Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.Comment: 52 pages, 6 figure

An Approach to Distributed Collaboration Problem with Conflictive Tasks

Abstract. Distributed collaboration problem (DCP) is a kind of distributed resource allocation problem. Previous works give the solution of DCP with non-conflictive tasks. In this paper, we present a completely distributed algorithm (CDA) to solve DCP with conflictive tasks where each task requires exactly two resources. We call this the measurement collaboration problem (MCP). We prove the aliveness and correctness of CDA and give the run-time performance of CDA by simulation. To provide a baseline of performance parameters, we design a simple single-wait-state algorithm (SWSA). The simulation indicates CDA gradually has higher efficiency than SWSA, especially when the tasks are highly conflictive. CDA can also be utilized in many aspects, such as disaster rescue, distributed agent collaboration, objects position and so on.

Transfer Learning for Region-Wide Trajectory Outlier Detection

Trajectory outlier detection is a crucial task in trajectory data mining and has received significant attention. However, the distribution of trajectories is tied to social activities, resulting in extreme unevenness among regions. While existing methods have demonstrated excellent performance in regions with sufficient historical trajectories, they frequently struggle to detect outliers in regions with limited trajectories. Unfortunately, this issue has not received much attention, leaving a gap in the current understanding of trajectory mining. To deal with this problem, we in this paper propose a model called TTOD that can effectively detect outliers in regions with sparse data by transferring knowledge among regions. The main idea is to learn a feature mapping function that maps the global feature space of auxiliary regions to the target region’s specific feature space. To achieve this, we adopt a VAE-based model called the Global VAE to learn the global feature space in auxiliary regions by modeling the trajectory patterns with Gaussian distributions. Then, we propose a Specific-region VAE that serves as the mapping function to learn the target feature space. Additionally, considering the data drift of feature distributions among regions, we introduced an additional pattern synthesis layer, named the De-drift Layer, to diversify the target feature space, thus addressing the pattern missing issue caused by the gap of feature distributions between the auxiliary regions and the target regions. Then the target feature space can be well studied and applied to detect outliers. Finally, we conduct extensive experiments on two real taxi trajectory datasets and the results show that TTOD achieves state-of-the-art performance compared with the baselines

Wireless Relay Selection in Pocket Switched Networks Based on Spatial Regularity of Human Mobility

Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays

Radical Mechanism of Isocyanide-Alkyne Cycloaddition by Multicatalysis of Ag₂CO₃, Solvent, and Substrate

A combined DFT and experimental study was performed to reveal the mechanism of isocyanide-alkyne cycloaddition. Our results indicate that the mechanism of this valuable reaction is an unexpected multicatalyzed radical process. Ag₂CO₃ is the pivotal catalyst, serving as base for the deprotonation of isocyanide and oxidant to initiate the initial isocyanide radical formation. After the cycloaddition between isocyanide radical and silver-acetylide, substrate (isocyanide) and solvent (dioxane) replace the role of Ag₂CO₃. They act as a radical shuttle to regenerate isocyanide radical for the next catalytic cycle, simultaneously completing the protonation. Furthermore, the bulk solvent effect significantly increases the reactivity by decreasing the activation barriers through the whole reaction, serving as solvent as well as catalyst