Degree-Ordered-Percolation on uncorrelated networks

We analyze the properties of Degree-Ordered Percolation (DOP), a model in which the nodes of a network are occupied in degree-descending order. This rule is the opposite of the much studied degree-ascending protocol, used to investigate resilience of networks under intentional attack, and has received limited attention so far. The interest in DOP is also motivated by its connection with the Susceptible-Infected-Susceptible (SIS) model for epidemic spreading, since a variation of DOP is related to the vanishing of the SIS transition for random power-law degree-distributed networks $P(k) \sim k^{-\gamma}$. By using the generating function formalism, we investigate the behavior of the DOP model on networks with generic value of $\gamma$ and we validate the analytical results by means of numerical simulations. We find that the percolation threshold vanishes in the limit of large networks for $\gamma \le 3$, while it is finite for $\gamma>3$, although its value for $\gamma$ between 3 and 4 is exceedingly small and preasymptotic effects are huge. We also derive the critical properties of the DOP transition, in particular how the exponents depend on the heterogeneity of the network, determining that DOP does not belong to the universality class of random percolation for $\gamma \le 3$.Comment: 10 pages, 9 figures, version to appear in JSTA

ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully

A Survey on the Distribution of Ovothiol and ovoA Gene Expression in Different Tissues and Cells: A Comparative Analysis in Sea Urchins and Mussels

Ovothiols are histidine-derived thiols produced by a variety of marine invertebrates, protists and bacteria. These compounds, which are among the strongest natural antioxidants, are involved in controlling the cellular redox balance due to their redox exchange with glutathione. Although ovothiols were initially reported as protective agents against environmental stressors, new evidence suggests that they can also act as pheromones and participate in fundamental biological processes such as embryogenesis. To get further insight into the biological roles of ovothiols, we compared ovothiol biosynthesis in the sea urchin Paracentrotus lividus and in the mussel Mytilus galloprovincialis, the two species that represent the richest sources of these compounds among marine invertebrates. Ovothiol content was measured in different tissues and in the immune cells from both species and the expression levels of ovoA, the gene responsible for ovothiol biosynthesis, was inferred from publicly available transcriptomes. A comparative analysis of ovothiol biosynthesis in the two species allowed the identification of the tissues and cells synthesizing the metabolite and highlighted analogies and differences between sea urchins and mussels. By improving our knowledge on the biological roles of ovothiols and pointing out the existence of sustainable natural sources for their isolation, this study provides the basis for future biotechnological investigations on these valuable compounds

MYCN is an immunosuppressive oncogene dampening the expression of ligands for NK-cell-activating receptors in human high-risk neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor occurring in childhood. Amplification of the MYCN oncogene is associated with poor prognosis. Downregulation on NB cells of ligands recognized by Natural Killer (NK) cell-activating receptors, involved in tumor cell recognition and lysis, may contribute to tumor progression and relapse. Here, we demonstrate that in human NB cell lines MYCN expression inversely correlates with that of ligands recognized by NKG2D and DNAM1 activating receptors in human NB cell lines. In the MYCN-inducible Tet-21/N cell line, downregulation of MYCN resulted in enhanced expression of the activating ligands MICA, ULBPs and PVR, which rendered tumor cells more susceptible to recognition and lysis mediated by NK cells. Conversely, a MYCN non-amplified NB cell line transfected with MYCN showed an opposite behavior compared with control cells. Consistent with these findings, an inverse correlation was detected between the expression of MYCN and that of ligands for NK-cell-activating receptors in 12 NB patient specimens both at mRNA and protein levels. Taken together, these results provide the first demonstration that MYCN acts as an immunosuppressive oncogene in NB cells that negatively regulates the expression of ligands for NKG2D and DNAM-1 NK-cell-activating receptors. Our study provides a clue to exploit MYCN expression levels as a biomarker to predict the efficacy of NK-cell-based immunotherapy in NB patients. KEYWORDS: Immunosuppressive oncogene, MYCN oncogene, neuroblastoma, NK-cell-activating receptor ligands, tumor immune escap

Degree-Ordered-Percolation on uncorrelated networks

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Dynamics and Collective Phenomena in Social and Socio-technical Systems.Peer reviewe

Using convolutional neural networks for predictive process analytics

Predictive process monitoring has recently become one of the main enablers of data-driven insights in process mining. As an application of predictive analytics, process prediction is mainly concerned with predicting the evolution of running traces based on models extracted from historical event logs. This paper presents a process mining approach, which uses convolutional neural networks to equip the execution scenario of a business process with a means to predict the next activity in a running trace. The basic idea is to convert the temporal data enclosed in the historical event log of a business process into spatial data so as to treat them as images. To this purpose, every trace of the event log is first transformed into the set of its prefix traces (i.e. sequences of events that represent the prefix of a trace). These prefix traces are mapped into 2D image-like data structures. Created spatial data are finally used to train a Convolutional Neural Network, in order to learn a deep learning model capable to predict the next activity (i.e. the activity associated to the event occurring after the last event in the considered prefix trace). This predictive deep model can be employed as a powerful service to support participants in performing business processes since it guarantees a higher utilization by acting proactively in anticipation. Preliminary tests with two benchmark logs are carried out to investigate the viability of the proposed approach

Predictive Process Mining Meets Computer Vision

Nowadays predictive process mining is playing a fundamental role in the business scenario as it is emerging as an effective means to monitor the execution of any business running process. In particular, knowing in advance the next activity of a running process instance may foster an optimal management of resources and promptly trigger remedial operations to be carried out. The problem of next activity prediction has been already tackled in the literature by formulating several machine learning and process mining approaches. In particular, the successful milestones achieved in computer vision by deep artificial neural networks have recently inspired the application of such architectures in several fields. The original contribution of this work consists of paving the way for relating computer vision to process mining via deep neural networks. To this aim, the paper pioneers the use of an RGB encoding of process instances useful to train a 2-D Convolutional Neural Network based on Inception block. The empirical study proves the effectiveness of the proposed approach for next-activity prediction on different real-world event logs

A multi-view deep learning approach for predictive business process monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods

JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction

In this paper, we propose a novel predictive process monitoring approach, named JARVIS, that is designed to achieve a balance between accuracy and explainability in the task of next-activity prediction. To this aim, JARVIS represents different process executions (traces) as patches of an image and uses this patch-based representation within a multi-view learning scheme combined with Vision Transformers (ViTs). Using multi-view learning we guarantee good accuracy by leveraging the variety of information recorded in event logs as different patches of an image. The use of ViTs enables the integration of explainable elements directly into the framework of a predictive process model trained to forecast the next trace activity from the completed events in a running trace by utilizing self-attention modules that give paired attention values between two picture patches. Attention modules disclose explainable information concerning views of the business process and events of the trace that influenced the prediction. In addition, we explore the effect of ViT adversarial training to mitigate overfitting and improve the accuracy and robustness of predictive process monitoring. Experiments with various benchmark event logs prove the accuracy of JARVIS compared to several current state-of-the-art methods and draw insights from explanations recovered through the attention modules

STARDUST: A Novel Process Mining approach to Discover Evolving Models From trace Streams

In this paper we introduce STARDUST (event STream Analysis for pRocess Discovery Using Sampling sTragies), a process discovery approach that analyses a trace stream, in order to discover a process model that may change over time. The basic idea is to adopt a sampling technique to select the most representative trace variants to be considered for the process discovery, then to alert a concept drift as the trace variants to be sampled change over time and, finally, to trigger the discovery of a new process model as a drift is alerted. We formulate the proposed approach under the assumption that the trace distribution commonly follows the Pareto’s principle (i.e., a few trace variants covers the majority of cases) which is commonly satisfied in several business processes. Experimental results on various benchmark event logs handled as streams show the effectiveness of the proposed approach also compared to a state-of-the-art concept drift detection approach