Página 1 dos resultados de 97407 itens digitais encontrados em 0.242 segundos

‣ Research in Computer Science and Computer Engineering

Feldman, J. A. ; Merriam, C. W.
Fonte: University of Rochester. Computer Science Department. Publicador: University of Rochester. Computer Science Department.
Tipo: Relatório
Português
Relevância na Pesquisa
660.24414%
This report describes many of the computer related research efforts at the University of Rochester. The Department of Computer Science is involved in research in automatic programming, including very high level languages and data structures; machine perception; and in problem solving using combinations of traditional heuristic methods, artificial intelligence,and utility theory. The research of the Department of Electrical Engineering includes basic computer engineering research in the construction of computer systems and operating systems, research in image processing and in numerical methods, and research in production automation which is concerned with mechanical manufacturing and assembly, and is currently developing mathematical models of parts, raw materials and tools. In conjunction with other departments, Electrical Engineering is also using computers for biomedical applications including ultrasound diagnostic techniques for heart disease, and pattern recognition techniques for detection of cancer from PAP smears.

‣ Research in Computer Science and Computer Engineering

Feldman, J. A. ; Merriam, C. W.
Fonte: University of Rochester. Computer Science Department. Publicador: University of Rochester. Computer Science Department.
Tipo: Relatório
Português
Relevância na Pesquisa
660.24414%
This report describes many of the computer related research efforts at the University of Rochester. The Department of Computer Science is involved in research in automatic programming, including very high level languages and data structures; machine perception; and in problem solving using combinations of traditional heuristic methods, artificial intelligence,and utility theory. The research of the Department of Electrical Engineering includes basic computer engineering research in the construction of computer systems and operating systems, research in image processing and in numerical methods, and research in production automation which is concerned with mechanical manufacturing and assembly, and is currently developing mathematical models of parts, raw materials and tools. In conjunction with other departments, Electrical Engineering is also using computers for biomedical applications including ultrasound diagnostic techniques for heart disease, and pattern recognition techniques for detection of cancer from PAP smears.

‣ Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections

Predd, Joel B.; Kulkarni, Sanjeev R.; Poor, H. Vincent
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/07/2005 Português
Relevância na Pesquisa
659.7225%
Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach.; Comment: To appear in the Proceedings of the SPIE Conference on Advanced Signal Processing Algorithms, Architectures and Implementations XV, San Diego, CA, July 31 - August 4, 2005

‣ Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Troccaz, Jocelyne; Baumann, Michael; Berkelman, Peter; Cinquin, Philippe; Daanen, Vincent; Leroy, Antoine; Marchal, Maud; Payan, Yohan; Promayon, Emmanuel; Voros, Sandrine; Bart, Stéphane; Bolla, Michel; Chartier-Kastler, Emmanuel; Descotes, Jean-Luc; Du
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/12/2007 Português
Relevância na Pesquisa
657.8165%
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed.

‣ Learning Monocular Reactive UAV Control in Cluttered Natural Environments

Ross, Stephane; Melik-Barkhudarov, Narek; Shankar, Kumar Shaurya; Wendel, Andreas; Dey, Debadeepta; Bagnell, J. Andrew; Hebert, Martial
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/11/2012 Português
Relevância na Pesquisa
657.5205%
Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.; Comment: 8 pages, 10 figures

‣ A Learning Scheme for Approachability in MDPs and Stackelberg Stochastic Games

Kalathil, Dileep; Borkar, Vivek; Jain, Rahul
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/11/2014 Português
Relevância na Pesquisa
655.92516%
The notion of approachability was introduced by Blackwell in the context of vector-valued repeated games. The famous approachability theorem prescribes a strategy for approachability, i.e., for `steering' the average vector-cost of a given player towards a given target set, irrespective of the strategies of the other players. In this paper, motivated from the multi-objective optimization/decision making problems in dynamically changing environments, we address the approachability problem in Markov Decision Processes (MDPs) and Stackelberg stochastic games with vector-valued cost functions. We make two main contributions. Firstly, we give simple and computationally tractable strategy for approachability for MDPs and Stackelberg stochastic games. Secondly, we give reinforcement learning algorithms to learn the approachable strategy when the transition kernel is unknown. We also show that the conditions that we give for approachability are both necessary and sufficient for convex sets and thus a complete characterization. We also give sufficient conditions for non-convex sets.; Comment: 18 Pages, Submitted to Mathematics of Operations Research

‣ Computer-Assisted Interactive Documentary and Performance Arts in Illimitable Space

Song, Miao
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/12/2012 Português
Relevância na Pesquisa
661.42414%
This major component of the research described in this thesis is 3D computer graphics, specifically the realistic physics-based softbody simulation and haptic responsive environments. Minor components include advanced human-computer interaction environments, non-linear documentary storytelling, and theatre performance. The journey of this research has been unusual because it requires a researcher with solid knowledge and background in multiple disciplines; who also has to be creative and sensitive in order to combine the possible areas into a new research direction. [...] It focuses on the advanced computer graphics and emerges from experimental cinematic works and theatrical artistic practices. Some development content and installations are completed to prove and evaluate the described concepts and to be convincing. [...] To summarize, the resulting work involves not only artistic creativity, but solving or combining technological hurdles in motion tracking, pattern recognition, force feedback control, etc., with the available documentary footage on film, video, or images, and text via a variety of devices [....] and programming, and installing all the needed interfaces such that it all works in real-time. Thus, the contribution to the knowledge advancement is in solving these interfacing problems and the real-time aspects of the interaction that have uses in film industry...

‣ Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining

Borne, Kirk D.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/08/2000 Português
Relevância na Pesquisa
656.10266%
The knowledge discovery potential of the new large astronomical databases is vast. When these are used in conjunction with the rich legacy data archives, the opportunities for scientific discovery multiply rapidly. A Virtual Observatory (VO) framework will enable transparent and efficient access, search, retrieval, and visualization of data across multiple data repositories, which are generally heterogeneous and distributed. Aspects of data mining that apply to a variety of science user scenarios with a VO are reviewed. The development of a VO should address the data mining needs of various astronomical research constituencies. By way of example, two user scenarios are presented which invoke applications and linkages of data across the catalog and image domains in order to address specific astrophysics research problems. These illustrate a subset of the desired capabilities and power of the VO, and as such they represent potential components of a VO Design Reference Mission.; Comment: 4 pages. Paper to appear in the proceedings of the June 2000 "Virtual Observatories of the Future" conference at Caltech, edited by R. J. Brunner, S. G. Djorgovski, & A. Szalay. (For figures and demos related to sample user scenarios, see http://adc.gsfc.nasa.gov/adc/adc_science/adc-science-scenario-papers.html .)

‣ Parallel Triangle Counting in Massive Streaming Graphs

Tangwongsan, Kanat; Pavan, A.; Tirthapura, Srikanta
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/08/2013 Português
Relevância na Pesquisa
655.7235%
The number of triangles in a graph is a fundamental metric, used in social network analysis, link classification and recommendation, and more. Driven by these applications and the trend that modern graph datasets are both large and dynamic, we present the design and implementation of a fast and cache-efficient parallel algorithm for estimating the number of triangles in a massive undirected graph whose edges arrive as a stream. It brings together the benefits of streaming algorithms and parallel algorithms. By building on the streaming algorithms framework, the algorithm has a small memory footprint. By leveraging the paralell cache-oblivious framework, it makes efficient use of the memory hierarchy of modern multicore machines without needing to know its specific parameters. We prove theoretical bounds on accuracy, memory access cost, and parallel runtime complexity, as well as showing empirically that the algorithm yields accurate results and substantial speedups compared to an optimized sequential implementation. (This is an expanded version of a CIKM'13 paper of the same title.)

‣ Learning Equilibria with Partial Information in Decentralized Wireless Networks

Rose, Luca; Perlaza, Samir M.; Lasaulce, Samson; Debbah, Mérouane
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/06/2011 Português
Relevância na Pesquisa
658.4198%
In this article, a survey of several important equilibrium concepts for decentralized networks is presented. The term decentralized is used here to refer to scenarios where decisions (e.g., choosing a power allocation policy) are taken autonomously by devices interacting with each other (e.g., through mutual interference). The iterative long-term interaction is characterized by stable points of the wireless network called equilibria. The interest in these equilibria stems from the relevance of network stability and the fact that they can be achieved by letting radio devices to repeatedly interact over time. To achieve these equilibria, several learning techniques, namely, the best response dynamics, fictitious play, smoothed fictitious play, reinforcement learning algorithms, and regret matching, are discussed in terms of information requirements and convergence properties. Most of the notions introduced here, for both equilibria and learning schemes, are illustrated by a simple case study, namely, an interference channel with two transmitter-receiver pairs.; Comment: 16 pages, 5 figures, 1 table. To appear in IEEE Communication Magazine, special Issue on Game Theory

‣ Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

Li, Peter; Qian, Jiyuan; Wang, Tian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/11/2015 Português
Relevância na Pesquisa
655.92516%
Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.

‣ Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

Lampos, Vasileios
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/08/2012 Português
Relevância na Pesquisa
656.3932%
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly...

‣ Galaxy-X: A Novel Approach for Multi-class Classification in an Open Universe

Dhifli, Wajdi; Diallo, Abdoulaye Baniré
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/11/2015 Português
Relevância na Pesquisa
656.3363%
Classification is a fundamental task in machine learning and artificial intelligence. Existing classification methods are designed to classify unknown instances within a set of previously known classes that are seen in training. Such classification takes the form of prediction within a closed-set. However, a more realistic scenario that fits the ground truth of real world applications is to consider the possibility of encountering instances that do not belong to any of the classes that are seen in training, $i.e.$, an open-set classification. In such situation, existing closed-set classification methods will assign a training label to these instances resulting in a misclassification. In this paper, we introduce Galaxy-X, a novel multi-class classification method for open-set problem. For each class of the training set, Galaxy-X creates a minimum bounding hyper-sphere that encompasses the distribution of the class by enclosing all of its instances. In such manner, our method is able to distinguish instances resembling previously seen classes from those that are of unseen classes. Experimental results on benchmark datasets show the efficiency of our approach in classifying novel instances from known as well as unknown classes. We also introduce a novel evaluation procedure to adequately evaluate open-set classification.

‣ Heterogeneous Learning in Zero-Sum Stochastic Games with Incomplete Information

Zhu, Quanyan; Tembine, Hamidou; Basar, Tamer
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/03/2011 Português
Relevância na Pesquisa
656.3363%
Learning algorithms are essential for the applications of game theory in a networking environment. In dynamic and decentralized settings where the traffic, topology and channel states may vary over time and the communication between agents is impractical, it is important to formulate and study games of incomplete information and fully distributed learning algorithms which for each agent requires a minimal amount of information regarding the remaining agents. In this paper, we address this major challenge and introduce heterogeneous learning schemes in which each agent adopts a distinct learning pattern in the context of games with incomplete information. We use stochastic approximation techniques to show that the heterogeneous learning schemes can be studied in terms of their deterministic ordinary differential equation (ODE) counterparts. Depending on the learning rates of the players, these ODEs could be different from the standard replicator dynamics, (myopic) best response (BR) dynamics, logit dynamics, and fictitious play dynamics. We apply the results to a class of security games in which the attacker and the defender adopt different learning schemes due to differences in their rationality levels and the information they acquire.

‣ The Revolution in Astronomy Education: Data Science for the Masses

Borne, Kirk D.; Jacoby, Suzanne; Carney, K.; Connolly, A.; Eastman, T.; Raddick, M. J.; Tyson, J. A.; Wallin, J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/09/2009 Português
Relevância na Pesquisa
656.301%
As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and unprecedented depths), so have the horizons of our understanding of the Universe been similarly expanding. This expansion is coupled to the exponential data deluge from multiple sky surveys, which have grown from gigabytes into terabytes during the past decade, and will grow from terabytes into Petabytes (even hundreds of Petabytes) in the next decade. With this increased vastness of information, there is a growing gap between our awareness of that information and our understanding of it. Training the next generation in the fine art of deriving intelligent understanding from data is needed for the success of sciences, communities, projects, agencies, businesses, and economies. This is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce). Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce...

‣ Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets

Prasad, Adarsh; Jegelka, Stefanie; Batra, Dhruv
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/11/2014 Português
Relevância na Pesquisa
658.4218%
To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image labelings, sentence parses, etc.) is exponentially large. We study greedy algorithms for finding a diverse subset of solutions in structured-output spaces by drawing new connections between submodular functions over combinatorial item sets and High-Order Potentials (HOPs) studied for graphical models. Specifically, we show via examples that when marginal gains of submodular diversity functions allow structured representations, this enables efficient (sub-linear time) approximate maximization by reducing the greedy augmentation step to inference in a factor graph with appropriately constructed HOPs. We discuss benefits, tradeoffs, and show that our constructions lead to significantly better proposals.

‣ Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots

Baranes, Adrien; Oudeyer, Pierre-Yves
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/01/2013 Português
Relevância na Pesquisa
659.878%
We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy pa- rameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: 1) learning the inverse kinematics in a highly-redundant robotic arm, 2) learning omnidirectional locomotion with motor primitives in a quadruped robot, 3) an arm learning to control a fishing rod with a flexible wire. We show that 1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; 2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly...

‣ Fluoroscopy-based navigation system in spine surgery

Merloz, Philippe; Troccaz, Jocelyne; Vouaillat, Hervé; Vasile, Christian; Tonetti, Jérôme; Eid, Ahmad; Plaweski, Stéphane
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 28/11/2007 Português
Relevância na Pesquisa
657.2066%
The variability in width, height, and spatial orientation of a spinal pedicle makes pedicle screw insertion a delicate operation. The aim of the current paper is to describe a computer-assisted surgical navigation system based on fluoroscopic X-ray image calibration and three-dimensional optical localizers in order to reduce radiation exposure while increasing accuracy and reliability of the surgical procedure for pedicle screw insertion. Instrumentation using transpedicular screw fixation was performed: in a first group, a conventional surgical procedure was carried out with 26 patients (138 screws); in a second group, a navigated surgical procedure (virtual fluoroscopy) was performed with 26 patients (140 screws). Evaluation of screw placement in every case was done by using plain X-rays and post-operative computer tomography scan. A 5 per cent cortex penetration (7 of 140 pedicle screws) occurred for the computer-assisted group. A 13 per cent penetration (18 of 138 pedicle screws) occurred for the non computer-assisted group. The radiation running time for each vertebra level (two screws) reached 3.5 s on average in the computer-assisted group and 11.5 s on average in the non computer-assisted group. The operative time for two screws on the same vertebra level reaches 10 min on average in the non computer-assisted group and 11.9 min on average in the computer-assisted group. The fluoroscopy-based (two-dimensional) navigation system for pedicle screw insertion is a safe and reliable procedure for surgery in the lower thoracic and lumbar spine.

‣ 6 Seconds of Sound and Vision: Creativity in Micro-Videos

Redi, Miriam; Hare, Neil O; Schifanella, Rossano; Trevisiol, Michele; Jaimes, Alejandro
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/11/2014 Português
Relevância na Pesquisa
655.33293%
The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.; Comment: 8 pages, 1 figures, conference IEEE CVPR 2014

‣ From Human-Computer Interaction to Human-Robot Social Interaction

Toumi, Tarek; Zidani, Abdelmadjid
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/12/2014 Português
Relevância na Pesquisa
657.4692%
Human-Robot Social Interaction became one of active research fields in which researchers from different areas propose solutions and directives leading robots to improve their interactions with humans. In this paper we propose to introduce works in both human robot interaction and human computer interaction and to make a bridge between them, i.e. to integrate emotions and capabilities concepts of the robot in human computer model to become adequate for human robot interaction and discuss challenges related to the proposed model. Finally an illustration through real case of this model will be presented.