Transfer learning has proved to be effective for building predictive models even in complex conditions with a low amount of available labeled data, by constructing a predictive model for a target domain also using the...
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Transfer learning has proved to be effective for building predictive models even in complex conditions with a low amount of available labeled data, by constructing a predictive model for a target domain also using the knowledge coming from a separate domain, called source domain. However, several existing transfer learning methods assume identical feature spaces between the source and the target domains. This assumption limits the possible real -world applications of such methods, since two separate, although related, domains could be described by totally different feature spaces. Heterogeneous transfer learning methods aim to overcome this limitation, but they usually i) make other assumptions on the features, such as requiring the same number of features, ii) are not generally able to distribute the workload over multiple computational nodes, iii) cannot work in the Positive-Unlabeled (PU) learning setting, which we also considered in this study, or iv) their applicability is limited to specific application domains, i.e., they are not general-purpose methods. In this manuscript, we present a novel distributed heterogeneous transfer learning method, implemented in Apache Spark, that overcomes all the above-mentioned limitations. Specifically, it is able to work also in the PU learning setting by resorting to a clustering-based approach, and can align totally heterogeneous feature spaces, without exploiting peculiarities of specific application domains. Moreover, our distributed approach allows us to process large source and target datasets. Our experimental evaluation was performed in three different application domains that can benefit from transfer learning approaches, namely the reconstruction of the human gene regulatory network, the prediction of cerebral stroke in hospital patients, and the prediction of customer energy consumption in power grids. The results show that the proposed approach is able to outperform 4 state -of -the -art heterogeneous transfer le
The emergent behavior of a distributed system is conditioned by the information available to the local decision-makers. Therefore, one may expect that providing decision-makers with more information will improve syste...
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The emergent behavior of a distributed system is conditioned by the information available to the local decision-makers. Therefore, one may expect that providing decision-makers with more information will improve system performance;in this letter, we find that this is not necessarily the case. In multi-agent maximum coverage problems, we find that even when agents' objectives are aligned with the global welfare, informing agents about the realization of the resource's random values can reduce equilibrium performance by a factor of 1/2. This affirms an important aspect of designing distributed systems: information need be shared carefully. We further this understanding by providing lower and upper bounds on the ratio of system welfare when information is (fully or partially) revealed and when it is not, termed the value-of-informing. We then identify a trade-off that emerges when optimizing the performance of the best-case and worst-case equilibrium.
In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. W...
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ISBN:
(纸本)9783031814037;9783031814044
In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. We address this challenge on an anonymous triangular grid graph, where each vertex can connect to up to six adjacent vertices. We propose a distributed deterministic algorithm that achieves dispersion on an unoriented triangular grid graph in O(root n) time, where n is the number of vertices. Each robot requires O(log n) bits of memory. The time complexity of our algorithm and the memory usage per robot are optimal. This work builds on previous studies by Kshemkalyani et al. [WALCOM 2020 [17]] and Banerjee et al. [ALGOWIN 2024 [3]]. Importantly, our algorithm terminates without requiring prior knowledge of n and resolves a question posed by Banerjee et al. [ALGOWIN 2024 [3]].
In prior work, Gupta et al. (SPAA 2022) presented a distributed algorithm for multiplying sparse.. x.. matrices, using.. computers. They assumed that the input matrices are uniformly sparse-there are at most.. non-zer...
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ISBN:
(纸本)9798400704161
In prior work, Gupta et al. (SPAA 2022) presented a distributed algorithm for multiplying sparse.. x.. matrices, using.. computers. They assumed that the input matrices are uniformly sparse-there are at most.. non-zeros in each row and column-and the task is to compute a uniformly sparse part of the product matrix. Initially each computer knows one row of each input matrix, and eventually each computer needs to know one row of the product matrix. In each communication round each computer can send and receive one O(log n)-bit message. Their algorithm solves this task in O(d(1.907)) rounds, while the trivial bound is O(d(2)). We improve on the prior work in two dimensions: First, we show that we can solve the same task faster, in only O(d(1.832)) rounds. Second, we explore what happens when matrices are not uniformly sparse. We consider the following alternative notions of sparsity: row-sparse matrices (at most.. non-zeros per row), column-sparse matrices, matrices with bounded degeneracy (we can recursively delete a row or column with at most.. non-zeros), average-sparse matrices (at most dn non-zeros in total), and general matrices. We show that we can still compute X = AB in O(d(1.832)) rounds even if one of the three matrices (A, B, or X) is average-sparse instead of uniformly sparse. We present algorithms that handle a much broader range of sparsity in O(d(2) + log n) rounds, and present conditional hardness results that put limits on further improvements and generalizations.
The Hard-state Protocol Independent Multicast-Dense Mode (HPIM-DM) multicast routing protocol is proposed. HPIM-DM is a hard-state version of PIM-DM that overcomes its poor convergence times and lack of resilience to ...
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The Hard-state Protocol Independent Multicast-Dense Mode (HPIM-DM) multicast routing protocol is proposed. HPIM-DM is a hard-state version of PIM-DM that overcomes its poor convergence times and lack of resilience to replay attacks. Like PIM-DM, HPIM-DM is meant for dense networks and supports its operation on a unicast routing protocol and reverse path forwarding. However, routers maintain sense of the multicast trees at all times, allowing fast reconfiguration in the presence of network failures or unicast route changes. This is achieved by (i) keeping information on all upstream neighbours from which multicast data can be received, (ii) ensuring the reliable transmission and sequencing of control messages, and (iii) synchronizing the routing information immediately when a new router joins the network. The correctness of the protocol was extensively validated using model checking and logical reasoning. The protocol was fully implemented in Python, and the implementation is publicly available. Finally, we show both theoretically and experimentally that HPIM-DM has much better convergence times than PIM-DM.
Leader election is a critical and extensively studied problem in distributed computing. This paper introduces the study of leader election using mobile agents. Consider n agents initially placed arbitrarily on the nod...
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ISBN:
(纸本)9783031814037;9783031814044
Leader election is a critical and extensively studied problem in distributed computing. This paper introduces the study of leader election using mobile agents. Consider n agents initially placed arbitrarily on the nodes of an arbitrary, n-node, m-edge graph G. These agents move autonomously across the nodes of G and elect one agent as the leader such that the leader is aware of its status as the leader, and the other agents know they are not the leader. The goal is to minimize both time and memory usage. We study the leader election problem in a synchronous setting where each agent performs operations simultaneously with the others, allowing us to measure time complexity in terms of rounds. We assume that the agents have prior knowledge of the number of nodes n and the maximum degree of the graph Delta. We first elect a leader deterministically in O(n log(2) n + D Delta log n) rounds with each agent using O(log n) bits of memory, where D is the diameter of the graph. Leveraging this leader election result, we then present a deterministic algorithm for constructing a minimum spanning tree of G in O(m+ n log n) rounds, with each agent using O(Delta log n) bits of memory. Finally, using the same leader election result, we improve time and memory bounds for other key distributed graph problems, including gathering, maximal independent set, and minimal dominating set. For all the aforementioned problems, our algorithms remain memory-optimal.
We consider the problem of finding a maximum independent set (MaxIS) of chordal graphs using mobile agents. Suppose n agents are initially placed arbitrarily on the nodes of an n-node chordal graph G = (V, E). Agents ...
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ISBN:
(纸本)9783031814037;9783031814044
We consider the problem of finding a maximum independent set (MaxIS) of chordal graphs using mobile agents. Suppose n agents are initially placed arbitrarily on the nodes of an n-node chordal graph G = (V, E). Agents need to find a maximum independent set M of G such that each node of M is occupied by at least one agent. Also, each of the n agents must know whether its occupied node is a part of M or not. Starting from both rooted and arbitrary initial configuration, we provide distributed algorithms for n mobile agents having O(log n) memory each to compute the MaxIS of G in O(mnlog Delta) time, where m denotes the number of edges in G and Delta is the maximum degree of the graph. Agents do not need prior knowledge of any parameters if the initial configuration is rooted. For arbitrary initial configuration, agents need to know few global parameters beforehand. We further show that using a similar approach it is possible to find the maximum clique in chordal graphs and color any chordal graph with the minimum number of colors. We also provide a dynamic programming-based approach to solve the MaxIS finding problem in trees in O(n) time.
We present a universally-optimal distributed algorithm for the exact weighted min-cut. The algorithm is guaranteed to complete in (O) over tilde (D + root n) rounds on every graph, recovering the recent result of Dory...
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ISBN:
(纸本)9781450392624
We present a universally-optimal distributed algorithm for the exact weighted min-cut. The algorithm is guaranteed to complete in (O) over tilde (D + root n) rounds on every graph, recovering the recent result of Dory, Efron, Mukhopadhyay, and Nanongkai [STOC'21], but runs much faster on structured graphs. Specifically, the algorithm completes in (O) over tilde (D) rounds on (weighted) planar graphs or, more generally, any (weighted) excluded-minor family. We obtain this result by designing an aggregation-based algorithm: each node receives only an aggregate of the messages sent to it. While somewhat restrictive, recent work shows any such black-box algorithm can be simulated on any minor of the communication network. Furthermore, we observe this also allows for the addition of (a small number of) arbitrarily-connected virtual nodes to the network. We leverage these capabilities to design a min-cut algorithm that is significantly simpler compared to prior distributed work. We hope this paper showcases how working within this paradigm yields simple-to-design and ultra-efficient distributed algorithms for global problems. Our main technical contribution is a distributed algorithm that, given any tree T, computes the minimum cut that 2-respects T (i.e., cuts at most 2 edges of T) in universally near-optimal time. Moreover, our algorithm gives a deterministic (O) over tilde (D)-round 2-respecting cut solution for excluded-minor families and a deterministic (O) over tilde (D + root n)-round solution for general graphs, the latter resolving a question of Dory, et al. [STOC'21]
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measuremen...
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ISBN:
(数字)9781665485470
ISBN:
(纸本)9781665485470
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurements at each agent are linear and corrupted with additive white Gaussian noise. Under such settings, this paper presents a novel distributed estimation algorithm that fuses the the concepts of consensus and innovations by incorporating the consensus terms (of neighboring estimates) into the innovation terms. Under the assumption of distributed parameter observability, introduced in this paper, we design the optimal gain matrices such that the distributed estimates are consistent and achieves fast convergence.
In this paper, we explore the field of self-reconfigurable modular robots, representing a significant advance in robotic technology. These robots have many capabilities, offering high adaptability and flexibility for ...
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ISBN:
(纸本)9798350377712;9798350377705
In this paper, we explore the field of self-reconfigurable modular robots, representing a significant advance in robotic technology. These robots have many capabilities, offering high adaptability and flexibility for a variety of applications. However, computing the stability is challenging as it is computationally intensive, it needs to be distributed and fast, as close as possible of real-time. In this article, we introduce a distributed algorithm designed to overcome these challenges while taking mechanical constraints into account. At the heart of this algorithm is the notion of the "support polygon", which enables the stability of a modular robot to be assessed in real time. The algorithm is based on a fully distributed tree partitioning approach, facilitating efficient communication and collaboration between modules. The algorithm also uses a polygon merging approach to reduce the number of messages when creating the polygon support, thus significantly reducing response time. In fact, the response time of the method used is very small compared to other research. We also present simulation results on a simulator, VisibleSim, as well as experimental validation on real robotic modules, which underlines the practical viability of the approach. Overall, this work lays a solid base for further advances aiming to guarantee the stability of modular robots.
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