93,274 research outputs found
Neural-Symbolic Recommendation with Graph-Enhanced Information
The recommendation system is not only a problem of inductive statistics from
data but also a cognitive task that requires reasoning ability. The most
advanced graph neural networks have been widely used in recommendation systems
because they can capture implicit structured information from graph-structured
data. However, like most neural network algorithms, they only learn matching
patterns from a perception perspective. Some researchers use user behavior for
logic reasoning to achieve recommendation prediction from the perspective of
cognitive reasoning, but this kind of reasoning is a local one and ignores
implicit information on a global scale. In this work, we combine the advantages
of graph neural networks and propositional logic operations to construct a
neuro-symbolic recommendation model with both global implicit reasoning ability
and local explicit logic reasoning ability. We first build an item-item graph
based on the principle of adjacent interaction and use graph neural networks to
capture implicit information in global data. Then we transform user behavior
into propositional logic expressions to achieve recommendations from the
perspective of cognitive reasoning. Extensive experiments on five public
datasets show that our proposed model outperforms several state-of-the-art
methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc
A New Phase Transition for Local Delays in MANETs
We consider Mobile Ad-hoc Network (MANET) with transmitters located according
to a Poisson point in the Euclidean plane, slotted Aloha Medium Access (MAC)
protocol and the so-called outage scenario, where a successful transmission
requires a Signal-to-Interference-and-Noise (SINR) larger than some threshold.
We analyze the local delays in such a network, namely the number of times slots
required for nodes to transmit a packet to their prescribed next-hop receivers.
The analysis depends very much on the receiver scenario and on the variability
of the fading. In most cases, each node has finite-mean geometric random delay
and thus a positive next hop throughput. However, the spatial (or large
population) averaging of these individual finite mean-delays leads to infinite
values in several practical cases, including the Rayleigh fading and positive
thermal noise case. In some cases it exhibits an interesting phase transition
phenomenon where the spatial average is finite when certain model parameters
are below a threshold and infinite above. We call this phenomenon, contention
phase transition. We argue that the spatial average of the mean local delays is
infinite primarily because of the outage logic, where one transmits full
packets at time slots when the receiver is covered at the required SINR and
where one wastes all the other time slots. This results in the "RESTART"
mechanism, which in turn explains why we have infinite spatial average.
Adaptive coding offers a nice way of breaking the outage/RESTART logic. We show
examples where the average delays are finite in the adaptive coding case,
whereas they are infinite in the outage case.Comment: accepted for IEEE Infocom 201
Approximate Quantum Fourier Transform and Decoherence
We discuss the advantages of using the approximate quantum Fourier transform
(AQFT) in algorithms which involve periodicity estimations. We analyse quantum
networks performing AQFT in the presence of decoherence and show that extensive
approximations can be made before the accuracy of AQFT (as compared with
regular quantum Fourier transform) is compromised. We show that for some
computations an approximation may imply a better performance.Comment: 14 pages, 10 fig. (8 *.eps files). More information on
http://eve.physics.ox.ac.uk/QChome.html
http://www.physics.helsinki.fi/~kasuomin
http://www.physics.helsinki.fi/~kira/group.htm
Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence
This paper presents a educational workshop in Scratch that is proposed for
the active participation of undergraduate students in contexts of Artificial
Intelligence. The main objective of the activity is to demystify the complexity
of Artificial Intelligence and its algorithms. For this purpose, students must
realize simple exercises of clustering and two neural networks, in Scratch. The
detailed methodology to get that is presented in the article.Comment: 6 pages, 7 figures, workshop presentatio
Neural networks for calibration tomography
Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data
How to help intelligent systems with different uncertainty representations cooperate with each other
In order to solve a complicated problem one must use the knowledge from different domains. Therefore, if one wants to automatize the solution of these problems, one has to help the knowledge-based systems that correspond to these domains cooperate, that is, communicate facts and conclusions to each other in the process of decision making. One of the main obstacles to such cooperation is the fact that different intelligent systems use different methods of knowledge acquisition and different methods and formalisms for uncertainty representation. So an interface f is needed, 'translating' the values x, y, which represent uncertainty of the experts' knowledge in one system, into the values f(x), f(y) appropriate for another one. The problem of designing such an interface as a mathematical problem is formulated and solved. It is shown that the interface must be fractionally linear: f(x) = (ax + b)/(cx + d)
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