4,045 research outputs found
Ball arithmetic
33 pagesThe Mathemagix project aims at the development of a ''computer analysis'' system, in which numerical computations can be done in a mathematically sound manner. A major challenge for such systems is to conceive algorithms which are both efficient, reliable and available at any working precision. In this paper, we survey several older and newer such algorithms. We mainly concentrate on the automatic and efficient computation of high quality error bounds, based on a variant of interval arithmetic which we like to call ''ball arithmetic''
Synthetic aperture radar/LANDSAT MSS image registration
Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint
Indirect measurement of flow of liquid pumped with pump packages
This paper discusses some research results for indirect measurement methods for pump packagesbased on their head-capacity curves. The paper shows the results of experimental research intoindirect measurement by approximation of vertical lift performance of a pump package with algebraicpolynomials of 2nd and 3rd order illustrating their applicability to solution of tasks pertaining toretrospective estimation of polynomial coefficients for the vertical lift performance approximation.Values of relative errors for indirect measurement of transferred liquid flow with such polynomialsare given
Advanced Centaur explicit guidance equation study Final report
Generalized equations and in-flight computer requirements for Centaur guidance and control and advanced mission plannin
Quantum Circuit Optimization of Arithmetic circuits using ZX Calculus
Quantum computing is an emerging technology in which quantum mechanical
properties are suitably utilized to perform certain compute-intensive
operations faster than classical computers. Quantum algorithms are designed as
a combination of quantum circuits that each require a large number of quantum
gates, which is a challenge considering the limited number of qubit resources
available in quantum computing systems. Our work proposes a technique to
optimize quantum arithmetic algorithms by reducing the hardware resources and
the number of qubits based on ZX calculus. We have utilised ZX calculus rewrite
rules for the optimization of fault-tolerant quantum multiplier circuits where
we are able to achieve a significant reduction in the number of ancilla bits
and T-gates as compared to the originally required numbers to achieve
fault-tolerance. Our work is the first step in the series of arithmetic circuit
optimization using graphical rewrite tools and it paves the way for advancing
the optimization of various complex quantum circuits and establishing the
potential for new applications of the same
Splines in Compressed Sensing
It is well understood that in any data acquisition system reduction in the amount of data reduces the time and energy, but the major trade-off here is the quality of outcome normally, lesser the amount of data sensed, lower the quality. Compressed Sensing (CS) allows a solution, for sampling below the Nyquist rate. The challenging problem of increasing the reconstruction quality with less number of samples from an unprocessed data set is addressed here by the use of representative coordinate selected from different orders of splines. We have made a detailed comparison with 10 orthogonal and 6 biorthogonal wavelets with two sets of data from MIT Arrhythmia database and our results prove that the Spline coordinates work better than the wavelets. The generation of two new types of splines such as exponential and double exponential are also briefed here .We believe that this is one of the very first attempts made in Compressed Sensing based ECG reconstruction problems using raw data.
Analogue neuromorphic systems.
This thesis addresses a new area of science and technology, that of neuromorphic
systems, namely the problems and prospects of analogue neuromorphic systems. The
subject is subdivided into three chapters.
Chapter 1 is an introduction. It formulates the oncoming problem of the creation
of highly computationally costly systems of nonlinear information processing (such as
artificial neural networks and artificial intelligence systems). It shows that an analogue
technology could make a vital contribution to the creation such systems. The basic principles
of creation of analogue neuromorphic systems are formulated. The importance
will be emphasised of the principle of orthogonality for future highly efficient complex
information processing systems.
Chapter 2 reviews the basics of neural and neuromorphic systems and informs on
the present situation in this field of research, including both experimental and theoretical
knowledge gained up-to-date. The chapter provides the necessary background for
correct interpretation of the results reported in Chapter 3 and for a realistic decision on
the direction for future work.
Chapter 3 describes my own experimental and computational results within the
framework of the subject, obtained at De Montfort University. These include: the
building of (i) Analogue Polynomial Approximator/lnterpolatoriExtrapolator, (ii) Synthesiser
of orthogonal functions, (iii) analogue real-time video filter (performing the
homomorphic filtration), (iv) Adaptive polynomial compensator of geometrical distortions
of CRT- monitors, (v) analogue parallel-learning neural network (backpropagation
algorithm).
Thus, this thesis makes a dual contribution to the chosen field: it summarises the
present knowledge on the possibility of utilising analogue technology in up-to-date and
future computational systems, and it reports new results within the framework of the
subject. The main conclusion is that due to its promising power characteristics, small
sizes and high tolerance to degradation, the analogue neuromorphic systems will playa
more and more important role in future computational systems (in particular in systems
of artificial intelligence)
Expressive variational quantum circuits provide inherent privacy in federated learning
Federated learning has emerged as a viable distributed solution to train
machine learning models without the actual need to share data with the central
aggregator. However, standard neural network-based federated learning models
have been shown to be susceptible to data leakage from the gradients shared
with the server. In this work, we introduce federated learning with variational
quantum circuit model built using expressive encoding maps coupled with
overparameterized ans\"atze. We show that expressive maps lead to inherent
privacy against gradient inversion attacks, while overparameterization ensures
model trainability. Our privacy framework centers on the complexity of solving
the system of high-degree multivariate Chebyshev polynomials generated by the
gradients of quantum circuit. We present compelling arguments highlighting the
inherent difficulty in solving these equations, both in exact and approximate
scenarios. Additionally, we delve into machine learning-based attack strategies
and establish a direct connection between overparameterization in the original
federated learning model and underparameterization in the attack model.
Furthermore, we provide numerical scaling arguments showcasing that
underparameterization of the expressive map in the attack model leads to the
loss landscape being swamped with exponentially many spurious local minima
points, thus making it extremely hard to realize a successful attack. This
provides a strong claim, for the first time, that the nature of quantum machine
learning models inherently helps prevent data leakage in federated learning.Comment: 24 pages, 13 figure
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