In personalized predictive medicine, accurately modeling a patient’s illness and care processes is essential, given their inherent long-term temporal dependencies. However, electronic medical records contain episodic and irregularly timed data due to patients visiting hospitals based on treatment needs, resulting in unique patterns for each hospital stay. Consequently, when constructing a personalized predictive model, it is crucial to consider these factors in order to accurately capture the patient’s health journey. To address this challenge, we present a novel deep dynamic memory neural network called Multi-Way adaptive Time Aware LSTM (MWTA-LSTM). The primary objective of MWTA-LSTM is to leverage medical records, memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power. To enhance its capabilities, MWTA-LSTM extends the conventional Long Short-Term Memory (LSTM) model in two key ways. Firstly, it incorporates frequency measurement and the most recent observation(last observation) to enhance personalized predictive modeling of patient illnesses, enabling a more accurate understanding of the patient’s condition. Secondly, it parameterizes the cell state to handle irregular timing effectively, utilizing both frequency measurement and elapsed times. Furthermore, the model capitalizes on both to comprehend the impact of interventions on the course of illness on the cell state, facilitating the memorization of illness courses and improving its ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in event and observation timing. Lastly, we introduce a novel adaptive pooling strategy that specifically targets and resolves outlier issues that can potentially occur during the analysis of EHR data. By incorporating these features, MWTA-LSTM significantly improves its ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in event and observation timing. We validate the effectiveness of our proposed model through empirical experiments conducted on two real-world clinical datasets and three real-world time series datasets. Our results demonstrate the superiority of MWTA-LSTM over current state-of-the-art models and other robust baselines. This showcases the potential of MWTA-LSTM in advancing personalized predictive medicine by offering a more accurate and comprehensive approach to modeling patient health trajectories.

In personalized predictive medicine , accurately modeling a patient's illness and care processes is essential, given their inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often contain episodic and irregularly timed data, resulting from patients' sporadic hospital admissions, leading to unique patterns for each hospital stay. Consequently, constructing a personalized predictive model requires careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. Long Short-Term Memory (LSTM) is an effective model for handling sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we present a novel deep dynamic memory neural network called Adaptive Multi-Way Interpretable Time-Aware LSTM for irregularly collected sequential data “AMITA”. The primary objective of AMITA is to leverage medical records , memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power. To enhance its capabilities, AMITA extends the standard LSTM model in two key ways. Firstly, it incorporates frequency measurement and the most recent observation to enhance personalized predictive modeling of patient illnesses, enabling a more accurate understanding of the patient's condition. Secondly, it parameterizes the cell state to handle irregular timing effectively, utilizing both elapsed times and a frequency-based decay factor, which considers both measurement frequency and contextual information. Furthermore, the model capitalizes on both to comprehend the impact of interventions on the course of illness on the cell state, facilitating the memorization of illness courses and improving its ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in event and observation timing. The effectiveness of our proposed model is validated through empirical experiments conducted on two real-world clinical datasets. The results demonstrate the superiority of AMITA over current state-of-the-art models and other robust baselines, showcasing its potential in advancing personalized predictive medicine by offering a more accurate and comprehensive approach to modeling patient health trajectories.

Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The development of high-throughput experimental technologies allows for the generation of numerous data which permits increasing the sophistication of PPI models. However, despite significant progress, current PPI networks remain incomplete. Discovering missing interactions through experimental techniques can be costly, time-consuming, and challenging. Therefore, computational approaches have emerged as valuable tools for predicting missing interactions. In PPI networks, a graph is usually used to model the interactions between proteins. An edge between two proteins indicates a known interaction, while the absence of an edge means the interaction is not known or missed. However, this binary representation overlooks the reliability of known interactions when predicting new ones. To address this challenge, we propose a novel approach for link prediction in weighted protein-protein networks, where interaction weights denote confidence scores. By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. Our model significantly improves prediction accuracy, surpassing traditional approaches in terms of Mean Absolute Error, Mean Relative Absolute Error, and Root Mean Square Error. Our proposed approach holds the potential for improved accuracy in predicting PPIs, which is crucial for better understanding the underlying biological processes.

High Performance Computing (HPC) systems are ever-evolving, increasing in complexity and heterogeneity, while providing high computing power to scientific and data analysis applications. They are attracting a broad range of users that execute applications from computational chemistry, physics, digital humanities, life sciences, and machine learning, to name a few. Not all users possess the knowledge and expertise to harness the full performance capabilities of complex HPC systems. These users tend to submit inaccurate resource requests for their jobs, especially regarding time limits. This leads to inefficient scheduling of jobs, increased wait times for other users, overall inefficient system utilization, and wasted computing resources and energy, ultimately slowing down scientific discovery. This situation motivates the analysis of the accuracy of resource requests and its reporting to users to raise awareness about job efficiency, improve future job submissions, and reduce job wait times. Existing analyses of job wait times often neglect the connection between the causes of job wait times that can range from user-given and Quality of Service (QoS) limits, to unavailable resources, and approaches of reducing wait times that can be reported to users in an understandable way. In this work, we analyze almost 350’000 jobs collected over 2 months on a local university HPC cluster. Our analysis placed an emphasis on time limit accuracy, QoS characteristics, and reasons for long wait times, as well as how to engage users to improve resource requests. This work shows the importance of analyzing reasons for wait times, approaches to reduce wait times, and motivates further research into supporting users to improve resource requests and system utilization, and to reduce avoidable resource waste.

Applications running on HPC systems waste time and energy if they: (a) use resources inefficiently, (b) deviate from allocation purpose (e.g. cryptocurrency mining), or (c) encounter errors and failures. It is important to know which applications are running on the system, how they use the system, and whether they have been executed before. To recognize known applications during execution on a noisy system, we draw inspiration from the way Shazam recognizes known songs playing in a crowded bar. Our contribution is an Execution Fingerprint Dictionary (EFD) that stores execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs for application recognition. Related work often relies on extensive system monitoring (many system metrics collected over large time windows) and employs machine learning methods to identify applications. Our solution only uses the first 2 minutes and a single system metric to achieve F-scores above 95 percent, providing comparable results to related work but with a fraction of the necessary data and a straightforward mechanism of recognition.

CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still the de facto standard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining. In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.

Adaptation of the dataset shift has grown to be of great importance in machine learning problems in recent years. Reframing has emerged as a new machine learning technique that adapts the context changes between training and target domains. One of the advantages of reframing is that it can offer good performances with a limited amount of deployment data. Reframing has already been implemented in classification and regression by reusing labelled training data with the help of few labelled target data. However, reframing in clustering is still a challenging research problem because of its unsupervised nature. In this paper, we concentrate on building a reframing method for clustering. We also show the necessity and effectiveness of our method in contrast to retraining, which is the process of learning new model in the testing and deployment phases. Our evaluation results with extensive experiments using both synthetic and real-life datasets show that our method correctly identifies most of the shifts between datasets and builds better clustering model than retraining.

Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.

Reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly because the operating context can be expected to vary from training to deployment. Dataset shift is a crucial example of this where training and testing datasets follow different distributions. However, most of the existing dataset shift solving algorithms need costly retraining operation and are not suitable to use the existing model. In this paper, we propose a new approach called reframing to handle dataset shift. The main objective of reframing is to build a model once and make it workable without retraining. We propose two efficient reframing algorithms to learn the optimal shift parameter values using only a small amount of labelled data available in the deployment. Thus, they can transform the shifted input attributes with the optimal parameter values and use the same existing model in several deployment environments without retraining. We have addressed supervised learning tasks both for classification and regression. Extensive experimental results demonstrate the efficiency and effectiveness of our approach compared to the existing solutions. In particular, we report the existence of dataset shift in two real-life datasets. These real-life unknown shifts can also be accurately modeled by our algorithms.

Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data.

In this paper, we present a new approach to enhance the performance of Bayesian classifiers. Our method relies on the combination of two ideas: pairwise classification on the one hand, and threshold optimization on the other hand. Introducing one threshold per pair of classes increases the expressivity of the model, therefore its performance on complex problems such as cost-sensitive problems increases as well. Indeed a comparison of our algorithm to other cost-sensitive approaches shows that it reduces the total misclassification cost.

In order to maintain and improve the quality of life of elderly and people with cognitive impairments in their home, we must elaborate assistive technologies that will alleviate the effects of cognitive decline. The use of ambient intelligence, such as smart homes, is a way to provide assistance services. However, managing these services is not a trivial task. In this paper, we propose an organizational centered multi-agent system (OCMAS) architecture in order to manage these services in a smart home. This approach allows to ease the management of services and the deployment of new services in the smart home system dynamically.

Active learning (AL) is a powerful framework to reduce labeling costs in supervised classification. However, spatial constraints on the sampling design have not yet received much attention and still pose problems for the application of AL on remote sensing data. In this study such issues are addressed in the context of landslide inventory mapping and it is shown that region-based query functions that focus the labeling efforts on compact spatial batches may provide several advantages over point-wise queries.

This paper introduces the implementation of Koza-style tree-based Genetic Programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza's books and then on a real world problem inspired from aeronautics, that extends the results to difficult problems with large data sets.

This paper presents a straightforward implementation of a standard evolutionary algorithm that evaluates its population in parallel on a GPGPU card.