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Recent high-redshift ($z>4$) spatially resolved observations with the James Webb Space Telesescope have shown the evolution of the star formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) and its main sequence in the $\Sigma_{\rm SFR}$-$M_*$ diagram ($\Sigma_{\rm SFR}{\rm MS}$). The $\Sigma_{\rm SFR}{\rm MS}$\ is already observed at cosmic morning ($z\sim7.5$). The use of $\Sigma_{\rm SFR}$\ is physically motivated because it is normalized by the area in which the star formation occurs, and this indirectly considers the gas density. The $\Sigma_{\rm SFR}$-$M_*$ diagram has been shown to complement the widely used (specific) SFR-$M_*$, particularly when selecting passive galaxies. We establish the $\Sigma_{\rm SFR}$\ evolution since $z=12$ in the framework of the L-Galaxies2020 semi-analytical model (SAM), and we interpret recent observations. We estimated $\Sigma_{\rm SFR}$(-$M_*$) and the cosmic star formation rate density (CSFRD) for the simulated galaxy population and for the subsamples, which were divided into stellar mass bins in the given redshift. The simulated $\Sigma_{\rm SFR}$\ decreases by $\sim3.5$ dex from $z=12$ to $z=0$. We show that galaxies with different stellar masses have different paths of $\Sigma_{\rm SFR}$\ evolution. We find that $\Sigma_{\rm SFR}{\rm MS}$\ is already observed at $z\sim11$. The simulated $\Sigma_{\rm SFR}{\rm MS}$\ agrees with the observed one at $z=0, 1, 2, 5$, and $7.5$ and with individual galaxies at $z>10$. We show that the highest $\Sigma_{\rm SFR}{\rm MS}$\ slope of $0.709\pm0.005$ is at $z\sim3$ and decreases to $\sim0.085\pm0.003$ at $z=0$. This is mostly driven by a rapid decrease in SFR with an additional size increase for the most massive galaxies in this redshift range. This coincides with the dominance of the most massive galaxies in the CSFRD from the SAM., Comment: 7 pages, 4 figures, 2 tables. Accepted in A&A Letters

Quenching of star-formation plays a fundamental role in galaxy evolution. This process occurs due to the removal of the cold interstellar medium (ISM) or stabilization against collapse, so that gas cannot be used in the formation of new stars. In this paper, we study the effect of different mechanisms of ISM removal. In particular, we revised the well-known Baldwin-Philips-Terlevich (BPT) and $\mathrm{EW_{H\alpha}}$ vs. $\mathrm{[NII]/H\alpha}$ (WHAN) emission-line ratio diagnostics, so that we could classify all galaxies, even those not detected at some emission lines, introducing several new spectral classes. We use spectroscopic data and several physical parameters of 2409 dusty early-type galaxies in order to find out the dominant ionization source [active galactic nuclei (AGNs), young massive stars, hot low-mass evolved stars (HOLMES)] and its effect on the ISM. We find that strong AGNs can play a significant role in the ISM removal process only for galaxies with ages lower than $10^{9.4}$ yr, but we cannot rule out the influence of weak AGNs at any age. For older galaxies, HOLMES/planetary nebulae contribute significantly to the ISM removal process. Additionally, we provide the BPT and WHAN classifications not only for the selected sample but also for all 300000 galaxies in the GAMA fields., Comment: 31 pages, 16 figures, 4 tables, accepted for publication in ApJS

This paper presents an overview of the Volvo Discovery Challenge, held during the ECML-PKDD 2024 conference. The challenge's goal was to predict the failure risk of an anonymized component in Volvo trucks using a newly published dataset. The test data included observations from two generations (gen1 and gen2) of the component, while the training data was provided only for gen1. The challenge attracted 52 data scientists from around the world who submitted a total of 791 entries. We provide a brief description of the problem definition, challenge setup, and statistics about the submissions. In the section on winning methodologies, the first, second, and third-place winners of the competition briefly describe their proposed methods and provide GitHub links to their implemented code. The shared code can be interesting as an advanced methodology for researchers in the predictive maintenance domain. The competition was hosted on the Codabench platform., Comment: ECML/PKDD 2024, Discovery Challenge

Removing cold interstellar medium (ISM) from a galaxy is central to quenching star formation. However, the exact mechanism of this process remains unclear. The objective of this work is to find the mechanism responsible for dust and gas removal in simulated early-type galaxies (ETGs). A statistically significant sample of massive (M_*>$10^{10}$M$_\odot$), simulated ETG in a redshift range of 0.02--0.32 is studied in the context of its ISM properties. In particular, we investigate the cold dust and gas removal timescales, the cold gas inflows, and their relation with black hole (BH) mass. We also investigate the evolution of galaxies in the dust vs. star formation rate (SFR) plane and the influence of merger events. We find agreement with previous observational works considering the timescales of dust and HI removal from ETGs. When considering the dust-to-stellar mass ratio as a function of time in simulations, we recovered a similar decline as in the observational sample as a function of stellar age, validating its use for timing the ISM decline. Moreover, we recover the observed relation between dust mass and SFR for actively star-forming galaxies as well as for passive ETGs. We also show that starburst galaxies form their own sequence on the dust vs. SFR plot in a form $\log(M_{\rm dust, SB})= 0.913\times \log({\rm SFR}) + 6.533$ with $2\sigma$ scatter of 0.32. Finally, we find that type II supernova reverse shocks dominate the dust destruction at the early stages of ETG evolution, while at later times stellar feedback becomes more important. We show that merger events lead to morphological transformations by increasing the bulge-to-total stellar mass ratio followed by an increase in BH masses. The BH feedback resulting from radio mode accretion prevents the hot halo gas from cooling, indirectly leading to a decrease in the SFR., Comment: 15 pages, 12 figures, accepted in A&A

Unsupervised meta-learning aims to learn feature representations from unsupervised datasets that can transfer to downstream tasks with limited labeled data. In this paper, we propose a novel approach to unsupervised meta-learning that leverages the generalization abilities of in-context learning observed in transformer architectures. Our method reframes meta-learning as a sequence modeling problem, enabling the transformer encoder to learn task context from support images and utilize it to predict query images. At the core of our approach lies the creation of diverse tasks generated using a combination of data augmentations and a mixing strategy that challenges the model during training while fostering generalization to unseen tasks at test time. Experimental results on benchmark datasets showcase the superiority of our approach over existing unsupervised meta-learning baselines, establishing it as the new state-of-the-art in the field. Remarkably, our method achieves competitive results with supervised and self-supervised approaches, underscoring the efficacy of the model in leveraging generalization over memorization.

Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spatio-temporal features in multivariate time series. However, these data-driven models often fail to capture the essential spatio-temporal relationships when significant signal corruption occurs. Additionally, calculating the high-order neighbor nodes in these models is of high computational complexity. To address these problems, we propose a novel higher-order spatio-temporal physics-incorporated GNN (HSPGNN). Firstly, the dynamic Laplacian matrix can be obtained by the spatial attention mechanism. Then, the generic inhomogeneous partial differential equation (PDE) of physical dynamic systems is used to construct the dynamic higher-order spatio-temporal GNN to obtain the missing time series values. Moreover, we estimate the missing impact by Normalizing Flows (NF) to evaluate the importance of each node in the graph for better explainability. Experimental results on four benchmark datasets demonstrate the effectiveness of HSPGNN and the superior performance when combining various order neighbor nodes. Also, graph-like optical flow, dynamic graphs, and missing impact can be obtained naturally by HSPGNN, which provides better dynamic analysis and explanation than traditional data-driven models. Our code is available at https://github.com/gorgen2020/HSPGNN., Comment: 18 pages, 7 figures, CIKM 2024

Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a new machine learning model. An approximation (also known as meta-model or a surrogate) of the true function can be used in such applications to alleviate the computation cost. In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. We define 'Approximation Usefulness' to capture the necessary conditions to ensure correctness of the EA computations when an approximation is used. Based on this definition, we propose a procedure to construct a lightweight qualitative meta-model by the active selection of data instances. We then use a meta-model to carry out the feature selection task. We apply this procedure to the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation) to create a Qualitative approXimations variant, CHCQX. We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy (as compared to CHC), particularly for large datasets with over 100K instances. We also demonstrate the applicability of the thinking behind our approach more broadly to Swarm Intelligence (SI), another branch of the Evolutionary Computation (EC) paradigm with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available.

This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs). Randomization, like weight noise and dropout, aids in reducing overfitting and enhancing generalization, but their interactions are poorly understood. The study categorizes randomness techniques into four types and proposes new methods: adding noise to the loss function and random masking of gradient updates. Using Particle Swarm Optimizer (PSO) for hyperparameter optimization, it explores optimal configurations across MNIST, FASHION-MNIST, CIFAR10, and CIFAR100 datasets. Over 30,000 configurations are evaluated, revealing data augmentation and weight initialization randomness as main performance contributors. Correlation analysis shows different optimizers prefer distinct randomization types. The complete implementation and dataset are available on GitHub.

To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environmental conditions. In this paper, a high resolution onboard data from passenger vessels in short-sea shipping (SSS) have been collected and preprocessed. We first investigated the available data to deploy it effectively to model the physics of the vessel, and hence the vessel performance. Since in SSS, the weather measurements and forecasts might have not been in temporal and spatial resolutions that accurately representing the actual environmental conditions. Then, We proposed a data-driven modeling approach for vessel energy efficiency. This approach addresses the challenges of data representation and energy modeling by combining and aggregating data from multiple sources and seamlessly integrates explainable artificial intelligence (XAI) to attain clear insights about the energy efficiency for a vessel in SSS. After that, the developed model of energy efficiency has been utilized in developing a framework for optimizing the vessel voyage to minimize the fuel consumption and meeting the constraint of arrival time. Moreover, we developed a spatial clustering approach for labeling the vessel paths to detect the paths for vessels with operating routes of repeatable and semi-repeatable paths., Comment: 20 pages, 28 figures, 9 tables, Keywords: Short-sea shipping, energy efficiency, spatiotemporal aggregation, voyage speed optimization, Time-series analysis, spatial clustering, vessel path identification, maritime transportation. arXiv admin note: substantial text overlap with arXiv:2402.00698, arXiv:2403.05778

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation., Comment: Keywords: Spatial clustering, vessel path identification, maritime transportation, average nearest neighbor distance, hierarchical clustering, likelihood estimation. This preprint has 12 pages, 14 figures, 2 tables

Several approaches have been developed for improving the ship energy efficiency, thereby reducing operating costs and ensuring compliance with climate change mitigation regulations. Many of these approaches will heavily depend on measured data from onboard IoT devices, including operational and environmental information, as well as external data sources for additional navigational data. In this paper, we develop a framework that implements time-series analysis techniques to optimize the vessel's speed profile for improving the vessel's energy efficiency. We present a case study involving a real-world data from a passenger vessel that was collected over a span of 15 months in the south of Sweden. The results indicate that the implemented models exhibit a range of outcomes and adaptability across different scenarios. The findings highlight the effectiveness of time-series analysis approach for optimizing vessel voyages within the context of constrained landscapes, as often seen in short-sea shipping., Comment: 8 pages, 7 figures and four tables

This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistical heterogeneity between the client's data, which degrades performance and slows down the convergence toward the global model. In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client. This becomes particularly important under empirical concept shifts among clients, rather than merely considering imbalanced classes, which have been studied until now. Therefore, we propose a method for knowledge transfer between clients where the server trains client-specific generators. Each generator generates samples for the corresponding client to remove the conflict with other clients' models. Experiments conducted on synthetic and real data, along with a theoretical study, support the effectiveness of our method in constructing a well-generalizable global model by reducing the conflict between local models.

Transient absorption (TA) spectroscopy has proved fundamental to our understanding of energy and charge transfer in biological systems, allowing measurements of photoactive proteins on sub-picosecond timescales. Recently, ultrafast TA spectroscopy has been applied in vivo, providing sub-picosecond measurements of photosynthetic light harvesting and electron transfer processes within living photosynthetic microorganisms. The analysis of the resultant data is hindered by the number of different photoactive pigments and the associated complexity of photoactive reaction schemes within living cells. Here we show how in vivo ultrafast TA spectroscopy can be applied to a diverse array of organisms from the tree of life, both photosynthetic and non-photosynthetic. We have developed a series of software tools for performing global, lifetime and target analysis of in vivo TA datasets. These advances establish in vivo TA spectroscopy as a versatile technique for studying energy and charge transfer in living systems., Comment: 39 pages

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research., Comment: 51 pages, 9 figures

A fundamental feature of human intelligence is the ability to infer high-level abstractions from low-level sensory data. An essential component of such inference is the ability to discover modularized generative mechanisms. Despite many efforts to use statistical learning and pattern recognition for finding disentangled factors, arguably human intelligence remains unmatched in this area. In this paper, we investigate a problem of learning, in a fully unsupervised manner, the inverse of a set of independent mechanisms from distorted data points. We postulate, and justify this claim with experimental results, that an important weakness of existing machine learning solutions lies in the insufficiency of cross-module diversification. Addressing this crucial discrepancy between human and machine intelligence is an important challenge for pattern recognition systems. To this end, our work proposes an unsupervised method that discovers and disentangles a set of independent mechanisms from unlabeled data, and learns how to invert them. A number of experts compete against each other for individual data points in an adversarial setting: one that best inverses the (unknown) generative mechanism is the winner. We demonstrate that introducing an orthogonalization layer into the expert architectures enforces additional diversity in the outputs, leading to significantly better separability. Moreover, we propose a procedure for relocating data points between experts to further prevent any one from claiming multiple mechanisms. We experimentally illustrate that these techniques allow discovery and modularization of much less pronounced transformations, in addition to considerably faster convergence.

Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relationship between the neighbour nodes of the dynamically generated graphs, which can easily lead to a risk in subsequent decisions. Moreover, few of them consider the uncertainty and noise of dynamic graphs based on the time series datasets, which are ubiquitous in real-world graph structure networks. In this paper, we propose a novel Dynamic Diffusion-Variational Graph Neural Network (DVGNN) for spatio-temporal forecasting. For dynamic graph construction, an unsupervised generative model is devised. Two layers of graph convolutional network (GCN) are applied to calculate the posterior distribution of the latent node embeddings in the encoder stage. Then, a diffusion model is used to infer the dynamic link probability and reconstruct causal graphs in the decoder stage adaptively. The new loss function is derived theoretically, and the reparameterization trick is adopted in estimating the probability distribution of the dynamic graphs by Evidence Lower Bound during the backpropagation period. After obtaining the generated graphs, dynamic GCN and temporal attention are applied to predict future states. Experiments are conducted on four real-world datasets of different graph structures in different domains. The results demonstrate that the proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result while exhibiting higher robustness. Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs.

In the latent bandit problem, the learner has access to reward distributions and -- for the non-stationary variant -- transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent's beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times. In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent's beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods.

Photosystems II and I (PSII and PSI) are the reaction centre complexes that drive the light reactions of photosynthesis. PSII performs light-driven water oxidation (quantum efficiencies and catalysis rates of up to 80% and 1000 $e^{-}\text{s}^{-1}$, respectively) and PSI further photo-energises the harvested electrons (quantum efficiencies of ~100%). The impressive performance of the light harvesting components of photosynthesis has motivated extensive biological, artificial and biohybrid approaches to re-wire photosynthesis to enable higher efficiencies and new reaction pathways, such as H2 evolution or alternative CO2 fixation. To date these approaches have focussed on charge extraction at the terminal electron quinones of PSII and terminal iron-sulfur clusters of PSI. Ideally electron extraction would be possible immediately from the photoexcited reaction centres to enable the greatest thermodynamic gains. However, this was believed to be impossible because the reaction centres are buried around 4 nm within PSII and 5 nm within PSI from the cytoplasmic face. Here, we demonstrate using in vivo ultrafast transient absorption (TA) spectroscopy that it is possible to extract electrons directly from photoexcited PSI and PSII, using both live cyanobacterial cells and isolated photosystems, with the exogenous electron mediator 2,6-dichloro1,4-benzoquinone (DCBQ). We postulate that DCBQ can oxidise peripheral chlorophyll pigments participating in highly delocalised charge transfer (CT) states after initial photoexcitation. Our results open new avenues to study and re-wire photosynthesis for bioenergy and semi-artificial photosynthesis.

Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets. We design and evaluate a Surrogate-Assisted Genetic Algorithm (SAGA) which utilizes this concept to guide the evolutionary search during the early phase of exploration. SAGA only switches to evaluating the original function at the final exploitation phase. We prove that the run-time upper bound of SAGA surrogate-assisted stage is at worse equal to the wrapper GA, and it scales better for induction algorithms of high order of complexity in number of instances. We demonstrate, using 14 datasets from the UCI ML repository, that in practice SAGA significantly reduces the computation time compared to a baseline wrapper Genetic Algorithm (GA), while converging to solutions of significantly higher accuracy. Our experiments show that SAGA can arrive at near-optimal solutions three times faster than a wrapper GA, on average. We also showcase the importance of evolution control approach designed to prevent surrogates from misleading the evolutionary search towards false optima.

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we leverage active learning and ensembles to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts), that automatically creates contexts from the feature set. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual and non-contextual anomaly detectors, and supervised classifiers) on seven datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary.

Using hierarchies of classes is one of the standard methods to solve multi-class classification problems. In the literature, selecting the right hierarchy is considered to play a key role in improving classification performance. Although different methods have been proposed, there is still a lack of understanding of what makes one method to extract hierarchies perform better or worse. To this effect, we analyze and compare some of the most popular approaches to extracting hierarchies. We identify some common pitfalls that may lead practitioners to make misleading conclusions about their methods. In addition, to address some of these problems, we demonstrate that using random hierarchies is an appropriate benchmark to assess how the hierarchy's quality affects the classification performance. In particular, we show how the hierarchy's quality can become irrelevant depending on the experimental setup: when using powerful enough classifiers, the final performance is not affected by the quality of the hierarchy. We also show how comparing the effect of the hierarchies against non-hierarchical approaches might incorrectly indicate their superiority. Our results confirm that datasets with a high number of classes generally present complex structures in how these classes relate to each other. In these datasets, the right hierarchy can dramatically improve classification performance.

We propose a new sequential decision-making setting, combining key aspects of two established online learning problems with bandit feedback. The optimal action to play at any given moment is contingent on an underlying changing state which is not directly observable by the agent. Each state is associated with a context distribution, possibly corrupted, allowing the agent to identify the state. Furthermore, states evolve in a Markovian fashion, providing useful information to estimate the current state via state history. In the proposed problem setting, we tackle the challenge of deciding on which of the two sources of information the agent should base its arm selection. We present an algorithm that uses a referee to dynamically combine the policies of a contextual bandit and a multi-armed bandit. We capture the time-correlation of states through iteratively learning the action-reward transition model, allowing for efficient exploration of actions. Our setting is motivated by adaptive mobile health (mHealth) interventions. Users transition through different, time-correlated, but only partially observable internal states, determining their current needs. The side information associated with each internal state might not always be reliable, and standard approaches solely rely on the context risk of incurring high regret. Similarly, some users might exhibit weaker correlations between subsequent states, leading to approaches that solely rely on state transitions risking the same. We analyze our setting and algorithm in terms of regret lower bound and upper bounds and evaluate our method on simulated medication adherence intervention data and several real-world data sets, showing improved empirical performance compared to several popular algorithms.

The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.

In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure to overcome the limitations of one-size-fits-all solutions. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI., Comment: Submitted to Expert Systems with Applications

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML., Comment: ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK), August 2019 Anchorage, USA

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers' heat-use habits.

We consider a Sturm-Liouville equation $\ell y:=-y'' + qy = \lambda y$ on the intervals $(-a,0)$ and $(0,b)$ with $a,b>0$ and $q \in L^2(-a,b)$. We impose boundary conditions $y(-a)\cos\alpha = y'(-a)\sin\alpha$, $y(b)\cos\beta = y'(b)\sin\beta$, where $\alpha \in [0,\pi)$ and $\beta \in (0,\pi]$, together with transmission conditions rationally-dependent on the eigenparameter via \begin{align*} -y(0^+)\left(\lambda \eta -\xi-\sum\limits_{i=1}^{N} \frac{b_i^2}{\lambda -c_i}\right) &= y'(0^+) - y'(0^-),\\ y'(0^-)\left(\lambda \kappa +\zeta-\sum\limits_{j=1}^{M}\frac{a_j^2}{\lambda -d_j}\right) &= y(0^+) - y(0^-), \end{align*} with $b_i, a_j>0$ for $i=1,\dots,N,$ and $j=1,\dots,M$. Here we take $\eta, \kappa \ge 0$ and $N,M\in \N_0$. The geometric multiplicity of the eigenvalues is considered and the cases in which the multiplicity can be $2$ are characterized. An example is given to illustrate the cases. A Hilbert space formulation of the above eigenvalue problem as a self-adjoint operator eigenvalue problem in $L^2(-a,b)\bigoplus \C^{N^*} \bigoplus \C^{M^*}$, for suitable $N^*,M^*$, is given. The Green's function and the resolvent of the related Hilbert space operator are expressed explicitly.

We consider a Sturm-Liouville operator on a finite interval as well as a scattering problem on the real line both with transfer conditions at the origin. On a finite interval we show that the the Titchmarsh-Weyl $m$-function can be uniquely determined from two spectra for the same equation but with varied boundary conditions at one end of the interval. In addition, we prove that the $m$-function can also be uniquely reconstructed from one spectrum and the corresponding norming constants. For the scattering problem on the real line we assume that the potential has compact essential support. For a given symmetric finite intervals containing the essential-support of the potential and a pair of separated boundary conditions imposed at the ends of the interval, the spectrum and corresponding norming constants can be uniquely recoverable from the scattering data on $\R$. Consequently the potential and transfer matrix can be determined.

The inverse scattering problem for Sturm-Liouville operators on the line with a matrix transfer condition at the origin is considered. We show that the transfer matrix can be reconstructed from the eigenvalues and reflection coefficient. In addition, for potentials with compact essential support, we show that the potential can be uniquely reconstructed.

Not only the Dirac operator, but also the spinor bundle of a pseudo-Riemannian manifold depends on the underlying metric. This leads to technical difficulties in the study of problems where many metrics are involved, for instance in variational theory. We construct a natural finite dimensional bundle, from which all the metric spinor bundles can be recovered including their extra structure. In the Lorentzian case, we also give some applications to Einstein-Dirac-Maxwell theory as a variational theory and show how to coherently define a maximal Cauchy development for this theory., Comment: 20 pages, presentation simplified, result about universal Dirac-operator added

In this article, we prove that on any compact spin manifold of dimension m congruent 0,6,7 mod 8, there exists a metric, for which the associated Dirac operator has at least one eigenvalue of multiplicity at least two. We prove this by catching the desired metric in a subspace of Riemannian metrics with a loop that is not homotopically trivial. We show how this can be done on the sphere with a loop of metrics induced by a family of rotations. Finally, we transport this loop to an arbitrary manifold (of suitable dimension) by extending some known results about surgery theory on spin manifolds., Comment: 23 pages, slightly improved article version of arXiv:1501.04045

Let M be a closed spin manifold of dimension at least three with a fixed topological spin structure. For any Riemannian metric, we can construct the associated Dirac operator. The spectrum of this Dirac operator depends on the metric of course. In 2005, Dahl conjectured that M can be given a metric, for which a finite part of the spectrum consists of arbitrarily prescribed eigenvalues of arbitrary (finite) multiplicity. The only constraints one has to respect are the exception of the zero eigenvalue (due to the Atiyah-Singer index theorem) and in certain dimensions, the quaternionic structure of the eigenspaces and also the symmetry of the spectrum. Dahl also proved his conjecture in case all eigenvalues have simple multiplicities. The question, if one can prescribe arbitrary multiplicities, or if the existence of eigenvalues of higher multiplicity might somehow be topologically obstructed, has been open ever since. In this thesis, we prove that on any closed spin manifold of dimension m congruent 0, 6, 7 mod 8, there exists a metric for which at least one eigenvalue is of higher multiplicity. For the proof, we introduce a technique which "catches" the desired metric with a loop in the space of all Riemannian metrics. We will construct such a loop on the sphere and transport it to a general manifold by extending some classical surgery theory results by B\"ar and Dahl. As a preparation, we will show that the Dirac spectrum can be described globally by a continuous family of functions on the space of Riemannian metrics and that the spinor field bundles with respect to the various metrics assemble to a continuous bundle of Hilbert spaces. These results might be useful in their own right., Comment: PhD thesis, 128 pages, 13 figures, contains an updated version of arXiv:1303.6561

It is a well-known fact that on a bounded spectral interval the Dirac spectrum can be described locally by a non-decreasing sequence of continuous functions of the Riemannian metric. In the present article we extend this result to a global version. We think of the spectrum of a Dirac operator as a function from the integers to the reals and endow the space of all spectra with an arsinh-uniform metric. We prove that the spectrum of the Dirac operator depends continuously on the Riemannian metric. As a corollary, we obtain the existence of a non-decreasing family of functions on the space of all Riemannian metrics, which represents the entire Dirac spectrum at any metric. We also show that in general these functions do not descend to the space of Riemannian metrics modulo spin diffeomorphisms due to spectral flow., Comment: 25 pages, 3 figures

The method of recurrence plots is extended to the cross recurrence plots (CRP), which among others enables the study of synchronization or time differences in two time series. This is emphasized in a distorted main diagonal in the cross recurrence plot, the line of synchronization (LOS). A non-parametrical fit of this LOS can be used to rescale the time axis of the two data series (whereby one of it is e.g. compressed or stretched) so that they are synchronized. An application of this method to geophysical sediment core data illustrates its suitability for real data. The rock magnetic data of two different sediment cores from the Makarov Basin can be adjusted to each other by using this method, so that they are comparable., Comment: Nonlinear Processes in Geophysics, 9, 2002, in press

This paper describes the experience of the Clemson University (South Carolina) faculty in choosing and administering a commercially available standardized test to assess general education at that institution. The paper emphasizes the importance of faculty involvement in the assessment of general education and its impact on the success of the process and the acceptance of the findings. The structure of general education at Clemson University is described as requiring students to take 38 semester hours in courses from 5 concentrations, with faculty having primary responsibility for these requirements and their evaluation. The process of selecting a standardized test, the concerns connected with that decision, and the value of using a commercial test are discussed. Also described is the assessment process itself, which involved testing random samples of the freshmen and junior classes, scoring by the testing company, and independent rating of the composition portion of the test. The use of the test results is detailed, covering the extent of dissemination of the results to students and staff, responses from students, and developments for future testing. Included are 11 references. (JB)

Investigates the need to include computer screens in documentation for software using a graphical user interface. Describes the uses and purposes of "minimal manuals" and their principles. Studies student reaction to their use of one of three on-screen manuals: screens, icon, and button. Finds some benefit for including icon and button information in the documentation. (HB)

Discussion of hypertext systems and navigation focuses on a study conducted with undergraduates at Clemson University (South Carolina) that compared a hypertext browsing system with a linearly based system for accessing information concerning college transfer courses. Results indicate that user perceptions and performance are more favorable with the hypertext system. (Contains 12 references.) (LRW)

This report summarizes academic research which has resulted in an increased appreciation for multidisciplinary efforts among our students, colleagues and administrators. It has also generated a number of research ideas that emerged from the interaction between disciplines. Overall, 17 undergraduate students and 16 graduate students benefited directly from the NASA grant: an additional 11 graduate students were impacted and participated without financial support from NASA. The work resulted in 16 theses (with 7 to be completed in the near future), 67 papers or reports mostly published in 8 journals and/or presented at various conferences (a total of 83 papers, presentations and reports published based on NASA inspired or supported work). In addition, the faculty and students presented related work at many meetings, and continuing work has been proposed to NSF, the Army, Industry and other state and federal institutions to continue efforts in the direction of multidisciplinary and recently multi-objective design and analysis. The specific problem addressed is component packing which was solved as a multi-objective problem using iterative genetic algorithms and decomposition. Further testing and refinement of the methodology developed is presently under investigation. Teaming issues research and classes resulted in the publication of a web site, (http://design.eng.clemson.edu/psych4991) which provides pointers and techniques to interested parties. Specific advantages of using iterative genetic algorithms, hurdles faced and resolved, and institutional difficulties associated with multi-discipline teaming are described in some detail.

The perceptions of engineers and scientists at NASA Langley Research Center toward engineering design teams were evaluated. A sample of 49 engineers and scientists rated 60 team behaviors in terms of their relative importance for team success. They also completed a profile of their own perceptions of their strengths and weaknesses as team members. Behaviors related to team success are discussed in terms of those involving the organizational culture and commitment to the team and those dealing with internal team dynamics. The latter behaviors focused on team issues occurring during the early stages of a team's existence. They included the level and extent of debate and discussion regarding methods for completing the team task and the efficient use of team time to explore and discuss methodologies critical to the problem. The discussion includes a comparison of engineering teams with the prototypical business team portrayed in the literature.

Using the formalism provided by the Systems Thinking approach, the dynamics present when operating multidisciplinary teams are examined in the context of the NASA Langley Research and Technology Group, an R&D organization organized along functional lines. The paper focuses on external dynamics and examines how an organization creates and nurtures the teams and how it disseminates and retains the lessons and expertise created by the multidisciplinary activities. Key variables are selected and the causal relationships between the variables are identified. Five "stories" are told, each of which touches on a different aspect of the dynamics. The Systems Thinking Approach provides recommendations as to interventions that will facilitate the introduction of multidisciplinary teams and that therefore will increase the likelihood of performing successful multidisciplinary developments. These interventions can be carried out either by individual researchers, line management or program management.

Multidisciplinary analysis and design is inherently a team activity due to the variety of required expertise and knowledge. As a team activity, multidisciplinary research cannot escape the issues that affect all teams. The level of technical diversity required to perform multidisciplinary analysis and design makes the teaming aspects even more important. A study was conducted at the NASA Langley Research Center to develop a model of multidiscipline teams that can be used to help understand their dynamics and identify key factors that influence their effectiveness. The study sought to apply the elements of systems thinking to better understand the factors, both generic and Langley-specific, that influence the effectiveness of multidiscipline teams. The model of multidiscipline research teams developed during this study has been valuable in identifying means to enhance team effectiveness, recognize and avoid problem behaviors, and provide guidance for forming and coordinating multidiscipline teams.

The perceptions of a sample of 49 engineers and scientists from NASA Langley Research Center toward engineering design teams were evaluated. The respondents rated 60 team behaviors in terms of their relative importance for team success. They also completed a profile of their own perceptions of their strengths and weaknesses as team members. Behaviors related to team success are discussed in terms of those involving the organizational culture and commitment to the team and those dealing with internal team dynamics. The latter behaviors included the level and extent of debate and discussion regarding methods for completing the team task and the efficient use of team time to explore and discuss methodologies critical to the problem. Successful engineering teams may find their greatest challenges occurring during the early stages of their existence. In contrast to the prototypical business team, members on an engineering design share expertise and knowledge which allows them to deal with task issues sooner. However, discipline differences among team members can lead to conflicts regarding the best method or approach to solving the engineering problem.

Research directed toward a better understanding of the computer user/computer machine relationship has increased in recent years. To identify what factors may predict success in computer programming, 286 college students from three computer classes (160 from introductory programming; 60 from Cobol programming; and 66 from senior level programming) completed a 30-minute question answering and problem solving test. Cognitive factors (past academic performance and problem solving ability) were assessed through self-reported grades and seven math and logic problems. Personality factors (computer anxiety and locus of control) were assessed by items from the Fennema and Sherman Mathematics Attitude Scale and a modified internal/external (I-E) scale of personal locus of control. An analysis of the results showed that past academic performance in mathematics and English courses, amount of previous computer experience, expected grade in the course, and performance on selected logic and algebraic-word problems were significantly correlated to course performance. Neither of the personality factors predicted performance, since few students showed any computer anxiety and most showed an internal locus of control. (BL)