An associative memory for the on-line recognition and prediction of temporal sequences

This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been proposed, and different sequence learning models have been analysed according to this framework. The network model is an associative memory with a separate store for the sequence context of a symbol. A sparse distributed memory is used to gain scalability. The context store combines the functionality of a neural layer with a shift register. The sensitivity of the machine to the sequence context is controllable, resulting in different characteristic behaviours. The model can store and predict on-line sequences of various types and length. Numerical simulations on the model have been carried out to determine its properties.Comment: Published in IJCNN 2005, Montreal, Canad

Online Matrix Completion Through Nuclear Norm Regularisation

It is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommender systems to sensor network localization through seismic data reconstruction, we consider the matrix completion problem when entries of the matrix of interest are observed gradually. Precisely, we place ourselves in the situation where the predictive rule should be refined incrementally, rather than recomputed from scratch each time the sample of observed entries increases. The extension of existing matrix completion methods to the sequential prediction context is indeed a major issue in the Big Data era, and yet little addressed in the literature. The algorithm promoted in this article builds upon the Soft Impute approach introduced in Mazumder et al. (2010). The major novelty essentially arises from the use of a randomised technique for both computing and updating the Singular Value Decomposition (SVD) involved in the algorithm. Though of disarming simplicity, the method proposed turns out to be very efficient, while requiring reduced computations. Several numerical experiments based on real datasets illustrating its performance are displayed, together with preliminary results giving it a theoretical basis.Comment: Corrected a typo in the affiliatio

Stochastic model of transcription factor-regulated gene expression

We consider a stochastic model of transcription factor (TF)-regulated gene expression. The model describes two genes: Gene A and Gene B which synthesize the TFs and the target gene proteins respectively. We show through analytic calculations that the TF fluctuations have a significant effect on the distribution of the target gene protein levels when the mean TF level falls in the highest sensitive region of the dose-response curve. We further study the effect of reducing the copy number of Gene A from two to one. The enhanced TF fluctuations yield results different from those in the deterministic case. The probability that the target gene protein level exceeds a threshold value is calculated with a knowledge of the probability density functions associated with the TF and target gene protein levels. Numerical simulation results for a more detailed stochastic model are shown to be in agreement with those obtained through analytic calculations. The relevance of these results in the context of the genetic disorder haploinsufficiency is pointed out. Some experimental observations on the haploinsufficiency of the tumour suppressor gene, Nkx3.1, are explained with the help of the stochastic model of TF-regulated gene expression.Comment: 17 pages, 11 figures. Accepted for publication in Physical Biolog

Detection of gravitational waves from inspiraling compact binaries using a network of interferometric detectors

We formulate the data analysis problem for the detection of the Newtonian waveform from an inspiraling compact-binary by a network of arbitrarily oriented and arbitrarily distributed laser interferometric gravitational wave detectors. We obtain for the first time the relation between the optimal statistic and the magnitude of the network correlation vector, which is constructed from the matched network-filter. This generalizes the calculation reported in an earlier work (gr-qc/9906064), where the detectors are taken to be coincident.Comment: 6 pages, RevTeX. Based on talk given at GWDAW-99, Rom

Aluminium foil for electrical windings

COMPAREL to copper, aluminium is far more abundant. This Is particularly true for India. The price of aluminium unlike that of copper has been quite stable for over a quarter century and for equal conductivity, the current price of aluminium is very considerably lower than that of copper. In view of its abundance coupled with versatile properties and relatively stable and competit-ive price, aluminium has rapidly established itself in various fields and has, in particular, replaced copper in many cases. Aluminium has firmly established itself as an electrical conductor. Today approximately 97% of high voltage transmissioncables and about 75% of distribution cables are made of aluminium. During recent years alumi-nium has also made effective inroad into the bus bar field

Differential Interleukin-2 Transcription Kinetics Render Mouse but Not Human T Cells Vulnerable to Splicing Inhibition Early after Activation

T cells are nodal players in the adaptive immune response against pathogens and malignant cells. Alternative splicing plays a crucial role in T cell activation, which is analyzed mainly at later time points upon stimulation. Here we have discovered a 2-h time window early after stimulation where optimal splicing efficiency or, more generally, gene expression efficiency is crucial for successful T cell activation. Reducing the splicing efficiency at 4 to 6 h poststimulation significantly impaired murine T cell activation, which was dependent on the expression dynamics of the Egr1-Nab2-interleukin-2 (IL-2) pathway. This time window overlaps the time of peak IL-2 de novo transcription, which, we suggest, represents a permissive time window in which decreased splicing (or transcription) efficiency reduces mature IL-2 production, thereby hampering murine T cell activation. Notably, the distinct expression kinetics of the Egr1-Nab2-IL-2 pathway between mouse and human render human T cells refractory to this vulnerability. We propose that the rational temporal modulation of splicing or transcription during peak de novo expression of key effectors can be used to fine-tune stimulation-dependent biological outcomes. Our data also show that critical consideration is required when extrapolating mouse data to the human system in basic and translational research

Quantum Liang Information Flow as Causation Quantifier

Liang information flow is widely used in classical systems and network theory for causality quantification and has been applied widely, for example, to finance, neuroscience, and climate studies. The key part of the theory is to freeze a node of a network to ascertain its causal influence on other nodes. Such a theory is yet to be applied to quantum network dynamics. Here, we generalize the Liang information flow to the quantum domain with respect to von Neumann entropy and exemplify its usage by applying it to a variety of small quantum networks