixes that exhibit characteristics of both derivational and inflectional morphemes. In addition to forming aspectual pairs as a grammatical marker of Perfective aspect, na-, in many cases, also carries lexical meaning; in these cases, na-prefixation changes the lexical/semantic meaning of the verbal stem, resulting in a distinct lexical item. I examine a sample of 40 verbs to compare the frequencies of na- as a lexicalized prefix and as a grammaticalized prefix. I then propose a radial category model to account for the polysemous functions of na-, with several metonymically and metaphorically related functions branching out from a single spatial prototype