Active Disk Paxos with infinitely many processes

Abstract We present an improvement to the Disk Paxos protocol by Gafni and Lamport which utilizes extended functionality and flexibility provided by Active Disks and supports unmediated concurrent data access by an unlimited number of processes. The solution facilitates coordination by an infinite number of clients using finite shared memory. It is based on a collection of read-modify-write objects with faults, that emulate a new, reliable shared memory abstraction called a ranked register. The required read-modify-write objects are readily available in Active Disks and in Object Storage Device controllers, making our solution suitable for state-of-the-art Storage Area Network (SAN) environments. A preliminary version of this work appears in Proceedings of the 21st ACM Symposium on Principles of Distributed Computing (PODC02), August 2002.     

Future temporal logic needs infinitely many modalities

Kamp’s theorem states that there is a temporal logic with two modalities (“until” and “since”) which is expressively complete for the first-order monadic logic of order over the real line. In this paper we show that there is no temporal logic with finitely many modalities which is expressively complete for the future fragment of first-order monadic logic of order over the real line (a future formula over the real time line is a formula whose truth value at a point is independent of what happened in the past).     

On standard control problems for systems with infinitely many unstable poles

In this paper, H^∞ control for a class of linear time invariant systems with infinitely many unstable poles is studied. An example of such a plant is a high gain system with delayed feedback. We formulate the problem via a generalized plant which consists of a rational transfer matrix and the inverse of a scalar (possibly irrational) inner function. It is shown that the problem can be decomposed into a finite-dimensional H^∞ control problem and an additional rank condition.     

On the mixed sensitivity minimization for systems with infinitely many unstable modes

In this note we consider a class of linear time invariant systems with infinitely many unstable modes. By using the parameterization of all stabilizing controllers and a data transformation, we show that controllers for such systems can be computed using the techniques developed earlier for infinite dimensional plants with finitely many unstable modes.     

DeMorgan systems with an infinitely many negations in the strict monotone operator case

We give a new representation theorem of negation based on the generator function of the strict operator. We study a certain class of strict monotone operators which build the DeMorgan class with infinitely many negations. We show that the necessary and sufficient condition for this operator class is f_c(x)f_d(x) = 1, where f_c(x) and f_d(x) are the generator functions of the strict t-norm and strict t-conorm.     

Learning via finitely many queries

This work introduces a new query inference model that can access data and communicate with the teacher by asking finitely many Boolean queries in a language L. In this model the parameters of interest are the number of queries used and the expressive power of L. We study how the learning power varies with these parameters. Results suggest that this model may help studying query inference in a resource bounded environment.AMS subject classification 68Q05, 68Q32, 68T05, 03D10, 03D80     

Learning conditional photometric stereo with high-resolution features

Computational Visual Media - Photometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination....     

Strong convergence of the modified Ishikawa iterative method for infinitely many nonexpansive mappings in Banach spaces

In this paper, we introduce a new modified Ishikawa iterative process for computing fixed points of an infinite family nonexpansive mapping in the framework of Banach spaces. Then, we establish the strong convergence theorem of the proposed iterative scheme under some mild conditions which solves a variational inequality. The results obtained in this paper extend and improve on the recent results of Qin et al. [Strong convergence theorems for an infinite family of nonexpansive mappings in Banach spaces, Journal of Computational and Applied Mathematics 230 (1) (2009) 121–127], Cho et al. [Approximation of common fixed points of an infinite family of nonexpansive mappings in Banach spaces, Computers and Mathematics with Applications 56 (2008) 2058–2064] and many others.     

Learning to classify with missing and corrupted features

A common assumption in supervised machine learning is that the training examples provided to the learning algorithm are statistically identical to the instances encountered later on, during the classification phase. This assumption is unrealistic in many real-world situations where machine learning techniques are used. We focus on the case where features of a binary classification problem, which were available during the training phase, are either deleted or become corrupted during the classification phase. We prepare for the worst by assuming that the subset of deleted and corrupted features is controlled by an adversary, and may vary from instance to instance. We design and analyze two novel learning algorithms that anticipate the actions of the adversary and account for them when training a classifier. Our first technique formulates the learning problem as a linear program. We discuss how the particular structure of this program can be exploited for computational efficiency and we prove statistical bounds on the risk of the resulting classifier. Our second technique addresses the robust learning problem by combining a modified version of the Perceptron algorithm with an online-to-batch conversion technique, and also comes with statistical generalization guarantees. We demonstrate the effectiveness of our approach with a set of experiments.     

Learning motion and content-dependent features with convolutions for action recognition

A variety of recognizing architectures based on deep convolutional neural networks have been devised for labeling videos containing human motion with action labels. However, so far, most works cannot properly deal with the temporal dynamics encoded in multiple contiguous frames, which distinguishes action recognition from other recognition tasks. This paper develops a temporal extension of convolutional neural networks to exploit motion-dependent features for recognizing human action in video. Our approach differs from other recent attempts in that it uses multiplicative interactions between convolutional outputs to describe motion information across contiguous frames. Interestingly, the representation of image content arises when we are at work on extracting motion pattern, which makes our model effectively incorporate both of them to analysis video. Additional theoretical analysis proves that motion and content-dependent features arise simultaneously from the developed architecture, whereas previous works mostly deal with the two separately. Our architecture is trained and evaluated on the standard video actions benchmarks of KTH and UCF101, where it matches the state of the art and has distinct advantages over previous attempts to use deep convolutional architectures for action recognition.     

Learning features from georeferenced seafloor imagery with location guided autoencoders

Although modern machine learning has the potential to greatly speed up the interpretation of imagery, the varied nature of the seabed and limited availability of expert annotations form barriers to its widespread use in seafloor mapping applications. This motivates research into unsupervised methods that function without large databases of human annotations. This paper develops an unsupervised feature learning method for georeferenced seafloor visual imagery that considers patterns both within the footprint of a single image frame and broader scale spatial characteristics. Features within images are learnt using an autoencoder developed based on the AlexNet deep convolutional neural network. Features larger than each image frame are learnt using a novel loss function that regularises autoencoder training using the Kullback–Leibler divergence function to loosely assume that images captured within a close distance of each other look more similar than those that are far away. The method is used to semantically interpret images taken by an autonomous underwater vehicle at the Southern Hydrates Ridge, an active gas hydrate field and site of a seafloor cabled observatory at a depth of 780 m. The method's performance when applied to clustering and content‐based image retrieval is assessed against a ground truth consisting of more than 18,000 human annotations. The study shows that the location based loss function increases the rate of information retrieval by a factor of two for seafloor mapping applications. The effects of physics‐based colour correction and image rescaling are also investigated, showing that the improved consistency of spatial information achieved by rescaling is beneficial for recognising artificial objects such as cables and infrastructures, but is less effective for natural objects that have greater dimensional variability.     

Learning CNN features from DE features for EEG-based emotion recognition

Pattern Analysis and Applications - Recently, deep neural networks (DNNs) have shown the remarkable success of feature representations in computer vision, audio analysis, and natural language...     

Learning multi-scale features for foreground segmentation

Pattern Analysis and Applications - Foreground segmentation algorithms aim at segmenting moving objects from the background in a robust way under various challenging scenarios....     

Learning invariant features using inertial priors

We address the technical challenges involved in combining key features from several theories of the visual cortex in a single coherent model. The resulting model is a hierarchical Bayesian network factored into modular component networks embedding variable-order Markov models. Each component network has an associated receptive field corresponding to components residing in the level directly below it in the hierarchy. The variable-order Markov models account for features that are invariant to naturally occurring transformations in their inputs. These invariant features give rise to increasingly stable, persistent representations as we ascend the hierarchy. The receptive fields of proximate components on the same level overlap to restore selectivity that might otherwise be lost to invariance.      

Learning a perceptual manifold with deep features for animation video resequencing

Multimedia Tools and Applications - We propose a novel deep learning framework for animation video resequencing. Our system produces new video sequences by minimizing a perceptual distance of...     

Learning flexible features for conditional random fields

Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features. These features can be functions of arbitrary combinations of observations, labels and auxiliary hidden variables. We also present a simple induction scheme to learn these features, which can automatically determine the complexity needed for a given data set. We apply the model to two real-world tasks, information extraction and image labeling, and compare our results to several other methods for discriminative labeling.     

Learning discriminative features for semi-supervised person re-identification

Multimedia Tools and Applications - We focus on the one-example person re-identification (Re-ID) task, where each identity has only one labeled example along with many unlabeled examples. Since...