No abstract

In this paper, a single-hop single-relay system with a direct link between the source and the destination is considered when the relay operates in the half-duplex mode. Motivated by the concept of signal space diversity, this paper introduces signal space cooperation, in which cooperation between the source and the relay is achieved using a novel constellation design approach. In this approach, the original constellation is expanded so that each member of the new constellation inherits its components from at least two members of the original constellation. The expanded constellation enables the relay to extract the required information in order to cooperate in the relay phase, and it helps the destination to effectively combine received signals during the broadcast phase and the relay phase. The analytical study of the proposed scheme leads to the development of two design criteria for the constellation expansion approach. The proposed design criteria aim at maximizing the relay role in the cooperative scheme by increasing the performance of the source-relay link. In this way, the source and the relay can effectively cooperate in order to maximize the overall performance of the system. The signal space cooperative scheme can be used for any constellation size without incurring significant complexity overhead to the system. Numerical results depict superior performance in comparison with other cooperative schemes such as the adaptive decode and forward and the distributed turbo code schemes.

No abstract

No abstract

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure, while spending a small budget on communication. Index Terms-Structure learning, Chow-Liu algorithm, Gaussian Graphical Model.!

Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals regarding their lifetime secret is violated. In this paper, we introduce the problem of private DNA sequencing, where the goal is to keep the DNA sequence private to the sequencer. We propose an architecture, where the task of reading fragments of DNA and the task of DNA assembly are separated, the former is done at the sequencer(s), and the later is completed at a local trusted data collector. To satisfy the privacy constraint at the sequencer and reconstruction condition at the data collector, we create an information gap between these two relying on two techniques: (i) we use more than one non-colluding sequencer, all reporting the read fragments to the single data collector, (ii) adding the fragments of some known DNA molecules, which are still unknown to the sequencers, to the pool. We prove that these two techniques provide enough freedom to satisfy both conditions at the same time.

Background: Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. Results: We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates statistics from reads and the reference genome automatically. Meta-aligner is implemented in C++ and runs in popular POSIX-like operating systems such as Linux. Conclusions: Meta-aligner achieves high recall rates and precisions especially for long reads and high error rates. Also, it improves performance of alignment in the case of PacBio long-reads in comparison with traditional schemes.

No abstract