21,124 research outputs found
Multisensory causal inference in the brain
At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions
Towards joint decoding of binary Tardos fingerprinting codes
The class of joint decoder of probabilistic fingerprinting codes is of utmost
importance in theoretical papers to establish the concept of fingerprint
capacity. However, no implementation supporting a large user base is known to
date. This article presents an iterative decoder which is, as far as we are
aware of, the first practical attempt towards joint decoding. The
discriminative feature of the scores benefits on one hand from the
side-information of previously accused users, and on the other hand, from
recently introduced universal linear decoders for compound channels. Neither
the code construction nor the decoder make precise assumptions about the
collusion (size or strategy). The extension to incorporate soft outputs from
the watermarking layer is straightforward. An extensive experimental work
benchmarks the very good performance and offers a clear comparison with
previous state-of-the-art decoders.Comment: submitted to IEEE Trans. on Information Forensics and Security. -
typos corrected, one new plot, references added about ECC based
fingerprinting code
Optimal sequential fingerprinting: Wald vs. Tardos
We study sequential collusion-resistant fingerprinting, where the
fingerprinting code is generated in advance but accusations may be made between
rounds, and show that in this setting both the dynamic Tardos scheme and
schemes building upon Wald's sequential probability ratio test (SPRT) are
asymptotically optimal. We further compare these two approaches to sequential
fingerprinting, highlighting differences between the two schemes. Based on
these differences, we argue that Wald's scheme should in general be preferred
over the dynamic Tardos scheme, even though both schemes have their merits. As
a side result, we derive an optimal sequential group testing method for the
classical model, which can easily be generalized to different group testing
models.Comment: 12 pages, 10 figure
Receivers for Diffusion-Based Molecular Communication: Exploiting Memory and Sampling Rate
In this paper, a diffusion-based molecular communication channel between two
nano-machines is considered. The effect of the amount of memory on performance
is characterized, and a simple memory-limited decoder is proposed and its
performance is shown to be close to that of the best possible imaginable
decoder (without any restriction on the computational complexity or its
functional form), using Genie-aided upper bounds. This effect is specialized
for the case of Molecular Concentration Shift Keying; it is shown that a
four-bits memory achieved nearly the same performance as infinite memory. Then
a general class of threshold decoders is considered and shown not to be optimal
for Poisson channel with memory, unless SNR is higher than a value specified in
the paper. Another contribution is to show that receiver sampling at a rate
higher than the transmission rate, i.e., a multi-read system, can significantly
improve the performance. The associated decision rule for this system is shown
to be a weighted sum of the samples during each symbol interval. The
performance of the system is analyzed using the saddle point approximation. The
best performance gains are achieved for an oversampling factor of three.Comment: Submitted to JSA
- …