Accessing files in an Internet: The Jade file system

Jade is a new distribution file system that provides a uniform way to name and access files in an internet environment. It makes two important contributions. First, Jade is a logical system that integrates a heterogeneous collection of existing file systems, where heterogeneous means that the underlying file systems support different file access protocols. Jade is designed under the restriction that the underlying file system may not be modified. Second, rather than providing a global name space, Jade permits each user to define a private name space. These private name spaces support two novel features: they allow multiple file systems to be mounted under one directory, and they allow one logical name space to mount other logical name spaces. A prototype of the Jade File System was implemented on Sun Workstations running Unix. It consists of interfaces to the Unix file system, the Sun Network File System, the Andrew File System, and FTP. This paper motivates Jade's design, highlights several aspects of its implementation, and illustrates applications that can take advantage of its features

Learning a face space for experiments on human identity

Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed "police sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to this submissio

Modeling Human Categorization of Natural Images Using Deep Feature Representations

Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli. In this paper, we use modern machine learning methods to extend this work into the realm of naturalistic stimuli, enabling human categorization to be studied over the complex visual domain in which it evolved and developed. We show that representations derived from a convolutional neural network can be used to model behavior over a database of >300,000 human natural image classifications, and find that a group of models based on these representations perform well, near the reliability of human judgments. Interestingly, this group includes both exemplar and prototype models, contrasting with the dominance of exemplar models in previous work. We are able to improve the performance of the remaining models by preprocessing neural network representations to more closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci 201

S-Net for multi-memory multicores

Copyright ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 5th ACM SIGPLAN Workshop on Declarative Aspects of Multicore Programming: http://doi.acm.org/10.1145/1708046.1708054S-Net is a declarative coordination language and component technology aimed at modern multi-core/many-core architectures and systems-on-chip. It builds on the concept of stream processing to structure dynamically evolving networks of communicating asynchronous components. Components themselves are implemented using a conventional language suitable for the application domain. This two-level software architecture maintains a familiar sequential development environment for large parts of an application and offers a high-level declarative approach to component coordination. In this paper we present a conservative language extension for the placement of components and component networks in a multi-memory environment, i.e. architectures that associate individual compute cores or groups thereof with private memories. We describe a novel distributed runtime system layer that complements our existing multithreaded runtime system for shared memory multicores. Particular emphasis is put on efficient management of data communication. Last not least, we present preliminary experimental data

Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels

Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explaining complex human visual representations. However, the categorization problem is posed differently for these networks than for humans: the accuracy of these networks is evaluated by their ability to identify single labels assigned to each image. These labels often cut arbitrarily across natural psychological taxonomies (e.g., dogs are separated into breeds, but never jointly categorized as "dogs"), and bias the resulting representations. By contrast, it is common for children to hear both "dog" and "Dalmatian" to describe the same stimulus, helping to group perceptually disparate objects (e.g., breeds) into a common mental class. In this work, we train CNN classifiers with multiple labels for each image that correspond to different levels of abstraction, and use this framework to reproduce classic patterns that appear in human generalization behavior.Comment: 6 pages, 4 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

A Variational Approach for Minimizing Lennard-Jones Energies

A variational method for computing conformational properties of molecules with Lennard-Jones potentials for the monomer-monomer interactions is presented. The approach is tailored to deal with angular degrees of freedom, {\it rotors}, and consists in the iterative solution of a set of deterministic equations with annealing in temperature. The singular short-distance behaviour of the Lennard-Jones potential is adiabatically switched on in order to obtain stable convergence. As testbeds for the approach two distinct ensembles of molecules are used, characterized by a roughly dense-packed ore a more elongated ground state. For the latter, problems are generated from natural frequencies of occurrence of amino acids and phenomenologically determined potential parameters; they seem to represent less disorder than was previously assumed in synthetic protein studies. For the dense-packed problems in particular, the variational algorithm clearly outperforms a gradient descent method in terms of minimal energies. Although it cannot compete with a careful simulating annealing algorithm, the variational approach requires only a tiny fraction of the computer time. Issues and results when applying the method to polyelectrolytes at a finite temperature are also briefly discussed.Comment: 14 pages, uuencoded compressed postscript fil

Effect of Phenolic Matrix Microcracking on the Structural Response of a 3-D Woven Thermal Protection System

The effect of microcracking in the phenolic matrix of a three-dimensional woven thermal protection system (TPS) and the resulting material stiffness reduction was studied via a comparison of finite element analysis results from a linear analysis and an iterative linear analysis. A TPS is necessary to protect space vehicles from the aerodynamic heating of planetary entry. The Heatshield for Extreme Entry Environment Technology (HEEET) project has developed a TPS for use in high heat-flux and pressure missions. The material is a dual-layer continuous dry weave, which is then infiltrated with a low-density phenolic resin matrix to form a rigid ablator. The phenolic resin matrix does not have structural load transfer requirements, and testing has shown that the phenolic resin can fully satisfy thermal requirements when the matrix contains microcracks. Due to high stresses in the through-the-thickness direction of the material, phenolic microcracks may form in the matrix material, which would result in a reduction of stiffness. An exploratory study was conducted to determine if reduction in material stiffness would change the load paths and/or decrease the structural margins. A comparison was performed between a linear finite element analysis that did not take into account phenolic microcracking and an iterative linear finite element analysis that accounted for propagation of phenolic microcracking. Four subcases using varying assumptions were analyzed and the results indicate that the assumed strength at which the phenolic microcracking propagates was the critical parameter for determining the extent of microcracking in the phenolic matrix. Phenolic microcracking does not have an adverse effect on the structural response of the test article and is not a critical failure

Practical quantum key distribution over a 48-km optical fiber network

The secure distribution of the secret random bit sequences known as "key" material, is an essential precursor to their use for the encryption and decryption of confidential communications. Quantum cryptography is a new technique for secure key distribution with single-photon transmissions: Heisenberg's uncertainty principle ensures that an adversary can neither successfully tap the key transmissions, nor evade detection (eavesdropping raises the key error rate above a threshold value). We have developed experimental quantum cryptography systems based on the transmission of non-orthogonal photon states to generate shared key material over multi-kilometer optical fiber paths and over line-of-sight links. In both cases, key material is built up using the transmission of a single-photon per bit of an initial secret random sequence. A quantum-mechanically random subset of this sequence is identified, becoming the key material after a data reconciliation stage with the sender. Here we report the most recent results of our optical fiber experiment in which we have performed quantum key distribution over a 48-km optical fiber network at Los Alamos using photon interference states with the B92 and BB84 quantum key distribution protocols.Comment: 13 pages, 7 figures, .pdf format submitted to Journal of Modern Optic