487 research outputs found
The application of a sparse, distributed memory to the detection, identification and manipulation of physical objects
To determine the relation of the sparse, distributed memory to other architectures, a broad review of the literature was made. The memory is called a pattern memory because they work with large patterns of features (high-dimensional vectors). A pattern is stored in a pattern memory by distributing it over a large number of storage elements and by superimposing it over other stored patterns. A pattern is retrieved by mathematical or statistical reconstruction from the distributed elements. Three pattern memories are discussed
Task-Projected Hyperdimensional Computing for Multi-Task Learning
Brain-inspired Hyperdimensional (HD) computing is an emerging technique for
cognitive tasks in the field of low-power design. As a fast-learning and
energy-efficient computational paradigm, HD computing has shown great success
in many real-world applications. However, an HD model incrementally trained on
multiple tasks suffers from the negative impacts of catastrophic forgetting.
The model forgets the knowledge learned from previous tasks and only focuses on
the current one. To the best of our knowledge, no study has been conducted to
investigate the feasibility of applying multi-task learning to HD computing. In
this paper, we propose Task-Projected Hyperdimensional Computing (TP-HDC) to
make the HD model simultaneously support multiple tasks by exploiting the
redundant dimensionality in the hyperspace. To mitigate the interferences
between different tasks, we project each task into a separate subspace for
learning. Compared with the baseline method, our approach efficiently utilizes
the unused capacity in the hyperspace and shows a 12.8% improvement in averaged
accuracy with negligible memory overhead.Comment: To be published in 16th International Conference on Artificial
Intelligence Applications and Innovation
The Emergence of Miller's Magic Number on a Sparse Distributed Memory
Human memory is limited in the number of items held in one's mindâa limit known as âMiller's magic numberâ. We study the emergence of such limits as a result of the statistics of large bitvectors used to represent items in memory, given two postulates: i) the Sparse Distributed Memory; and ii) chunking through averaging. Potential implications for theoretical neuroscience are discussed
Geometric Algebra Model of Distributed Representations
Formalism based on GA is an alternative to distributed representation models
developed so far --- Smolensky's tensor product, Holographic Reduced
Representations (HRR) and Binary Spatter Code (BSC). Convolutions are replaced
by geometric products, interpretable in terms of geometry which seems to be the
most natural language for visualization of higher concepts. This paper recalls
the main ideas behind the GA model and investigates recognition test results
using both inner product and a clipped version of matrix representation. The
influence of accidental blade equality on recognition is also studied. Finally,
the efficiency of the GA model is compared to that of previously developed
models.Comment: 30 pages, 19 figure
White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures
Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many
challenges due to the complex and highly dynamic nature of the environment.
Common approaches consider service composition as a decision problem whose
solution is usually addressed from optimization perspectives which are not
feasible in practice due to the intractability of the problem, limited
computational resources of smart devices, service host's mobility, and time
constraints to tailor composition plans. Thus, our main contribution is the
development of a cognitively-inspired agent-based service composition model
focused on bounded rationality rather than optimality, which allows the system
to compensate for limited resources by selectively filtering out continuous
streams of data. Our approach exhibits features such as distributedness,
modularity, emergent global functionality, and robustness, which endow it with
capabilities to perform decentralized service composition by orchestrating
manifold service providers and conflicting goals from multiple users. The
evaluation of our approach shows promising results when compared against
state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on
Artificial Intelligence and Mobile Services) on June 2
Incremental dimension reduction of tensors with random index
We present an incremental, scalable and efficient dimension reduction
technique for tensors that is based on sparse random linear coding. Data is
stored in a compactified representation with fixed size, which makes memory
requirements low and predictable. Component encoding and decoding are performed
on-line without computationally expensive re-analysis of the data set. The
range of tensor indices can be extended dynamically without modifying the
component representation. This idea originates from a mathematical model of
semantic memory and a method known as random indexing in natural language
processing. We generalize the random-indexing algorithm to tensors and present
signal-to-noise-ratio simulations for representations of vectors and matrices.
We present also a mathematical analysis of the approximate orthogonality of
high-dimensional ternary vectors, which is a property that underpins this and
other similar random-coding approaches to dimension reduction. To further
demonstrate the properties of random indexing we present results of a synonym
identification task. The method presented here has some similarities with
random projection and Tucker decomposition, but it performs well at high
dimensionality only (n>10^3). Random indexing is useful for a range of complex
practical problems, e.g., in natural language processing, data mining, pattern
recognition, event detection, graph searching and search engines. Prototype
software is provided. It supports encoding and decoding of tensors of order >=
1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure
Neonatal Intestinal Failure Is Independently Associated With Impaired Cognitive Development Later in Childhood
Objective: The impact of pediatric intestinal failure (IF) on neurodevelopment beyond infancy has not been systematically studied. Our aim was to evaluate cognitive and motor impairment and to identify risk factors for adverse outcomes among children with IF. Methods: We conducted a cross-sectional single-center study at the Helsinki University Children's Hospital. Patients with IF with >60 days of parental nutrition (PN) dependency aged between 3 and 16 years (n = 40) were invited to participate. The cognitive and motor skills were evaluated using validated tests: Wechsler Preschool and Primary Scale of Intelligence, 3rd edition, Wechsler Intelligence Scale for Children, 4th edition, and Movement Assessment Battery for Children, 2nd edition. Results: All the patients attending the study tests (n = 30, males = 24) were included. Their median age, gestational age, and birth weight was 7.5 (range 3-16) years, 35 (interquartile range [IQR] 28-38) weeks and 2238 (IQR 1040-3288) grams, respectively. Median duration of PN was 13 (IQR 5-37) months and 9 patients were currently on PN. Median intelligence quotient was 78 (IQR 65-91) and 10 (35%) patients had an intelligence quotient under 70 (-2 standard deviation). Significant motor impairment was detected in 10 patients (36%) and milder difficulties in 8 (28%). Adverse cognitive outcome was associated with neonatal short bowel syndrome, number of interventions under general anesthesia, and length of inpatient status, whereas adverse motor outcome was associated with prematurity. Conclusion: Clinically significant cognitive and motor impairments are alarmingly common among neonatal patients with IF. We recommend early neurodevelopmental follow-up for all children with IF.Peer reviewe
Patterning of Heteroepitaxial Overlayers from Nano to Micron Scales
Thin heteroepitaxial overlayers have been proposed as templates to generate stable, self-organized nanostructures at large length scales, with a variety of important technological applications. However, modeling strain-driven self-organization is a formidable challenge due to different length scales involved. In this Letter, we present a method for predicting the patterning of ultrathin films on micron length scales with atomic resolution. We make quantitative predictions for the type of superstructures (stripes, honeycomb, triangular) and length scale of pattern formation of two metal-metal systems, Cu on Ru(0001) and Cu on Pd(111). Our findings are in excellent agreement with previous experiments and call for future experimental investigations of such systems.Peer reviewe
Cartoon Computation: Quantum-like computing without quantum mechanics
We present a computational framework based on geometric structures. No
quantum mechanics is involved, and yet the algorithms perform tasks analogous
to quantum computation. Tensor products and entangled states are not needed --
they are replaced by sets of basic shapes. To test the formalism we solve in
geometric terms the Deutsch-Jozsa problem, historically the first example that
demonstrated the potential power of quantum computation. Each step of the
algorithm has a clear geometric interpetation and allows for a cartoon
representation.Comment: version accepted in J. Phys.A (Letter to the Editor
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