The Kumbh Mela festival is the largest mass gathering in the world that is celebrated every three years. In 2016, it attracted over 70 million people to Ujjain, India. The Mahakal temple is the “heart” of the festival that attracts a huge number of pilgrims and needs to accommodate with massive crowds. These types of events pose significant safety challenges as large-scale mass gatherings are often associated with risks such as crowd crushes. There have been a number of serious incidents documented in recent history such as the Hajj crush at Mina, Mecca, Saudi Arabia (2006 and 2015), the Lame Horse crush during a fire at Perm, Russia (2009), the Love Parade disaster at Duisburg, Germany (2010), and the Kumbh Mela stampede at Allahabad, Uttar Pradesh, India (2013) to name a few. Safety assurance at events of such tremendous size is closely connected with crowd control and understanding the general behaviour of the crowd. One of the basic challenges in understanding crowd dynamics is being able to predict crowd flows at a particular location based on past/present flows from another location. There are several existing methods and models used to predict and manage crowd flow. In this paper, we introduce a novel method for short-term crowd flow prediction and show that it decreases the prediction error by 13% as compared to existing methods. The model is based on ensemble learning where we demonstrate that a combination of complementary methods with different a-priory assumptions can create better estimations. Utilizing a unique data set derived from CCTV camera recordings of pilgrims that we collected during the Kumbh Mela 2016 festival, we tested different methods from artificial intelligence and computational modelling, such as simple shift of time-series (time-shift), agent-based modelling, machine learning methods, and show how combinations of these different methods as an ensemble provide synergy to obtain better predictions. Our results demonstrate that agent-based modelling, when combined with other models, provides better predictive power especially in complex scenarios. These results point to something fundamental about the information contained within and generated by these methods. We anticipate that our research could be a starting point for further research of informational synergetic aspects of models and predictors.

From the moment of the fall of the Soviet Union in 1991, Russian higher education underwent drastic changes to international standards and enter the European Higher Education Area on equal terms. Joint efforts of Russian and Dutch peers and strong incentives from high-tech industries helped to launch new educational programs in Computational Science. Our international team of educators attempts to pick the best from the curricula and educational methods of post-Soviet programs and adopt the world's best practices in engineering education. In this paper we describe the curricula of a double-degree Master's program in Supercomputer Technologies in Interdisciplinary Research of the ITMO University and the University of Amsterdam and newly-developed programs in Big Data and Urban Supercomputing. We comment on the form and content of our programs and give some examples of student research topics.

MOTIVATION: Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. RESULTS: We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. AVAILABILITY AND IMPLEMENTATION: The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

Existing studies on the propagation of infectious diseases have not sufficiently considered the uncertainties that are related to individual behavior and its influence on individual decision making to prevent infections, even though it is well known that changes in behavior can lead to variations in the macrodynamics of the spread of infectious diseases. These influencing factors can be categorized into emotion-related and cognition-related components. We present a fuzzy cognitive map (FCM) denotative model to describe how the factors of individual emotions and cognition influence each other. We adjust the weight matrix of causal relationships between these factors by using a so-called nonlinear Hebbian learning method. Based on this FCM model, we can implement individual decision rules against possible infections for disease propagation studies. We take the simulation of influenza A [H1N1] spreading on a campus as an example. We find that individual decision making against infections (frequent washing, respirator usage, and crowd contact avoidance) can significantly decrease the at-peak number of infected patients, even when common policies, such as isolation and vaccination, are not deployed.

SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

MOTIVATION: The abundance of biomedical literature has attracted significant interest in novel methods to automatically extract biomedical relations from the literature. Until recently, most research was focused on extracting binary relations such as protein-protein interactions and drug-disease relations. However, these binary relations cannot fully represent the original biomedical data. Therefore, there is a need for methods that can extract fine-grained and complex relations known as biomedical events. RESULTS: In this article we propose a novel method to extract biomedical events from text. Our method consists of two phases. In the first phase, training data are mapped into structured representations. Based on that, templates are used to extract rules automatically. In the second phase, extraction methods are developed to process the obtained rules. When evaluated against the Genia event extraction abstract and full-text test datasets (Task 1), we obtain results with F-scores of 52.34 and 53.34, respectively, which are comparable to the state-of-the-art systems. Furthermore, our system achieves superior performance in terms of computational efficiency. AVAILABILITY: Our source code is available for academic use at http://dl.dropbox.com/u/10256952/BioEvent.zip.

MOTIVATION: Protein-protein interactions (PPIs) play an important role in understanding biological processes. Although recent research in text mining has achieved a significant progress in automatic PPI extraction from literature, performance of existing systems still needs to be improved. RESULTS: In this study, we propose a novel algorithm for extracting PPIs from literature which consists of two phases. First, we automatically categorize the data into subsets based on its semantic properties and extract candidate PPI pairs from these subsets. Second, we apply support vector machines (SVMs) to classify candidate PPI pairs using features specific for each subset. We obtain promising results on five benchmark datasets: AIMed, BioInfer, HPRD50, IEPA and LLL with F-scores ranging from 60% to 84%, which are comparable with the state-of-the-art PPI extraction systems. Furthermore, our system achieves the best performance on cross-corpora evaluation and comparative performance in terms of computational efficiency. AVAILABILITY: The source code and scripts used in this article are available for academic use at http://staff.science.uva.nl/~bui/PPIs.zip CONTACT: [email protected].

The inherent complexity of biomedical systems is well recognized; they are multi-scale, multi-science systems, bridging a wide range of temporal and spatial scales. This article reviews the currently emerging field of multi-scale modelling in computational biomedicine. Many exciting multi-scale models exist or are under development. However, an underpinning multi-scale modelling methodology seems to be missing. We propose a direction that complements the classic dynamical systems approach and introduce two distinct case studies, transmission of resistance in human immunodeficiency virus spreading and in-stent restenosis in coronary artery disease.

BACKGROUND: In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature. RESULTS: In this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation. CONCLUSIONS: The proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.

Virology research is nowadays a discipline involving a broad number of researchers gathered in different institutes and cooperating on defined issues. An example of such an endeavor is the research tackling anti-HIV treatment problems [7] conducted within the Virolab project. The main objective of the ViroLab project is to develop a Virtual Laboratory for Infectious Diseases that facilitates medical knowledge discovery and decision support for HIV drug resistance. Large, high quality in-vitro and clinical patient databases which can be used to relate genotype to drug-susceptibility phenotype have become available. The core of the ViroLab Virtual Laboratory is a rule-based ranking system. More specifically, using a Grid-based service oriented architecture, Virolab vertically integrates the biomedical information from viruses (proteins and mutations), patients and literature (drug resistance experiments), resulting in a rule-based decision support system for drug ranking. This paper is a contribution to virologists, epidemiologists and clinicians in medical knowledge discovery and decision support. The final aim is reasoning on the properties of algorithm modeling the interaction among drugs and HIV virus and detecting its anomalies such as rules that can never be satisfied and subset of rules that are in contradiction.

FPGA-based computation engines have been used as Cellular Automata accelerators in the scientific community for some time now. With the recent availability of more advanced FPGA logic it becomes necessary to better understand the mapping of Cellular Automata to these systems. There are many trade-offs to consider when mapping a Cellular Automata algorithm from an abstract system to the physical implementation using FPGA logic. The trade-offs include both the available FPGA resources and the Cellular Automata algorithm's execution time. The most important aspect is to fully understand the behavior of the specified CA algorithm in terms of its execution times which are either compute bound or I/O bound. In this article, we present a methodology to categorize a specified CA algorithm as a compute bound or an I/O bound. We take the methodology further by presenting rigorous analysis for each of the two cases identifying the various parameters that control the mapping process and are defined both by the Cellular Automata algorithm and the given FPGA hardware specifications. This methodology helps to predict the performance of running Cellular Automata algorithms on specific FPGA hardware and to determine optimal values for the various parameters that control the mapping process. The model is validated for both compute and I/O bound two-dimensional Cellular Automata algorithms. We find that our model predictions are accurate within 7%.

The 11 papers in this special section focus on biomedical computations on the grid.

FPGA-based computation engines have been used as cellular automata (CA) accelerators within the scientific community for some time now. In this paper, we present a methodology to predict the performance of running such applications on a specific FPGA hardware technology before engineering the design in reality. This will help to determine the optimal values for the various parameters that control the application and the given FPGA hardware specifications. The model will be validated for a specific two-dimensional CA.

The growing complexity of distributed biomedical application requirements present new challenges to the representation of software architectural analysis and design. This is the case in particular when using grid infrastructures for supporting interactive biomedical applications. In this paper we present a generic grid software architecture for biomedicine, where we focus on a conceptual level of architecture design. In this approach, we identify high-level components, their subcomponents and their relationships as required to allow for basic interactivity. We apply our approach to a biomedical application for the simulation of blood flow, within a production grid framework, and present our conclusions

This paper reports on our research into supporting collaborative distributed applications on the Grid. Our case study application, a Virtual Reactor problem solving environment, was built for simulation of industrially important technology of plasma chemical deposition. It incorporates a number of components distributed among different organizations and requires both high-performance and high-throughput computing, as well as interactivity and advanced visualization. The application has been ported to the Grid environment and tested on a highly heterogeneous infrastructure of the RidGrid testbed. We describe the Virtual Reactor architecture and approaches used to integrate distributed components of the application on the Grid, emphasizing the role of the Migrating Desktop Grid portal and tools that bridge the gap between Grid resources and end-user applications. We outline the main features and functionalities of our RidGrid testbed built within a larger European CrossGrid infrastructure, and describe novel solutions and technical approaches that facilitate the usage of Grid resources and optimize load balancing for parallel applications on heterogeneous resources.

Collaborative environments where users can interact with running simulations are an important aspect of e- Science. To achieve efficient execution of High Level Architecture (HLA)-based collaborative environments on the Grid, we introduce a Grid HLA Management System (GHLAM) for their management. This is done by introducing migration and monitoring mechanisms for such applications. In this paper we present how G-HLAM can be applied to the collaborative environment supporting surgeons with simulations of vascular reconstruction, using distributed federations on the Grid for the communication among simulation and visualization components.

We report on our findings after running a number of on-line performance monitoring experiments with a biomedical parallel application to investigate levels of performance at hardware resources distributed across a computational Grid network. We use on-line application monitoring for improved computational resource selection and application optimization. We used a number of user-defined performance metrics within the European CrossGrid Project's G-PM tool together with a blood flow simulation application based on the lattice Boltzmann method for fluid dynamics. We found that the performance results observed during our on-line experiments give us a more accurate view of computational resource status than the regular resource information provided by standard information services to resource brokers, and that on-line monitoring has good potential for optimizing our biomedical application for more efficient runs.

This paper addresses grid-based integration and access of distributed data from infectious disease patient databases, literature on in-vitro and in-vivo pharmaceutical data, mutation databases, clinical trials, simulations and medical expert knowledge. Artificial intelligence and grid technology is used to abstract knowledge from the data and provide the physicians with adaptive interactive advice on treatment applied to drug resistant HIV. An important aspect of our research is to use a variety of statistical and numerical methods to identify relationships between HIV genetic sequences and antiviral resistance to investigate consistency of results. The output of the problem-solving environment (PSE) consists of a prediction of the drug sensitivity of the virus, generated by comparing the viral genotype to a relational database which contains a large number of phenotype-genotype pairs. Multivariate analyses combined with rule-based fuzzy logic are applied to the integrated data to provide ranking of patient-specific drugs. In addition, cellular automata-based simulations are used to predict the drug behaviour overtime. Access to and integration of data is done through existing Internet servers and emerging grid-based frameworks like Globus. Data presentation is done by standalone PC based software, Web-access and PDA roaming WAP access. The experiments were carried out on the DAS, a Dutch Grid testbed.

By allowing human users to manipulate simulation models and steer their execution at run time, interactive simulation systems (ISS) are essential to realise problem solving environments (PSE) for studying complex problems that are difficult to investigate using conventional methodologies. However, the complexity of system development critically hampers the construction of ISSs; when a scientist explores a complex problem, he has to spend much of his effort on various implementation issues, instead of on the investigation of the experiment itself A layered framework for developing ISSs is crucial to hide the underlying development issues from scientists and to allow them to focus on the high-level behaviour of the system. In this paper we investigate a solution to the complexity issues in ISSs based on the separation of application logic control and system functionality. We implement a proof of concept architecture, called interactive simulation system conductor, based on high level architecture (HLA), and demonstrate that this architecture allows a scientist to quickly adapt the system to his needs in a rapid prototyping approach. A medical application for planning surgical operations is used as a test case.

In e-science environments, scientific workflow management systems (SWMS) hide the integration details among grid resources and allow scientists to prototype an experimental computing system at a high level of abstraction. However, the development of an effective SWMS requires profound knowledge on both application domains and the network programming, and is often time consuming. Agent technologies provide suitable solutions to decompose the control intelligence of flow execution and to encapsulate distributed e-science resources. The work presented in this paper is conducted in the context of the Dutch Virtual Laboratory for e-science (VL-e) project. Agent technologies are proposed to realize generic workflow support.

We conduct computer simulation experiments in preoperative planning of vascular reconstruction with a physician in the experimental loop. We constructed a problem-solving environment, which offers an integrating approach for constructing and running complex interactive systems. Grid resources are used for access to medical image repositories, segmentation resources, simulation of blood flow, and visualization of the simulated results, together with medical data obtained from MRI/CT scanners. This case study in vascular reconstruction planning has been validated via contextual analysis in a number of hospitals, resulting in a set of new requirements gathered for future versions of the system.

In this paper we describe a Grid-enabled system for monitoring HLA-based applications to enable load balancing by migration of federates. The monitoring is based on the OCM-G system [2].We show how the design concepts of the OCM-G enable easy adaptation to monitoring of HLA, C++-based applications. The solution presented in this paper is transparent to the user application and does not require any changes to the original HLA RTI code. We also describe the role of monitoring the whole system for managing execution of HLA-based applications described in [25]. We discuss implementation issues and present test results for the monitoring overhead.

Hybrid architectures are systems where a high performance general purpose computer is coupled to one or more special purpose devices (SPDs). Such a system can be the optimal choice for several fields of computational science. Configuring the system and finding the optimal mapping of the application tasks onto the hybrid machine often is not straightforward. Performance modeling is a tool to tackle and solve these problems. We have developed a performance model to simulate the behavior of a hybrid architecture consisting of a parallel multiprocessor where some nodes are the host of a GRAPE board. GRAPE is a very high performance SPD used in computational astrophysics. We validate our model on the architecture at our disposal, and show examples of predictions that our model can produce.

We investigate the performance of the time warp kernel APSIS when running on various communication layers, in particular on a wide-area grid. Several cancellation strategies are tried, among them the lazy cancellation and a little known bulk anti-messages optimization. Our experiments with an Ising spin simulation indicate that the slowdown caused by high latency networks, while significant, is not catastrophic; and that it can be significantly reduced using the lazy cancellation. Experiments suggest that further improvements can be expected if a more elaborate communication infrastructure is put in place.

This paper presents the design of a system that supports execution of HLA (high level architecture) distributed interactive simulations in an unreliable grid environment. The design of the architecture is based on the OGSA (Open Grid Services Architecture) concept that allows for modularity and compatibility with grid services already being developed. First of all, we focus on the part of the system which is responsible for migration of a HLA-connected component or components of the distributed application in the grid environment. We present a runtime support library for easily plugging HLA simulations into the grid services framework. We also present the impact of execution management (namely migration) on overall system performance.

This paper is devoted to multi-modal interface design and implementation of a simulated vascular reconstruction system. It provides multi-modal interaction methods such as speech recognition, hand gestures, direct manipulation of virtual 3D objects and measurement tools. The main challenge is that no general interface scenario in existence today can satisfy all the users of the system (radiologists, vascular surgeons, medical students, etc.). The potential users of the system can vary by their skills, expertise level, habits and psycho-motional characteristics. To make a multimodal interface user-friendly is a crucial issue. In this paper we introduce an approach to develop such an efficient, user-friendly multi-modal interaction system. We focus on adaptive interaction as a possible solution to address the variety of end-users. Based on a user model, the adaptive user interface identifies each individual by means of a set of criteria and generates a customized exploration environment.

Abstract The paper provides an overview of an experimental, Grid‐like computing environment, Polder, and its components. Polder offers high‐performance computing and interactive simulation facilities to computational science. It was successfully implemented on a wide‐area cluster system, the Distributed ASCI Supercomputer. An important issue is an efficient management of resources, in particular multi‐level scheduling and migration of tasks that use PVM or sockets. The system can be applied to interactive simulation, where a cluster is used for high‐performance computations, while a dedicated immersive interactive environment (CAVE) offers visualization and user interaction. Design considerations for the construction of dynamic exploration environments using such a system are discussed, in particular the use of intelligent agents for coordination. A case study of simulatedabdominal vascular reconstruction is subsequently presented: the results of computed tomography or magnetic resonance imaging of a patient are displayed in CAVE, and a surgeon can evaluate the possible treatments by performing the surgeries virtually and analysing the resulting blood flow which is simulated using the lattice‐Boltzmann method. Copyright © 2002 John Wiley & Sons, Ltd.

Genericity of parallel programming environments, enabling development of portable parallel programs, is expected to result in performance penalties. Furthermore, programmability and tool support of programming environments are important issues if a choice between programming environments has to be made. In this paper we propose a methodology to compare native and generic parallel programming environments, taking into account such competing issues as portability and performance. As a case study, this paper compares the Iserver-Occam, Parix, Express and PVM parallel programming environments on a 512-node Parasytec GCel. Furthermore, we apply our methodology to compare Parix and PVM on a new architecture, a 32-node Parsytec PowerXplorer, which is based on the PowerPC chip. In our approach we start with a representative application and isolate the basic (environment)-dependent building blocks. These basic building blocks, which depend on floating-point performance and communication capabilities of the environments, are analysed independently. We have measured point-to-point communication times, global communication times and floating-point performance. All information is combined into a time complexity analysis, allowing comparison of the environments on different degrees of functionality. Together with demands for portability of the code and development time (i.e. programmability), an overall judgement of the environments is given.