Miracle’s 2005 Approach to Monolingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Monolingual Information Retrieval. The goal for the experiments in this year was twofold: continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extracting, paragraph extracting, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query

A Functional Architecture Approach to Neural Systems

The technology for the design of systems to perform extremely complex combinations of real-time functionality has developed over a long period. This technology is based on the use of a hardware architecture with a physical separation into memory and processing, and a software architecture which divides functionality into a disciplined hierarchy of software components which exchange unambiguous information. This technology experiences difficulty in design of systems to perform parallel processing, and extreme difficulty in design of systems which can heuristically change their own functionality. These limitations derive from the approach to information exchange between functional components. A design approach in which functional components can exchange ambiguous information leads to systems with the recommendation architecture which are less subject to these limitations. Biological brains have been constrained by natural pressures to adopt functional architectures with this different information exchange approach. Neural networks have not made a complete shift to use of ambiguous information, and do not address adequate management of context for ambiguous information exchange between modules. As a result such networks cannot be scaled to complex functionality. Simulations of systems with the recommendation architecture demonstrate the capability to heuristically organize to perform complex functionality

Multilingual manager: a new strategic role in organizations

Today?s knowledge management (KM) systems seldom account for language management and, especially, multilingual information processing. Document management is one of the strongest components of KM systems. If these systems do not include a multilingual knowledge management policy, intranet searches, excessive document space occupancy and redundant information slow down what are the most effective processes in a single language environment. In this paper, we model information flow from the sources of knowledge to the persons/systems searching for specific information. Within this framework, we focus on the importance of multilingual information processing, which is a hugely complex component of modern organizations

Designer cell signal processing circuits for biotechnology

Microorganisms are able to respond effectively to diverse signals from their environment and internal metabolism owing to their inherent sophisticated information processing capacity. A central aim of synthetic biology is to control and reprogramme the signal processing pathways within living cells so as to realise repurposed, beneficial applications ranging from disease diagnosis and environmental sensing to chemical bioproduction. To date most examples of synthetic biological signal processing have been built based on digital information flow, though analogue computing is being developed to cope with more complex operations and larger sets of variables. Great progress has been made in expanding the categories of characterised biological components that can be used for cellular signal manipulation, thereby allowing synthetic biologists to more rationally programme increasingly complex behaviours into living cells. Here we present a current overview of the components and strategies that exist for designer cell signal processing and decision making, discuss how these have been implemented in prototype systems for therapeutic, environmental, and industrial biotechnological applications, and examine emerging challenges in this promising field

Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Generation and sampling of quantum states of light in a silicon chip

Implementing large instances of quantum algorithms requires the processing of many quantum information carriers in a hardware platform that supports the integration of different components. While established semiconductor fabrication processes can integrate many photonic components, the generation and algorithmic processing of many photons has been a bottleneck in integrated photonics. Here we report the on-chip generation and processing of quantum states of light with up to eight photons in quantum sampling algorithms. Switching between different optical pumping regimes, we implement the Scattershot, Gaussian and standard boson sampling protocols in the same silicon chip, which integrates linear and nonlinear photonic circuitry. We use these results to benchmark a quantum algorithm for calculating molecular vibronic spectra. Our techniques can be readily scaled for the on-chip implementation of specialised quantum algorithms with tens of photons, pointing the way to efficiency advantages over conventional computers