Flow Decomposition With Subpath Constraints

Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e., decompose the flow into the original paths and their weights. Thus, variations on flow decomposition are often used as subroutines in multiassembly problems such as RNA transcript assembly. In practice, we frequently have access to information beyond flow values in the form of subpaths, and many tools incorporate these heuristically. But despite acknowledging their utility in practice, previous work has not formally addressed the effect of subpath constraints on the accuracy of flow network decomposition approaches. We formalize the flow decomposition with subpath constraints problem, give the first algorithms for it, and study its usefulness for recovering ground truth decompositions. For finding a minimum decomposition, we propose both a heuristic and an FPTalgorithm. Experiments on RNA transcript datasets show that for instances with larger solution path sets, the addition of subpath constraints finds 13% more ground truth solutions when minimal decompositions are found exactly, and 30% more ground truth solutions when minimal decompositions are found heuristically.Peer reviewe

SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.Comment: Full versio

SNACKs: Leveraging Proofs of Sequential Work for Blockchain Light Clients

The success of blockchains has led to ever-growing ledgers that are stored by all participating full nodes. In contrast, light clients only store small amounts of blockchain-related data and rely on the mediation of full nodes when interacting with the ledger. A broader adoption of blockchains calls for protocols that make this interaction trustless. We revisit the design of light-client blockchain protocols from the perspective of classical proof-system theory, and explain the role that proofs of sequential work (PoSWs) can play in it. To this end, we define a new primitive called succinct non-interactive argument of chain knowledge (SNACK), a non-interactive proof system that provides clear security guarantees to a verifier (a light client) even when interacting only with a single dishonest prover (a full node). We show how augmenting any blockchain with any graph-labeling PoSW (GL-PoSW) enables SNACK proofs for this blockchain. We also provide a unified and extended definition of GL-PoSWs covering all existing constructions, and describe two new variants. We then show how SNACKs can be used to construct light-client protocols, and highlight some deficiencies of existing designs, along with mitigations. Finally, we introduce incremental SNACKs which could provide a new approach to light mining

Lifetime and latency aware data collection in wireless sensor networks

A Wireless Sensor Network (WSN) consists of a set of sensor nodes deployed in the environment where we intend to collect physical information such as temperatures. All the senor nodes are connected wirelessly, and work cooperatively to fulfill some specified tasks. Sensor nodes are typically battery powered. As a result, the network lifetime becomes a major optimization objective in the design of a WSN. Another important optimisation objective is to minimize the maximum latency of data collection for time-critical applications. In this thesis, we study the problem of lifetime and latency aware data collection in a static WSN with only one base station. We propose two novel routing structures, namely, k-tree and k-DAG, to balance the loads of the neighbouring sensor nodes of the base station to prolong the lifetime of the network while providing the maximum latency guarantee. Firstly, we investigate the lifetime aware data collection problem by using ktree. A k-tree is a spanning tree with the base station as the root such that the path from each sensor node to the base station is at most k hops longer than the shortest path from this sensor node to the base station. We propose a distributed algorithm for constructing a k-tree such that the loads of the base station s children are balanced. Secondly, we study the lifetime aware data collection problem by using k-DAG. A k-DAG is a spanning Directed Acyclic Graph (DAG) with the base station as the only source node such that the path length of any path from each sensor node to the base station is not k hops longer than its shortest path length to the base station. We present a distributed algorithm for constructing a k-DAG such that the loads of the base station s children are balanced. In addition, we propose an efficient distributed naming scheme to assign a unique ID to each sensor node for efficient point-to-point communication. We have implemented all of our algorithms by Cooja simulator. The simulation results show that our approaches significantly increase the network lifetime by up to 82%

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM