Discovery Algorithm Research Articles

SkyOrbs: A Fast 3-D Directional Neighbor Discovery Algorithm for UAV Networks

SkyOrbs: A Fast 3-D Directional Neighbor Discovery Algorithm for UAV Networks

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. SDUCL integrates image features, spatial relationships and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. https://github.com/WeiMin-Li-visual/SDUCL. Supplementary data are available at Bioinformatics online.

On Process Discovery Experimentation

Process mining aims to derive insights into business processes from event logs recorded from information systems. Process discovery algorithms construct process models that describe the executed process. With the increasing availability of large-scale event logs, process discovery has shifted towards a data-oriented research discipline, aiming to design algorithms that are applicable and useful in practice. This shift has revealed a fundamental problem in process discovery research: Currently, contributions can only be considered in isolation. Researchers conduct experiments to show that they move the field forward, but due to a lack of reliability and validity, the individual contributions are hard to generalise. In this paper, we argue that one reason for these problems is the lack of conventions or standards for experimental design in process discovery. Hence, we propose “process discovery engineering”: a research methodology for process discovery, consisting of a shared terminology and a checklist for conducting experiments. We demonstrate its applicability by means of an example experimental evaluation of process discovery algorithms and discuss the implications of the methodology on the field. This paper is not meant to be prescriptive, but to invite and encourage the community to contribute to this discussion to advance the field as a whole.

Valid Inference After Causal Discovery

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to “double dipping,” invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while allowing for a trade-off between causal discovery accuracy and confidence interval width. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Towards Integrating Federated Learning with Split Learning via Spatio-temporal Graph Framework for Brain Disease Prediction.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (FS2G). Specifically, we introduce federated learning and split learning techniques to split a spatio-temporal model into a client temporal model and a server spatial model. In the client temporal model, we propose a time-aware mechanism to focus on changes in brain functional states and use an InceptionTime model to extract information about changes in the brain states of each subject. In the server spatial model, we propose a united graph convolutional network to integrate multiple graph convolutional networks. Integrating federated learning and split learning, FS2G can utilize multi-site fMRI data without violating data privacy protection and reduce the risk of overfitting as it is capable of learning from limited training data sets. Moreover, it boosts the extraction of spatio-temporal features of fMRI using spatio-temporal graph networks. Experiments on ABIDE and ADHD200 datasets demonstrate that our proposed method outperforms state-of-the-art methods. In addition, we explore biomarkers associated with brain disease prediction using community discovery algorithms using intermediate results of FS2G. The source code is available at https://github.com/yutian0315/FS2G.

CoSD: Balancing behavioral consistency and diversity in unsupervised skill discovery

In hierarchical reinforcement learning, unsupervised skill discovery holds promise for overcoming the challenge of sparse rewards commonly encountered in traditional reinforcement learning. However, previous unsupervised skill discovery methods have excelled at maximizing intrinsic rewards but have overly prioritized skill diversity. Unrestrained pursuit of diversity leads skills to concentrate attention on unexplored domains, overlooking the internal consistency of skills themselves, resulting in the state visit distribution of individual skills lacking concentration. To address this problem, the Constrained Skill Discovery (CoSD) algorithm is proposed to balance the diversity and behavioral consistency of skills. CoSD integrates both the forward and backward decomposition forms of mutual information and uses the maximum entropy policy to maximize the information-theoretic objective of skill learning while requiring that each skill maintain low state entropy internally, which enhances the behavioral consistency of the skills while pursuing the diversity of the skills and ensures that the learned skills have a high degree of stability. Experimental results demonstrated that, compared to other skill discovery methods based on mutual information, skills from CoSD exhibit a more concentrated state visit distribution, indicating higher behavioral consistency and stability. In some complex downstream tasks, these skills with higher behavioral consistency exhibit superior performance.

How the Spacer Influences the Stability of 2D Perovskites?

Two-dimensionallead halide perovskites (2D HPs) represent as an emerging class of materials given their tunable optoelectronic properties and long-term stability in perovskite solar cells. However, the ever-growing field of optoelectronic devices using 2D HPs requires fundamental understanding of the influence of the spacer on the physiochemical properties and stability of perovskites as well as establish which cation properties are closely related to suppress the halogen ion mobility. This study focuses on investigating the influence of organic spacers with intrinsic properties (e.g., rigidity and flexibility, special groups) and variations of material dimensions on the stability of halogen ions and inorganic frameworks in 2D HPs. It is found that the perovskite structure composed of rigidity molecules owns better stability of halogen ion and inorganic framework than that of flexible molecules. The stability of ions exhibits a negative correlation with the dimensions of perovskite. More importantly, a simple descriptor for measuring the stability of halogen ions in 2D HPs is constructed. By causal discovery algorithms with more physical and chemical significance, the Kappa shape index, number of rotatable bonds, and aromatic carbocycles in organic spacers are identified as causal and important features for the stability of halogen ions in 2D HPs.

Enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery in wireless ad hoc networks

The utilization of directional antennas for neighbor discovery in wireless ad hoc networks brings notable benefits, such as extended transmission range, reduced transmission interference, and enhanced antenna gain. However, when nodes use directional antennas for neighbor discovery, the communication range is limited, resulting in a lack of knowledge of potential neighbors. Hence, it is necessary to design a special antenna direction switching strategy for neighbor discovery based on directional antennas. Traditional methods of switching antenna directions are often random or follow predefined sequences, overlooking the historical knowledge of sector exploration for antenna directions. In contrast, existing machine learning approaches aim to leverage observed historical knowledge to adjust antenna directions for faster neighbor discovery. Nonetheless, the latency of neighbor discovery is still high because the node cannot fully utilize the observed historical knowledge (i.e.., only using the knowledge observed by the node in transmission mode, ignoring the knowledge observed by the node in reception mode). Meanwhile, the corresponding reward and penalty mechanisms are still not detailed enough (i.e.., these reward and penalty mechanisms only consider the sectors of discovered and undiscovered neighboring nodes, ignoring the scenario of sectors that have been rewarded). In this paper, the neighbor discovery process is modeled as a reinforcement learning-based learning automaton. We propose an enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery algorithm, called ERTTND. The algorithm consists of a two-way transmit-receive reinforcement learning mechanism (TTRL) and an enhanced reward-and-penalty mechanism (ERAP). This algorithm leverages insights from nodes in transmission and reception modes to refine their tactical decisions. Then, through an enriched reward-and-penalty framework, nodes optimize their strategies, thus expediting neighbor discovery based on directional antennas in wireless ad hoc networks. Simulation results demonstrate that compared to existing representative algorithms, the proposed ERTTND algorithm can achieve over 30% savings in terms of average discovery delay and energy consumption.

Exploring an adaptive management model for “status-optimization-regulation” of mining city in transition: A case study of Huangshi, China

Mining cities face the challenges of ecological transformation and sustainable development after mineral depletion. Thus, ecological space optimization and adaptive management are pivotal after ecological restoration project, but easily neglected. Taking Huangshi in Hubei Province as an example, a top-down adaptive management model for regional mining ecological space integrating status-optimization-regulation (SOR) was established based on land system resilience (LSR) evaluation, circuit theory, complex network model and community discovery algorithm. The results showed that i) As the LSR increased, the land use structure shifted from non-ecological zones to forest and cropland, indicating that balancing agriculture and forest protection was crucial for increasing LSR in Huangshi. i) The ecological networks (ENs) of Huangshi had an irregular fishnet pattern, with densely intricate corridors in the south and broader, sparser ones in the north, and numerous ecological barriers near the mining area. Furthermore, the distribution of ecological sources and corridors displayed complementarity and hierarchy. iii) The optimized ENs exhibited higher connectivity and efficiency under disturbance, reducing corridor redundancy and migration costs. iv) The ENs clusters were classified into five types based on ecological connectivity: ecological buffer zone, priority restoration zone, moderate restoration zone, natural restoration zone, and moderate development zone, and tailored ecological regulatory strategies were proposed. The findings provide practice-oriented guidance for the sustainable development of mining cities, and offer a direct approach to support conscious, clear, and coherent adaptive management of ecological restoration in rapidly changing environments.

Non-blocking functional dependency discovery from data streams

With the proliferation of sensors and IoT technologies, there is an increasing need to analyze information from data streams that they produce dynamically. However, the volume and velocity of this data require algorithms that mine knowledge as data are read from streams. The capability of dynamically extracting functional dependencies (fds) from data streams would not only permit to assess and improve the quality of data, but also provide knowledge on the evolution of data correlations within streams, allowing to understand the relevance that each feature has in predicting unknown features. In this paper, we propose a new discovery algorithm, namely COD3, which allows to continuous discovery fds holding on a data stream, as the data are read from it. COD3 represents the first proposal to use a non-blocking architectural model for discovering fds from data streams. Furthermore, we present novel data structures and a validation method to handle dynamic discovery and reduce data load inbound streams. Experimental evaluations demonstrate its effectiveness on both adapted real-world datasets and real data streams, such as those from air quality sensors. Moreover, by integrating COD3 with Bleach, a well-known fd-based data stream cleansing framework, we demonstrate its effectiveness in a real-world use case.

A wrapper feature selection approach using Markov blankets

In feature selection, Markov Blanket (MB) based approaches have attracted considerable attention with most MB discovery algorithms being categorized as filter based techniques. Typically, the Conditional Independence (CI) test employed by such methods is different for different data types. In this article, we propose a novel Markov Blanket based wrapper feature selection method. The proposed approach employs Predictive Permutation Independence (PPI), a novel Conditional Independence (CI) test that allows it to work out-of-the-box for both classification and regression tasks on mixed data. PPI can work with any supervised algorithm to estimate the association of a feature with the target variable while also providing a measure of feature importance. The proposed approach also includes an optional MB aggregation step that can be used to find the optimal MB under non-faithful conditions. Our method11Implementation and experimental results are available at https://github.com/AnonymousMLSubmissions/PatternRecognitionSubmission. outperforms other MB discovery methods, in terms of F1-score, by 7% on average, over 3 large-scale BN datasets. It also outperforms state-of-the-art feature selection techniques on 13 real-world datasets.

Causal inference of urban heat island effect and its spatial heterogeneity: A case study of Wuhan, China

The accelerated urbanization process has exacerbated the urban heat island effect, leading to significant negative impacts on the physical and mental health of urban residents. A deeper understanding of the mechanisms underlying the urban heat island phenomenon is essentially beneficial for providing scientific supports towards improving the urban thermal environment. To address the challenge of effectively depicting the complex interactions among urban environment factors, this study employed the Peter-Clark causal discovery algorithm to analyze the causal structure of urban thermal driving factors, and validated the effectiveness of the 6 key factors directly influencing the surface urban heat island intensity (SUHII). In response to the inadequacy of existing big data causal inference tools in assessing the spatial heterogeneity of causal effects, this study proposed a method for evaluating the causal effects on SUHII and their spatial heterogeneity based on local analysis and geospatial causal principle. The result for 4 different intervention scenarios in this study show that there is obvious spatial heterogeneity in the causal effects of different interventions on SUHII in Wuhan, and that increasing greenery and preserving natural environments is an effective way to mitigate the urban heat island (UHI) effect. This approach, provides a new perspective for studying the phenomenon of UHI and insights of potential approaches for mitigating UHI.

Automated generation of digital models for manufacturing systems: The event-centric process mining approach

Digital and simulation models support the design and management of complex systems. However, system modelling is a time-demanding and knowledge-intensive activity. Moreover, modern manufacturing systems are subjected to frequent changes in production plans and subsequent reconfigurations. Therefore, the quick regeneration of the digital models is necessary to align digital twins and cyber-physical systems. This paper proposes a novel event-centric process mining paradigm, a process discovery algorithm, and a set of Key Performance Indicators for the fast and automated generation of digital models and their benchmarking. The discovery algorithm is based on the Event Relationship Graph of the conceptual model of the physical line. The algorithm is tested in four realistic systems of increasing complexity to verify the accuracy in modelling multi-product systems with re-entrant flows and random reworks in the presence of the assembly, disassembly, and split processes beyond the processing operations, and multi-operation workstations. The Event Relationship Graphs of the four systems are presented through the equivalent Petri nets models. The proposed approach is suitable for systems where the sensor positions are known and meaningful, like manufacturing systems, and it is effective for the quick automated generation of digital models for the activities of production planning and control as it requires a few seconds of computation time and a few hours of system observation.

Timed Genetic Process Mining for Robust Tracking of Processes under Incomplete Event Log Conditions

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms.

MantaRay-ProM: An efficient process model discovery algorithm

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.

Multidimensional time series motif group discovery based on matrix profile

With the continuous advancements in sensor technology and the increasing capabilities for data collection and storage, the acquisition of time series data across diverse domains has become significantly easier. Consequently, there is a growing demand for identifying potential motifs within multidimensional time series. The introduction of the Matrix Profile (MP) structure and the mSTOMP algorithm enables the detection of multidimensional motifs in large-scale time series datasets. However, the Matrix Profile (MP) does not provide information regarding the frequency of occurrence of these motifs. As a result, it is challenging to determine whether a motif appears frequently or to identify the specific time periods during which it typically occurs, thereby limiting further analysis of the discovered motifs. To address this limitation, we proposed Index Link Motif Group Discovery (ILMGD) algorithm, which uses index linking to rapidly merge and group multidimensional motifs. Based on the results of the ILMGD algorithm, we can determine the frequency and temporal positions of motifs, facilitating deeper analysis. Our proposed method requires minimal additional parameters and reduces the need for extensive manual intervention. We validate the effectiveness of our algorithm on synthetic datasets and demonstrate its applicability on three real-world datasets, highlighting how it enables a comprehensive understanding of the discovered motifs.

A hybrid constrained continuous optimization approach for optimal causal discovery from biological data.

Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. https://github.com/zhu-yh1/PC-NOTEARS.

Development of optimised causal AI and its application in retail banking

Causal artificial intelligence (AI) has emerged as a promising approach in machine learning (ML), as it considers not only correlations but also cause-and-effect relationships in data, resulting in more human-like decision making. The pivotal stages within causal AI involve causal discovery and inferencing, each playing a crucial role in extracting meaningful insights from the data. In the realm of causal discovery, various algorithms have been developed to uncover the underlying cause-and-effect structures within data sets. A notable limitation, however, surfaces when attempting to apply these algorithms to data sets characterised by binary variables. This constraint prompts a crucial examination of the current methodologies and calls for innovative solutions that can seamlessly navigate the complexities of binary variable data sets. This paper proposes an optimised causal discovery algorithm that is integrated with the causal inference method based on the estimation of conditional average treatment effects (CATE) scores. The results present the potential of causal AI in terms of incremental impact on the predictive capability of AI/ML models. And the incremental impact is elucidated by comparing conventional propensity-based modelling and causal AI-based modelling by means of a use case in the field of retail banking.

Can algorithms Replace Expert Knowledge for Causal Inference? A Case Study on Novice Use of Causal Discovery.

With growing interest in causal inference and machine learning among epidemiologists, there is increasing discussion of causal discovery algorithms for guiding covariate selection. We present a case study of novice application of causal discovery tools and attempt to validate the results against a well-established causal relationship. As a case study, we attempted causal discovery of relationships relevant to the effect of adherence on mortality in the placebo arm of the Coronary Drug Project (CDP) dataset. We used four algorithms available as existing software implementations and varied several model inputs. We identified 15 adjustment sets from 17 model parameterizations. When applied to a baseline covariate adjustment analysis, these 15 adjustment sets returned effect estimates with similar magnitude and direction of bias as prior published results. When using methods to control for time-varying confounding, there was generally more residual bias than compared to expert-selected adjustment sets. Although causal discovery algorithms can perform on par with expert knowledge, we do not recommend novice use of causal discovery without the input of experts in causal discovery. Expert support is recommended to aid in choosing the algorithm, selecting input parameters, assessing underlying assumptions, and finalizing selection of the adjustment variables.

Tropical and mid-latitude causal drivers of the eastern Mediterranean Etesians during boreal summer

During boreal summer, large scale subsidence and a persistent northerly flow, known as the Etesians, characterize the tropospheric circulation over the eastern Mediterranean. The Etesians bring clear skies and alleviate the impact of heat waves over the region. The intraseasonal variability of the Etesians and subsidence over the eastern Mediterranean has been thought to be influenced by the South Asian monsoon and atmospheric processes over the North Atlantic. Here, we employ causal effect networks and causal maps, obtained by applying the Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery algorithm, to identify causal precursors of Etesians. We find that both wave train activity over the North Atlantic/North American region and convective activity over South Asia associated with the Indian summer monsoon (ISM) are causally related to the Etesians at 3-day time scale. Thus, intraseasonal ISM variability affects the eastern Mediterranean circulation, though its influence is conveyed via a Middle East ridge. On longer weekly time scale, the mid-latitude influence weakens, while the influence of the tropical convective activity via the Middle East ridge remains stable. Moreover, the heat low over the Arabian Peninsula, a feature strongly responsible for the development of the Etesians, is caused by a stronger Middle East ridge and not by North Atlantic wave activity. Finally, we discuss potential implication for circulation changes in the eastern Mediterranean due to anthropogenic global warming.