This paper presents a visual analytics workflow for detecting stable cross-impact patterns in time series pairs. A sliding window technique computes multiple impact measures, including a novel Kendall's tau variant that tolerates minor fluctuations. Evaluating these measures across various time lags reveals dynamic relationships between time series. An interactive Ikat plot facilitates the exploration of impact distributions, helping identify intervals where specific cross-impacts remain stable (e.g., trends in one series followed by similar or opposite trends in another after a lag). These intervals are extracted as events, whose temporal (and, when applicable, spatial) distributions can be analyzed to uncover broader patterns across multiple time series pairs and over extended time spans. This includes identifying co-occurring cross-impacts and variations in cross-impact presence or type across different periods and data subsets. Experiments on real-world datasets demonstrate the framework's ability to isolate robust patterns, providing a scalable and interpretable approach to analyzing complex temporal dynamics.

In this work, we introduce RATS ( R esource A llocator for T umor S imulations), the first optimizer for the execution of tumor simulations over HPC infrastructures. Given a set of drug therapies under in-silico study, the optimization framework of RATS can: (i) devise the optimal number of cores and prescribe the required number of core hours; and (ii) under core capacity constraints, RATS schedules the execution of simulations minimizing the overall number of core hours, simultaneously prioritizing the execution of expectedly promising in-silico trials higher compared to unpromising ones. RATS is deployed by life scientists at the Barcelona Supercomputing Center to remove the burden of blindly guessing the core hours needing to be reserved from HPC admins to study various tumor treatment methodologies, as well as to rapidly distinguish effective drug combinations, thus, potentially cutting time to market for new cancer therapies. The latter is further elevated by the RATS+ extension we plug into the initial framework. RATS+ employs a Transfer Learning approach to leverage optimization models and decisions from prior in-silico studies, thereby reducing the optimization effort required for new studies in this domain. Our experimental evaluation, on real-world data derived from the execution of more than 2500 tumor simulations on the MareNostrum4 supercomputer, confirms the effectiveness of both RATS and RATS+ across the aforementioned performance dimensions.

We introduce RATS (Resource Allocator for Tumor Simulations), the first optimizer for the execution of tumor simulations over HPC infrastructures. The optimization framework of RATS incorporates 3 vital performance criteria (i) expected utility of a simulation in terms of effective drug combination on the simulated tumor, (ii) simulation execution time and (iii) number of cores required for achieving that execution time. RATS is to be used by life scientists at the Barcelona Supercomputing Center to not only remove the burden of blindly guessing the core hours we need to reserve from HPC admins to study various tumor treatment methodologies, but also to help in more rapidly distinguishing effective drug combinations, thus, potentially cutting time to market for new cancer therapies.

Conventional Programmable Logic Controller (PLC) systems are becoming increasingly challenging to manage due to hardware and software dependencies. Moreover, the number and size of conventional PLCs on factory floors continue to increase, and virtualized PLC (vPLC) offers a solution to address these challenges. The utilization of vPLC offers the advantages of streamlining communication between high-level applications and low-level machine operations, enhancing programming ability in process control systems by abstracting control functions from I/O modules, and increasing automation in industrial control networks. Nevertheless, the connection of vPLC to the internet and cloud services presents a considerable cybersecurity risk, and the crucial aspect of information security for vPLCs is ensuring their availability. Distributed Denial of Service (DDoS) attacks can be particularly devastating for vPLCs, as they rely on internet connectivity to function. DDoS attacks on vPLC overwhelm it and causing it to become unavailable. vPLCs manages control systems and if targeted by a DDoS attack, these systems could become unresponsive, leading to significant disruption to industrial processes. Thus, implementing effective DDoS protection measures is crucial for ensuring the availability and reliability of vPLCs in industrial settings. Therefore, this work proposes a Federated learning enabled Threat Intelligence Unit (FedTIU) for detecting DDoS attacks on vPLCs on an Edge Compute Stack near to vPLC. The proposed approach involves collaborative model training using federated learning techniques to gain knowledge of new attack patterns from other industrial sites while maintaining data privacy.

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real‐world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi‐scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.

We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Traditionally, network management tasks manually performed by system administrators include monitoring alarms based on collected statistics across many heterogeneous systems, correlating these alarms to identify potential problems or changes to management policies and responding by performing system re-configurations to ensure optimal performance of network services. System administrators have a narrow focus of factors impacting network service provisioning and performance due to the heterogeneity and scale of generated underlying network events. However, self-adaption principles are conceptual approaches for autonomously managing such complex distributed systems. Network management systems that harness such principles can dynamically and autonomously optimise the operation of network services, responding quickly to changes in user requirements and underlying network conditions. In this paper, we present a novel self-adaptive network management framework that takes advantage of a recursive network architecture for a simpler and more comprehensive application of ontologies, semantic web rules and machine learning to automatically adjust network configuration parameters to provide more optimal QoS management of network services. We demonstrate the applicability of the approach using a content distribution network (CDN) operating over such a recursive network architecture.

Recent endeavors in addressing the challenges of the current and future Internet pursue a clean slate design methodology. Simultaneously, it is argued that the Internet is unlikely to be changed in one fell swoop and that its next generation requires an evolutionary design approach. Recognizing both positions, we claim that cleanness and evolution are not mutually exclusive, but rather complementary and indispensable properties for sustainable management in the future Internet. In this paper we propose the in-network management (INM) paradigm, which adopts a clean slate design approach to the management of future communication networks that is brought about by evolutionary design principles. The proposed paradigm builds on embedded management capabilities to address the intrinsic nature, and hence, close relationship between the network and its management. At the same time, INM assists in the gradual adoption of embedded self-managing processes to progressively achieve adequate and practical degrees of INM. We demonstrate how INM can be exploited in current and future network management by its application to P2P networks.