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Covering alignment problems arise from recent developments in genomics; so called pan-genome graphs are replacing reference genomes, and advances in haplotyping enable full content of diploid genomes to be used as basis of sequence analysis. In this paper, we show that the computational complexity will change for natural extensions of alignments to pan-genome representations and to diploid genomes. More broadly, our approach can also be seen as a minimal extension of sequence alignment to labelled directed acyclic graphs (labeled DAGs). Namely, we show that finding a covering alignment of two labeled DAGs is NP-hard even on binary alphabets. A covering alignment asks for two paths $R_1$ (red) and $G_1$ (green) in DAG $D_1$ and two paths $R_2$ (red) and $G_2$ (green) in DAG $D_2$ that cover the nodes of the graphs and maximize the sum of the global alignment scores: $\mathsf {as}(\mathsf {sp}(R_1),\mathsf {sp}(R_2))+\mathsf {as}(\mathsf {sp}(G_1),\mathsf {sp}(G_2))$ , where $\mathsf {sp}(P)$ is the concatenation of labels on the path $P$ . Pair-wise alignment of haplotype sequences forming a diploid chromosome can be converted to a two-path coverable labelled DAG, and then the covering alignment models the similarity of two diploids over arbitrary recombinations. We also give a reduction to the other direction, to show that such a recombination-oblivious diploid alignment is NP-hard on alphabets of size 3. [ABSTRACT FROM AUTHOR]

The amazing proliferation of communication technologies for embedded systems opens the way for completely new applications but forces designers to adopt new methodologies to meet time-to-market constraints. Computer-Aided Design (CAD) has been traditionally applied to computers and embedded systems in isolation without considering them as a global inter-connected system. The paper contributes to fill this gap by proposing 1) a communication-aware design flow for network-interconnected embedded systems and 2) a formal framework to efficiently synthesize their network aspects by formulating and solving an optimization problem. Presented case studies show the potentiality of the proposed approach to address heterogeneous scenarios, e.g., related to smart spaces up to the ever-more-mentioned Internet-of-Things. [ABSTRACT FROM AUTHOR]

RNA-Seq technology offers new high-throughput ways for transcript identification and quantification based on short reads, and has recently attracted great interest. This is achieved by constructing a weighted DAG whose vertices stand for exons, and whose arcs stand for split alignments of the RNA-Seq reads to the exons. The task consists of finding a number of paths, together with their expression levels, which optimally explain the weights of the graph under various fitting functions, such as least sum of squared residuals. In (Tomescu et al. BMC Bioinformatics, 2013) we studied this genome-guided multi-assembly problem when the number of allowed solution paths was linear in the number of arcs. In this paper, we further refine this problem by asking for a bounded number $k$ of solution paths, which is the setting of most practical interest. We formulate this problem in very broad terms, and show that for many choices of the fitting function it becomes NP-hard. Nevertheless, we identify a natural graph parameter of a DAG $G$ , which we call arc-width and denote $\langle G\rangle$ , and give a dynamic programming algorithm running in time $O(W^k\langle G\rangle ^k(\langle G\rangle + k)n)$ , where $n$ is the number of vertices and $W$ is the maximum weight of $G$ . This implies that the problem is fixed-parameter tractable (FPT) in the parameters $W$ , $\langle G\rangle$ , and $k$ . We also show that the arc-width of DAGs constructed from simulated and real RNA-Seq reads is small in practice. Finally, we study the approximability of this problem, and, in particular, give a fully polynomial-time approximation scheme (FPTAS) for the case when the fitting function penalizes the maximum ratio between the weights of the arcs and their predicted coverage. [ABSTRACT FROM PUBLISHER]

In this paper, we define a new problem that has not been addressed in the past: the trade-off between energy efficiency and throughput for multicast services in 802.16e or similar mobile networks. In such networks, the mobile host can reduce its energy consumption by entering the sleep mode when it is not supposed to receive or transmit information. For unicast applications, the trade-off between delay and energy efficiency has been extensively researched. However, for mobile hosts running multicast (usually push-based) applications, it is much more difficult to determine when data should be transmitted by the base station and when each host should enter the sleep mode. In order to maximize the channel throughput while limiting the energy consumption, a group of hosts needing similar data items should be active during the same time intervals. We define this as an optimization problem and present several algorithms for it. We show that the most efficient solution is the one that employs cross-layer optimization by dividing the hosts into groups according to the quality of their downlink physical (PHY) channels. [ABSTRACT FROM AUTHOR]

This paper establishes the state of the art in both deterministic and randomized online permutation routing in the POPS network. Indeed, we show that any permutation can be routed online on a POPS(d, g) network either with O(d/g log g) deterministic slots, or, with high probability, with 5c[d/g] + o(d/g) + O(log log g) randomized slots, where constant c = exp(1 + e-1) ≈ 3.927. When d = ϴ(g), which we claim to be the ‘interesting’ case, the randomized algorithm is exponentially faster than any other algorithm in the literature, both deterministic and randomized ones. This is true in practice as well. Indeed, experiments show that it outperforms its rivals even starting from as small a network as a POPS(2, 2) and the gap grows exponentially with the size of the network. We can also show that, under proper hypothesis, no deterministic algorithm can asymptotically match its performance. [ABSTRACT FROM AUTHOR]

A ‘simulation program with integrated circuit emphasis’ (SPICE) model for the evaluation, in frequency and time domain, of the coupling of a bundle of shielded coaxial cables with an external electromagnetic source such as a bulk current injection clamp, is developed. The proposed equivalent circuit takes into account the presence of the transfer impedance and the presence of the transfer admittance, and it does not need the subdivision in elementary cells. Intermediate results are validated by comparing the exact numerical solutions with the output of the SPICE circuit, and the final results are validated by means of comparison with measurements carried out on an experimental setup. The feature selective validation (FSV) technique is used as a measure of the comparisons. [ABSTRACT FROM AUTHOR]

This paper shows that an n = 2k processor Partitioned Optical Passive Stars (POPS) network with g groups and d processors per group can simulate every bidirectional move of an n processor hypercube using one slot when d < g, two slots when d = g, and [d/g] slots when d > g. Moreover, the same POPS network can simulate every monodirectional move of an n processor hypercube using one slot when d = g. All these results are shown to be optimal. Our simulations improve on the literature whenever d ≠ g and directly yield several important consequences. For example, as a direct consequence of our simulations, a POPS network, n = dg and d < g, can compute the prefix sums of n data values in log2 n slots. This is faster than the best previously known ad hoc algorithm and is actually optimal. Similarly, we improve on the best POPS network algorithms for both the prefix sums problem on general POPS networks and the fundamental online permutation routing problem, among others. [ABSTRACT FROM AUTHOR]

Broadcast is an efficient and scalable way of transmitting data to an unlimited number of clients that are listening to a channel. Cyclically broadcasting data over the channel is a basic scheduling technique, which is known as flat scheduling. When multiple channels are available, a data allocation technique is needed to assign data to channels. Partitioning data among channels in an unbalanced way, depending on data popularities, is an allocation technique known as skewed allocation. In this paper, the problem of data broadcasting over multiple channels is considered, assuming skewed data allocation to channels and flat data scheduling per channel, with the objective of minimizing the average waiting time of the clients. First, several algorithms, based on dynamic programming, are presented which provide optimal solutions for N data items and K channels. Specifically, for data items with uniform lengths, an 0(NK log N) time algorithm is proposed, which improves over the previously known 0(N2K) time algorithm. When K ≤ 4, a simpler 0(NlogN) time algorithm is exhibited which requires only 0(N) time if the data items are sorted. Moreover, for data items with nonuniform lengths, it is shown that the problem is NP-hard when K = 2 and strong NP-hard for arbitrary K. In the former case, a pseudopolynomial algorithm is discussed whose time is O(NZ), where Z is the sum of the data lengths. In the latter case, an algorithm is devised with time exponential in the maximum data length, which can optimally solve, in reasonable time, only small instances. For larger instances, a new heuristic is devised which is experimentally tested on some benchmarks whose popularities are characterized by Zipf distributions. Such experimental tests reveal that the new heuristic proposed here always outperforms the best previously known heuristic in terms of solution quality. [ABSTRACT FROM AUTHOR]

The haplotype resolution from xor-genotype data has been recently formulated as a new model for genetic studies [1]. The xor-genotype data is a cheaply obtainable type of data distinguishing heterozygous from homozygous sites without identifying the homozygous alleles. In this paper, we propose a formulation based on a well-known model used in haplotype inference: pure parsimony. We exhibit exact solutions of the problem by providing polynomial time algorithms for some restricted cases and a fixed-parameter algorithm for the general case. These results are based on some interesting combinatorial properties of a graph representation of the solutions. Furthermore, we show that the problem has a polynomial time k-approximation, where k is the maximum number of xor-genotypes containing a given single nucleotide polymorphisms (SNP). Finally, we propose a heuristic and produce an experimental analysis showing that it scales to real-world large instances taken from the HapMap project. [ABSTRACT FROM PUBLISHER]