Search

Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware. Compilers try to address these problems; however, they are limited by design in what they can do. To address this, we present CryptOpt, an automatic optimizer for long stretches of straightline code. Experimental results across eight hardware platforms show that CryptOpt achieves a speed-up factor of up to 2.56 over current off-the-shelf compilers.

Rutile GeO2 has been predicted to be an ultra-wide bandgap semiconductor suitable for future power electronics devices while quartz-like GeO2 shows piezoelectric properties. To explore these crystalline phases for application and fundamental materials investigations, molecular beam epitaxy (MBE) is a well-suited thin film growth technique. In this study, we investigate the reaction kinetics of GeO2 during plasma-assisted MBE using elemental Ge and plasma-activated oxygen fluxes. The growth rate as a function of oxygen flux is measured in-situ by laser reflectometry at different growth temperatures. A flux of the suboxide GeO desorbing off the growth surface is identified and quantified in-situ by the line-of-sight quadrupole mass spectrometry. Our measurements reveal that the suboxide formation and desorption limits the growth rate under metal-rich or high temperature growth conditions, and leads to etching of the grown GeO2 layer under Ge flux in the absence of oxygen. The quantitative results fit the sub-compound mediated reaction model, indicating the intermediate formation of the suboxide at the growth front. This model is further utilized to delineate the GeO2-growth window in terms of oxygen-flux and substrate temperature. Our study can serve as a guidance for the thin film synthesis of GeO2 and defect-free mesa etching in future GeO2-device processing.

Although information theory has found success in disciplines, the literature on its applications to software evolution is limit. We are still missing artifacts that leverage the data and tooling available to measure how the information content of a project can be a proxy for its complexity. In this work, we explore two definitions of entropy, one structural and one textual, and apply it to the historical progression of the commit history of 25 open source projects. We produce evidence that they generally are highly correlated. We also observed that they display weak and unstable correlations with other complexity metrics. Our preliminary investigation of outliers shows an unexpected high frequency of events where there is considerable change in the information content of the project, suggesting that such outliers may inform a definition of surprisal., 8 pages, 6 figures, Accepted at the NLBSE 2023 workshop

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have far-reaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network's development team. To tackle this challenge, we present Socialz, a novel approach to social fuzz testing that (1) characterises real users of a social network, (2) diversifies their interaction using evolutionary computation across multiple, non-trivial features, and (3) collects performance data as these interactions are executed. With Socialz, we aim to provide anyone with the capability to perform comprehensive social testing, thereby improving the reliability and security of online social networks used around the world.

This report aims to summarize the journey of the automotive demonstrator, including the need for the ReCiPSS platform, the setting-up of the ReCiPSS platform, and the benefits (technical, financial, and environmental) achieved due to the platform. The need for the ReCiPSS platform is evident from the reverse logistics of used products or components in the following, further referred to as cores, for remanufacturing in the circular economy is overly complex since the forward and reverse logistics must be seamlessly integrated for efficient core collection. Even in a simple reverse supply chain consisting of automotive workshops, wholesaler outlets, wholesaler central, and remanufactured, repeated inspections do not provide any substantial financial or logistical benefit but increase the complexity and risk of core returns for all stakeholders involved. The main goal for the demo phase was to physically handle and return up to 80,000 cores from the automotive workshops to the remanufacturers. This was shown by digitally incorporating the concept of the “right to return a core” and making it transferable in the independent automotive aftermarket. To demonstrate the service, two wholesalers were involved which collected more than 90,000 cores during the project period until November 2022. During the pilot, the cores were identified based on technical criteria and were evaluated and accepted/rejected based on the specific technical and commercial guidelines of the individual remanufacturers. All process stages (identification, evaluation, sorting) have been implemented as an automated workflow using the ReCiPSS automotive platform and the CoremanNet data management system. The ReCiPSS platform compiles and provides extensive reports on core return options to understand and control the commercial opportunities and risks. This reduces the complexity for the wholesalers, as they can make an informed decision about whether to accept or reject a core and where to route it. The major challenges for the core return flow were multiple inspections and evaluations without any value addition and unclear crediting conditions for the stakeholders involved, such as wholesalers and remanufacturers. The identification process dealt with identifying the core as precisely as possible. The evaluation criteria include not only a technical, physical, or visual evaluation of the state of the part but also a commercial evaluation. The commercial evaluation is taken care of by specialized IT systems, which enhances the decision-making for the evaluation of the cores. The intention behind processing many cores was to understand the applicability and relevance of the new concept on an industrial scale and to develop a statistically significant case that could be used to support circularity in the future. The resulting benefits span from reduced complexity of core management to improved transparency due to the implementation of procedures for commercial compensation of cores. The trust in core management is strengthened, and the commercial value is better understood and estimated with enhanced visibility in the ReCiPSS platform. This in turn, will pave the way for strengthening the circular economy within the remanufacturing business and improving the resource efficiency of materials in general.

How does the behavior of political elites affect the relationship between different voter groups? How does political exclusion, such as a cordon sanitaire – or disapproval of specific potential coalitions in a party system – affect public sentiment? Most recently the literature on affective polarization has begun to answer the first question, with single case studies showing the relevance of elite’s behavior on citizens. We add and move beyond this literature by tackling the second question in a comparative design. We look at the cases of Austria, the Flemish Region, Germany, and Spain. While parties in the Flemish Region and Germany have so far enforced a strong political exclusion towards the extreme right, this tendency is much weaker in Austria and Spain. We implement an online survey experiment that shows hypothetical newspaper stories to the respondents. Whereas the control group will only learn about recent opinion polls and parties’ history of co-governance, the newspaper will also report to the treatment group that the main center-right party is willing to govern with the radical-right party. We expect that coalition signals from the center-right party to the radical-right party will reduce citizens’ dislike for supporters of the radical-right party, especially for center-right party supporters. Similarly, we expect that such a signal will reduce radical-right party supporters’ dislike for other parties’ supporters. We hypothesize that these effects are stronger in cases with a cordon sanitaire (Germany/Flemish Region) compared to those without (Austria/Spain). Finally, we hypothesize that center-left voters will instead display an increased dislike towards supporters of the center-right party, as they facilitate the radical-right’s road to governance. Our results will have important implications for the literature on affective dislike and affective polarization more generally, particularly when examining potential solutions to their adverse consequences.

We provide an open source framework to experiment with evolutionary algorithms which we call "Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA)". ELEA is browser-based and allows to assemble evolutionary algorithms using drag-and-drop, starting from a number of simple pre-designed examples, making the startup costs for employing the toolkit minimal. The designed examples can be executed and collected data can be displayed graphically. Further features include export of algorithm designs and experimental results as well as multi-threading. With the very intuitive user interface and the short time to get initial experiments going, this tool is especially suitable for explorative analyses of algorithms as well as for the use in classrooms., Comment: You can find a running instance of ELEA as well as its source code at the following URLs: https://elea-toolkit.netlify.app/ https://github.com/HPI-ELEA/elea

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work's approach that GitHub developers can configure to automatically scan for outdated code element references in their GitHub project's documentation whenever a pull request is submitted.

The rapidly growing literature on affective polarization has predominately focused on party identification as a basis of out-group animosity. This makes sense in an American context where partisanship is perfectly aligned with political ideology, and also, albeit to a much lesser extent, somewhat aligned with policy attitudes. However, comparative scholars have questioned whether the concept of party identification can be seamlessly applied to Western European democracies (Budge and Farlie, 1976). For instance, there may be multiple party identifications (Garry 2003). Moreover, political identities in the European context may be more multi-faceted including not only party identification but also attachment to a position, or at least a general area, on the ideological left/right dimension (Bolstad and Dinas 2016, Vegetti and Sirinic 2018). This is what Ellis and Stimson (2013) term “symbolic ideology”. Recently, Oshri et al. (2022) have also argued that in some political systems (in their case, Israel), attachments can form towards ideological groups as well. This study also builds on recent work by Orr and Huber (2021) as well as Diaz and Lelkes (2022) that examines whether reactions to partisan affiliation are mostly based on the policy positions that partisanship implies. We add to this debate the notion that what people take from partisan support can also be an ideological attachment towards the Left or Right. This study aims to explore whether identification with a political ideology works as a basis for affective polarization. Is ideology a stronger source of out-group animosity than partisanship? We focus on how the effects of partisanship might mask the effects of ideological identifications: i.e. whether respondents dislike an out-partisan because they assume that this implies an ideological out-group. Furthermore, we also test whether ideological out-group animosity is motivated by differences in political interest (Krupnikov and Ryan 2021) and policy attitudes (Orr and Huber 2021, Diaz and Lelkes 2022). We run our study in Germany. We choose this case because Germany is likely to be a moderate case both in terms of the meaning left and right hold for people as well as for the strength of partisanship. The stability of Germany’s party system means that party attachments are more stable than in more fluid systems. This means that Germany is neither a most likely, nor a least likely case for ideological attachment.

Countries with access to large bodies of water often aim to protect their maritime transport by employing maritime surveillance systems. However, the number of available sensors (e.g., cameras) is typically small compared to the to-be-monitored targets, and their Field of View (FOV) and range are often limited. This makes improving the situational awareness of maritime transports challenging. To this end, we propose a method that not only distributes multiple sensors but also plans paths for them to observe multiple targets, while minimizing the time needed to achieve situational awareness. In particular, we provide a formulation of this sensor allocation and path planning problem which considers the partial awareness of the targets' state, as well as the unawareness of the targets' trajectories. To solve the problem we present two algorithms: \emph{1)} a greedy algorithm for assigning sensors to targets, and \emph{2)} a distributed multi-agent path planning algorithm based on regret-matching learning. Because a quick convergence is a requirement for algorithms developed for high mobility environments, we employ a forgetting factor to quickly converge to correlated equilibrium solutions. Experimental results show that our combined approach achieves situational awareness more quickly than related work.

Social Democratic parties, once dominant on the Left across Western Europe, have faced varying fortunes in different countries, ranging from near-total collapse to continued popularity. Most accounts of Social Democratic decline have tended to focus on aspects of their policy programme, either in terms of economic ideology or in terms of “newer” topics such as immigration or social liberalism. Alternative accounts emphasize the role of declining social cleavages. However, one aspect has so far been disregarded, namely the declining ability of Social Democratic parties to appeal to segments of the electorate that are less ideologically oriented and focus more on government outcomes – voters we call ‘responsibility voters’. We argue that one reason for the varying success of Social Democratic parties lies in the fact that part of their support has traditionally come from voters who care about competence, rather than about ideological priorities. These voters prioritize the executive competence of the government and focus on the person that will be the head of government. Such competence-oriented individuals care most about political, economic and social outcomes, so whether their country is moving in the right direction or not. These voters then choose the party they believe to be most likely to govern well and bring about positive outcomes: hence, they evaluate parties based on their perceived competence. Arguing that competence is an important component of voters’ decision calculus is far from new; instead, it has long been a central part of such accounts. It is therefore even more surprising that such assessments have not been examined as a determinant of SD party decline. We argue that there has been a systematic downgrading of SD parties’ ability to garner the vote of competence-oriented voters. This is not because SD parties have been uniquely bad at governing or uniquely unlucky in the context in which they have had to do so, though in single cases this may play a role. Instead, we argue that SD parties are nowadays less unique in being included in consideration sets due to their governing status or experience. Having governed (be it at national, regional or local levels) is likely to be a key heuristic for assessing potential competence. All things being equal, parties with more governing experience should be seen as better equipped to govern (well). Voters who care about competence are thus more likely to include a party in their choice set if it has governing experience. However, the competence bonus for parties with governing experience has implications for patterns of voting behaviour and party competition. This is because the weakening of cleavage voting and the fragmentation of party systems has led to an increase in the number of parties that have gained governing experience. In many countries, for instance, Green parties have joined governments at the local, regional and national level. The implication of this is that the choice set of many voters has been increasing. The survey experiment tests a key prediction of this theoretical framework: voters are more likely to consider voting for a party if the party has governing experience. In the experiment, hypothetical parties vary randomly in their government experience, in addition to other attributes. We test whether that governing experience has a positive effect of choice set inclusion. Our second aim is to examine whether the positive reaction to governing experience is universal. We expect some voters to react more strongly to governing experience, namely those voters who care about competence and efficacy, those who hold centrist political stances, and those who do not hold populist stances.

To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather, and seasons. Continually adapting the detector to incorporate these changes is a promising solution, but it can be computationally costly. Our proposed approach is to selectively adapt the detector only when necessary, using new data that does not have the same distribution as the current training data. To this end, we investigate three popular metrics for domain gap evaluation and find that there is a correlation between the domain gap and detection accuracy. Therefore, we apply the domain gap as a criterion to decide when to adapt the detector. Our experiments show that our approach has the potential to improve the efficiency of the detector's operation in real-world scenarios, where environmental conditions change in a cyclical manner, without sacrificing the overall performance of the detector. Our code is publicly available at https://github.com/dadung/DGE-CDA., Comment: Submitted to CVIU

The plant propagation algorithm is a crossoverless population based evolutionary algorithm, defaultly deploying the plus-selection method for survivor selection - combining parents and offspring population and then selecting the best popSize individuals. In this study, we explore eight different survivor selection methods (plus and comma selection, tournament selection with and without replacement, elitist tournament selection, linear ranking selection and (elitist) roulette wheel selection) on 59 continuous benchmark test function instances and compare the results. In the results, the tournament selection methods outperform PPA's default selection method on 83% to 88% of the 59 benchmark functions. Results show that the performance of all selection methods diminish as dimensions of benchmark function instances increase. Analysis indicates that the tournament selection methods employ more exploitation by decreasing population diversity. Future research should establish if this also benefits performance on other benchmark functions and industrial problems.

The dependable guarantee of very high seam quality requirements in laser welding of demanding material combinations and highly stressed structures, such as powertrain components, is becoming increasingly important. The combination of sensor-based inline process monitoring and real-time data analysis using machine learning shows enormous potential for ensuring this. The subject of this paper is the assessment of process monitoring based on acoustic and optical sensor data by means of machine learning during laser welding on rotationally symmetric test specimens. The results show that typical welding defects caused by process variations can be detected with an accuracy of approx. 96 %, almost in real-time. Furthermore, approaches for predictive maintenance of system components and predictive modeling of component properties, supported by numerical simulations, are presented.

Long-term management after stroke has not been standardized in contrast to acute and rehabilitative stroke care. The fragmented outpatient sector of healthcare allows a high degree of flexibility but also variability in the quality of care provided. The commission on long-term stroke care of the German Stroke Society developed a concept on how to provide standardized multiprofessional long-termstroke carewith a focus on patient-centered comprehensive care. We propose a diagnostic work-up in stages, with an initial patient-reported screening, followed by an adapted in-depth assessment of affected domains. This includes internistic domains (management of risk factors) and also genuine neurological domains (spasticity, cognitive deficits etc.) that must be considered. This information is then merged with patient expectations and prioritization to a standardized treatment plan. Special challenges for the implementation of such a comprehensive care system are the intersectoral and intrasectoral communication between healthcare providers, patients and relatives, the need to create a compensation system for providers and the establishment of appropriate quality management services. Digital health applications are helpful tools to provide aspects of diagnostics, treatment and communication in long-term stroke care., Die Schlaganfallnachsorge ist im Gegensatz zur akuten und rehabilitativen Versorgung des Schlaganfalls wenig standardisiert. Der fragmentierte ambulante Sektor erlaubt hierbei ein hohes Maß an Flexibilität, leidet aber folglich an variabler Qualität der Nachsorge. Die Kommission Nachsorge der Deutschen Schlaganfall-Gesellschaft formuliert in diesem Positionspapier ein inhaltliches Konzept, um eine strukturierte Nachsorge mit multiprofessionellem Ansatz zu entwickeln. Diese soll im Sinne einer „Comprehensive-care“-Versorgung und patientenzentriert erfolgen. Dazu schlagen wir ein diagnostisches Stufenkonzept mit Screening und ggf. weitergehender Untersuchung vor, das in Absprache mit den Betroffenen zu einem standardisierten Therapieplan führt, der im Langzeitverlauf entsprechend angepasst werden muss. Inhaltlich sind sowohl internistische Domänen (Management von Risikofaktoren) als auch genuin neurologische Domänen (Spastik, kognitive Defizite etc.) zu berücksichtigen. Besondere Herausforderungen an dieses Konzept sind die sektorenübergreifende (inter- und intrasektorale) Kommunikation zwischen den Akteuren im Gesundheitswesen untereinander sowie mit den Patienten und Angehörigen, die Notwendigkeit zur Schaffung eines Vergütungsmodells für eine solche Nachsorge und letztlich die Etablierung eines entsprechenden Qualitätsmanagements. Digitale Lösungen erachten wir als hilfreiche Werkzeuge für Aspekte der Diagnose, Therapie und Kommunikation in der Schlaganfallnachsorge.

Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we analysed over 3,000 GitHub projects and found that most projects contain at least one outdated code element reference at some point in their history. We submitted GitHub issues to real-world projects containing outdated references detected by our approach, some of which have already led to documentation fixes. As an initiative toward keeping documentation in software repositories up-to-date, we have made our implementation available for developers to scan their GitHub projects for outdated code element references.

We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variables as possible, including variables we might not be aware of, we assume no prior knowledge of sensitive variables: our algorithms use unsupervised fairness only, meaning no information related to the sensitive variables is used for our fairness-improving methods. All images of faces (even generated ones) have been removed to mitigate legal risks.

Recent studies show that many voters are willing to support candidates who advocate illiberal procedures under certain conditions. According to these studies, many citizens trade off the protection of democratic norms in elections against partisan and policy interests. However, these studies do not take into account that people might support politicians who advocate illiberal practices out of conviction, nor how to mitigate these illiberal tendencies. To address this gap, we use a novel survey experiment that examines the relevance of (1) citizens’ populist and technocratic attitudes as well as conceptions of democracy in explaining support for politicians who advocate illiberal procedures, and (2) procedural justice in mitigating these illiberal tendencies. We argue that citizens’ support for politicians who seek to undermine democratic principles is motivated by a polity congruence between voters’ and politicians’ populist and technocratic beliefs as well as conceptions of democracy. In addition, we examine whether individuals’ experience with procedural justice affects the likelihood of supporting politicians who advocate illiberal procedures. In our experimental design, respondents are first exposed to an intervention with varying levels of procedural justice. Respondents are then presented with several vignettes of politicians using different democratic or illiberal procedures to implement their policy proposals in local politics. Our study thus contributes to a better understanding of people's motivations for supporting illiberal procedures and how these tendencies can be mitigated.

One of the main aims of parties and candidates is to obtain support for their policy positions. However, such appeals are often not solely based on dry policy statements. Instead, political elites often tie descriptions of their issue stances to appeals to social groups. These rhetorical strategies could increase support for a policy, as group attitudes shape political opinion formation and behavior. In this paper, we thus argue that the strategic addition of group appeals can help politicians and parties to increase the popularity of their policy positions. In our study, we investigate the effects of a large set of social group appeals on voters' support for policy positions. Drawing on the existing literature, we expect that the effect of social group appeals crucially depends on the respondents' affect for specific social groups. Thus, respondents with a positive attitude towards a group should react more positively when a group appeal is added to a policy statement. In contrast, respondents with a negative view of a certain group should react more negatively. We test our expectations with a survey experiment that allows for an in-depth analysis of the effect of positive and negative appeals to a variety of politically relevant groups. Our results contribute to our understanding of the effect of social group appeals and provide important implications for political communication and representation.

GitHub is the largest host of open source software on the Internet. This large, freely accessible database has attracted the attention of practitioners and researchers alike. But as GitHub's growth continues, it is becoming increasingly hard to navigate the plethora of repositories which span a wide range of domains. Past work has shown that taking the application domain into account is crucial for tasks such as predicting the popularity of a repository and reasoning about project quality. In this work, we build on a previously annotated dataset of 5,000 GitHub repositories to design an automated classifier for categorising repositories by their application domain. The classifier uses state-of-the-art natural language processing techniques and machine learning to learn from multiple data sources and catalogue repositories according to five application domains. We contribute with (1) an automated classifier that can assign popular repositories to each application domain with at least 70% precision, (2) an investigation of the approach's performance on less popular repositories, and (3) a practical application of this approach to answer how the adoption of software engineering practices differs across application domains. Our work aims to help the GitHub community identify repositories of interest and opens promising avenues for future work investigating differences between repositories from different application domains.

We employ a combined experimental-theoretical study of the first- and second-order Raman modes of monoclinic β−Ga2 O3. Gallium oxide has attracted considerable interest due to its deep-UV band gap paired with a high critical field strength, offering promising applications in power electronics. A crucial prerequisite for the future development of Ga2 O3-based devices is a detailed understanding of the lattice vibrations, i.e., phonons, as they govern important material properties such as elasticity, thermal conductivity, temperature-dependence of the band gap, or free-carrier transport. Polarized micro-Raman spectroscopy measurements on the (010) and (¯201) planes of β−Ga2 O3 single crystals enable the determination of the phonon frequencies of all 15 first-order and more than 40 second-order Raman modes. The experimental results are correlated with calculations of the mode frequencies, the phonon dispersion relation, and the phonon density of states using density functional perturbation theory. By applying a group-theoretical analysis, we are able to distinguish between overtones and combinational modes and identify the high-symmetry points in the Brillouin zone that contribute to the observed second-order modes. Based on this information, we demonstrate the simultaneous determination of Raman, IR, and acoustic phonons in β−Ga2 O3 by second-order Raman spectroscopy.

Ga$_2$O$_3$ and its polymorphs are attracting increasing attention. The rich structural space of polymorphic oxide systems such as Ga$_2$O$_3$ offers potential for electronic structure engineering, which is of particular interest for a range of applications, such as power electronics. $\gamma$-Ga$_2$O$_3$ presents a particular challenge across synthesis, characterisation, and theory due to its inherent disorder and resulting complex structure -- electronic structure relationship. Here, density functional theory is used in combination with a machine learning approach to screen nearly one million potential structures, thereby developing a robust atomistic model of the $\gamma$-phase. Theoretical results are compared with surface and bulk sensitive soft and hard X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, spectroscopic ellipsometry, and photoluminescence excitation spectroscopy experiments representative of the occupied and unoccupied states of $\gamma$-Ga$_2$O$_3$. The first onset of strong absorption at room temperature is found at 5.1 eV from spectroscopic ellipsometry, which agrees well with the excitation maximum at 5.17 eV obtained by PLE spectroscopy, where the latter shifts to 5.33 eV at 5 K. This work presents a leap forward in the treatment of complex, disordered oxides and is a crucial step towards exploring how their electronic structure can be understood in terms of local coordination and overall structure.

Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural languages, and because surprisal is constructed with the probability of an event occuring, it is therefore possible to determine the surprisal associated with English sentences. The issues and pull requests of software repository issue trackers give insight into the development process and likely contain the surprising events of this process. Objective. Prior works have identified that unusual events in software repositories are of interest to developers, and use simple code metrics-based methods for detecting them. In this study we will propose a new method for unusual event detection in software repositories using surprisal. With the ability to find surprising issues and pull requests, we intend to further analyse them to determine if they actually hold importance in a repository, or if they pose a significant challenge to address. If it is possible to find bad surprises early, or before they cause additional troubles, it is plausible that effort, cost and time will be saved as a result. Method. After extracting the issues and pull requests from 5000 of the most popular software repositories on GitHub, we will train a language model to represent these issues. We will measure their perceived importance in the repository, measure their resolution difficulty using several analogues, measure the surprisal of each, and finally generate inferential statistics to describe any correlations., 8 pages, 1 figure. Submitted to 2022 International Conference on Mining Software Repositories Registered Reports track

The aim of visualization is to support people in dealing with large and complex information structures, to make these structures more comprehensible, facilitate exploration, and enable knowledge discovery. However, users often have problems reading and interpreting data from visualizations, in particular when they experience them for the first time. A lack of visualization literacy, i.e., knowledge in terms of domain, data, visual encoding, interaction, and also analytical methods can be observed. To support users in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other domains. However, it has not received much attention from the visualization community so far. This chapter aims to fill this gap by defining the concept and systematically laying out the design space of onboarding in the context of visualization as a descriptive design space. On this basis, we present a survey of approaches from the academic community as well as from commercial products, especially surveying educational theories that inform the onboarding strategies. Additionally, we derived design considerations based on previous publications and present some guidelines for the design of visualization onboarding concepts.

Vehicular edge computing has recently been proposed to support computation-intensive applications in Intelligent Transportation Systems (ITS) such as self-driving cars and augmented reality. Despite progress in this area, significant challenges remain to efficiently allocate limited computation resources to a range of time-critical ITS tasks. To this end, the current paper develops a new task assignment scheme for vehicles in a highway. Because of the high speed of vehicles and the limited communication range of road side units (RSUs), the computation tasks of participating vehicles are to be dynamically migrated across multiple servers. We formulate a binary nonlinear programming (BNLP) problem of assigning computation tasks from vehicles to RSUs and a macrocell base station. To deal with the potentially large size of the formulated optimization problem, we develop a distributed multi-agent regret-matching learning algorithm. Based on the regret minimization principle, the proposed algorithm employs a forgetting method that allows the learning process to quickly adapt to and effectively handle the high mobility feature of vehicle networks. We theoretically prove that it converges to the correlated equilibrium solutions of the considered BNLP problem. Simulation results with practical parameter settings show that the proposed algorithm offers the lowest total delay and cost of processing tasks, as well as utility fairness among agents. Importantly, our algorithm converges much faster than existing methods as the problem size grows, demonstrating its clear advantage in large-scale vehicular networks., 10 pages, 12 figures, and 1 table

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics. In the Large project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

We employ a combined experimental-theoretical study of the first- and second-order Raman modes of monoclinic $\beta$-Ga$_{2}$O$_{3}$. The investigated materials is of particular interest due to its deep-UV bandgap paired with a high critical field strength, offering promising applications in power-electronics. A crucial prerequisite for the future development of Ga$_{2}$O$_{3}$-based devices is a detailed understanding of the lattice dynamics as they are important for the elasticity (through acoustic phonons), thermal conductivity (through the heat transferred by phonons), the temperature-dependence of the bandgap (impacted by electron-phonon coupling) or the free carrier transport (via phonon scattering). Polarized micro-Raman spectroscopy measurements on the (010) and ($\bar{2}01$) planes enable the determination of the phonon frequencies of all 15 first-order and more than 40 second-order Raman modes. The experimental results are correlated with calculations of the mode frequencies, phonon dispersion relation and phonon density of states using density functional perturbation theory (DFPT). By applying a group-theoretical analysis, we are able to distinguish between overtones and combinational modes and identify the high symmetry points in the Brillouin zone which contribute to the observed second order modes. Based on these information, we demonstrate the simultaneous determination of Raman-, IR-, and acoustic phonons in $\beta$-Ga$_{2}$O$_{3}$ by second-order Raman spectroscopy.

Software developers are increasingly dependent on question and answer portals and blogs for coding solutions. While such interfaces provide useful information, there are concerns that code hosted here is often incorrect, insecure or incomplete. Previous work indeed detected a range of faults in code provided on Stack Overflow through the use of static analysis. Static analysis may go a far way towards quickly establishing the health of software code available online. In addition, mechanisms that enable rapid automated program improvement may then enhance such code. Accordingly, we present this proof of concept. We use the PMD static analysis tool to detect performance faults for a sample of Stack Overflow Java code snippets, before performing mutations on these snippets using GIN. We then re-analyse the performance faults in these snippets after the GIN mutations. GIN's RandomSampler was used to perform 17,986 unique line and statement patches on 3,034 snippets where PMD violations were removed from 770 patched versions. Our outcomes indicate that static analysis techniques may be combined with automated program improvement methods to enhance publicly available code with very little resource requirements. We discuss our planned research agenda in this regard., Comment: 4 pages, 4 tables, 1 listing

Unintentionally doped (001)-oriented orthorhombic κ-Ga(2)O(3) epitaxial films on c-plane sapphire substrates are characterized by the presence of ≈ 10 nm wide columnar rotational domains that can severely inhibit in-plane electronic conduction. Comparing the in- and out-of-plane resistance on well-defined sample geometries, it is experimentally proved that the in-plane resistivity is at least ten times higher than the out-of-plane one. The introduction of silane during metal-organic vapor phase epitaxial growth not only allows for n-type Si extrinsic doping, but also results in the increase of more than one order of magnitude in the domain size (up to ≈ 300 nm) and mobility (highest µ ≈ 10 cm(2)V(-1)s(-1), with corresponding lowest ρ ≈ 0.2 Ωcm). To qualitatively compare the mean domain dimension in κ-Ga(2)O(3) epitaxial films, non-destructive experimental procedures are provided based on X-ray diffraction and Raman spectroscopy. The results of this study pave the way to significantly improved in-plane conduction in κ-Ga(2)O(3) and its possible breakthrough in new generation electronics. The set of cross-linked experimental techniques and corresponding interpretation here proposed can apply to a wide range of material systems that suffer/benefit from domain-related functional properties.

Unintentionally doped (001)-oriented orthorhombic κ-Ga2O3 epitaxial films on c-plane sapphire substrates are characterized by the presence of ≈ 10 nm wide columnar rotational domains that can severely inhibit in-plane electronic conduction. Comparing the in- and out-of-plane resistance on well-defined sample geometries, it is experimentally proved that the in-plane resistivity is at least ten times higher than the out-of-plane one. The introduction of silane during metal-organic vapor phase epitaxial growth not only allows for n-type Si extrinsic doping, but also results in the increase of more than one order of magnitude in the domain size (up to ≈ 300 nm) and mobility (highest µ ≈ 10 cm2V−1s−1, with corresponding lowest ρ ≈ 0.2 Ωcm). To qualitatively compare the mean domain dimension in κ-Ga2O3 epitaxial films, non-destructive experimental procedures are provided based on X-ray diffraction and Raman spectroscopy. The results of this study pave the way to significantly improved in-plane conduction in κ-Ga2O3 and its possible breakthrough in new generation electronics. The set of cross-linked experimental techniques and corresponding interpretation here proposed can apply to a wide range of material systems that suffer/benefit from domain-related functional properties.

Cellular additively manufactured metallic structures for load-bearing scaffolds in the context of bone tissue engineering (BTE) have emerged as promising candidates. Due to many advantages in terms of morphology, stiffness, strength and permeability compared to conventional truss structures, lattices based on triply periodic minimal surfaces (TPMS) have recently attracted increasing interest for this purpose. In addition, the finite element method (FEM) has been proven to be suitable for accurately predicting the deformation behavior as well as the mechanical properties of geometric structures after appropriate parameter validation based on experimental data. Numerous publications have examined many individual aspects, but conceptual design procedures that consider at least the essential requirements for cortical and trabecular bone simultaneously are still rare. Therefore, this paper presents a numerical approach to first determine the actual admissible design spaces for a choice of TPMS based lattices with respect to key parameters and then weight them with respect to further benefit parameters. The admissible design spaces are limited by pore size, strut size and volume fraction, and the subsequent weighting is based on Young's modulus, cell size and surface area. Additively manufactured beta-Ti-42Nb with a strain stiffness of 60.5GPa is assumed as material. In total, the procedure considers twelve lattice types, consisting of six different TPMS, each as network solid and as sheet solid. The method is used for concrete prediction of suitable TPMS based lattices for cortical bone and trabecular bone. For cortical bone a lattice based on the Schwarz Primitive sheet solid with 67.572mm pore size, 0.5445 volume fraction and 18.758GPa Young's modulus shows to be the best choice. For trabecular bone a lattice based on the Schoen Gyroid network solid with 401.39mm pore size, 0.3 volume fraction and 4.6835GPa Young's modulus is the identified lattice. Finally, a model for a long bone scaffold is generated from these two lattices using functional grading methods in terms of volume fraction, cell size and TPMS type. In particular, the presented procedure allows an efficient estimation for a likely suitable biometric TPMS-based scaffolds. In addition to medical applications, however, the method can also be transferred to numerous other applications in mechanical, civil and electrical engineering.

We employ a combined experimental-theoretical study of the first- and second-order Raman modes of monoclinic $��$-Ga$_{2}$O$_{3}$. The investigated materials is of particular interest due to its deep-UV bandgap paired with a high critical field strength, offering promising applications in power-electronics. A crucial prerequisite for the future development of Ga$_{2}$O$_{3}$-based devices is a detailed understanding of the lattice dynamics as they are important for the elasticity (through acoustic phonons), thermal conductivity (through the heat transferred by phonons), the temperature-dependence of the bandgap (impacted by electron-phonon coupling) or the free carrier transport (via phonon scattering). Polarized micro-Raman spectroscopy measurements on the (010) and ($\bar{2}01$) planes enable the determination of the phonon frequencies of all 15 first-order and more than 40 second-order Raman modes. The experimental results are correlated with calculations of the mode frequencies, phonon dispersion relation and phonon density of states using density functional perturbation theory (DFPT). By applying a group-theoretical analysis, we are able to distinguish between overtones and combinational modes and identify the high symmetry points in the Brillouin zone which contribute to the observed second order modes. Based on these information, we demonstrate the simultaneous determination of Raman-, IR-, and acoustic phonons in $��$-Ga$_{2}$O$_{3}$ by second-order Raman spectroscopy.

Online participant recruitment platforms such as Prolific have been gaining popularity in research, as they enable researchers to easily access large pools of participants. However, participant quality can be an issue; participants may give incorrect information to gain access to more studies, adding unwanted noise to results. This paper details our experience recruiting participants from Prolific for a user study requiring programming skills in Node.js, with the aim of helping other researchers conduct similar studies. We explore a method of recruiting programmer participants using prescreening validation, attention checks and a series of programming knowledge questions. We received 680 responses, and determined that 55 met the criteria to be invited to our user study. We ultimately conducted user study sessions via video calls with 10 participants. We conclude this paper with a series of recommendations for researchers., Comment: To appear at RoPES 2022

There is an urgent need for transformational change in agriculture to address current and future issues caused by climate change, biodiversity loss and socio-ecological disruption. But change is slow to come and is hindered by a lack of transdisciplinary evidence on potential approaches which take a systems approach. The research described here was co-developed with the Pasture Fed Livestock Association in the UK to objectively evidence their practices. These include producing pasture-based meat from livestock fed on pasture and pasture-based forages alone. This approach sits alongside wider aims of fitting their practices with the ecological conditions on each individual farm to facilitate optimal production and working collaboratively through a forum for sharing knowledge. The research provides strong indications that the PFLA approach to livestock production is resilient and viable, as well as contributing to wider public goods delivery, despite variability within and between farms. It also reveals that learning and adaption of practice (through farmer experience) is central to farming using agro-ecological approaches. This fluidity of practice presents challenges for reductionist approaches to "measuring" agricultural innovations.

Education is an important component of every healthcare system. Patients need to be educated about their planned procedures; healthcare professionals need to be trained in their respective profession. Both patient education and the training of healthcare professionals are often completed in person, which requires resources and is bound to certain times and places. Virtual educational environments can potentially save human and monetary resources, increase learner engagement, and enable users to learn according to their own schedules. This design study describes proofs of concept for two augmented reality-enabled (AR) educational tools, utilizing a Microsoft HoloLens head-mounted display. In the first use case, we demonstrate an AR application which could be used to educate cancer patients about their radiotherapy treatment and potentially reduce patient anxiety. The second use case demonstrates an AR training environment, which could complement the practical training of undergraduate radiography students. Two prototypes—VIPER, for patient education, and ARTUR for the training of radiography students—were developed and tested for viability and usability, both based on individual user tests. Both patient and student education were evaluated as viable and usable additions to conventional educational methods, despite being limited in terms of accessibility, usability, and fidelity. Suitable hardware is becoming more accessible and capable, and higher-fidelity holograms, better utilization of real-world objects, and more intuitive input methods could increase user immersion and acceptance of the technology.

Does a large influx of asylum seekers in the local community lead to a backlash in public opinion towards foreign populations? We assess the effects of asylum seeker presence using original survey and macro-level municipality data from Austria, exploiting exogenous elements of the placement of asylum seekers on the municipality level. Methodologically, we draw on entropy balancing for causal identification. Our findings are threefold. First, respondents in municipalities receiving asylum seekers report substantially higher exposure on average, but largely without the stronger contact that would allow for meaningful interaction. Second, hostility towards asylum seekers on average increased in areas that housed them. Third, this backlash spilt over: general attitudes towards Muslims and immigrants are less favourable in contexts with local asylum seeker presence, while vote intention for the main anti-immigration party is higher. Our findings go beyond existing work by examining contact directly as a mechanism, by showing a backlash effect in the medium term, and by focusing on a broad set of attitudinal and behavioural measures. Our results point to a need to design policy interventions that minimise citizen backlash against rapid migration inflows., European Journal of Political Research, 61 (1), ISSN:0304-4130, ISSN:1475-6765

Most literature surrounding optimal bidding strategies for aggregators in European day-ahead market (DAM) considers only hourly orders. While other order types (e.g., block orders) may better represent the temporal characteristics of certain sources of flexibility (e.g., behind-the-meter flexibility), the increased combinations from these orders make it hard to develop a tractable optimization formulation. Thus, our aim in this paper is to develop a tractable optimal offering strategy for flexibility aggregators in the European DAM (a.k.a. Elspot) considering these orders. Towards this, we employ a price-based mechanism of procuring flexibility and place hourly and regular block orders in the market. We develop two mixed-integer bi-linear programs: 1) a brute force formulation for validation and 2) a novel formulation based on logical constraints. To evaluate the performance of these formulations, we proposed a generic flexibility model for an aggregated cluster of prosumers that considers the prosumers' responsiveness, inter-temporal dependencies, and seasonal and diurnal variations. The simulation results show that the proposed model significantly outperforms the brute force model in terms of computation speed. Also, we observed that using block orders has potential for profitability of an aggregator., Comment: 6 pages and 4 figures. Accepted for publishing by IEEE PES ISGT Europe 2022 Conference

Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences. Bilevel optimisation has been successfully applied to truss optimisation to consider topology and sizing in upper and lower levels, respectively. We introduce exact enumeration to rigorously analyse the topology search space and remove randomness for small problems. We also propose novelty-driven binary particle swarm optimisation for bigger problems to discover new designs at the upper level by maximising novelty. For the lower level, we employ a reliable evolutionary optimiser to tackle the layout configuration aspect of the problem. We consider truss optimisation problem instances where designers need to select the size of bars from a discrete set with respect to practice code constraints. Our experimental investigations show that our approach outperforms the current state-of-the-art methods and it obtains multiple high-quality solutions.

In image generation, where diversity is critical, people can express their preferences by choosing among several proposals. Thus, the image generation system can be refined to satisfy the user's needs. In this paper, we focus on multi-objective optimization as a tool for proposing diverse solutions. Multiobjective optimization is the area of research that deals with optimizing several objective functions simultaneously. In particular, it provides numerous solutions corresponding to trade-offs between different objective functions. The goal is to have enough diversity and quality to satisfy the user. However, in computer vision, the choice of objective functions is part of the problem: typically, we have several criteria, and their mixture approximates what we need. We propose a criterion for quantifying the performance in multi-objective optimization based on cross-validation: when optimizing n−1 of the n criteria, the Pareto front should include at least one good solution for the removed n th criterion. After providing evidence for the validity and usefulness of the proposed criterion, we show that the diversity provided by multiobjective optimization is helpful in diverse image generation, namely super-resolution and inspirational generation.

We tackle the Thief Orienteering Problem (ThOP), an academic multi-component problem that combines two classical combinatorial problems, namely the Knapsack Problem and the Orienteering Problem. In the ThOP, a thief has a time limit to steal items that distributed in a given set of cities. While traveling, the thief collects items by storing them in their knapsack, which in turn reduces the travel speed. The thief has as the objective to maximize the total profit of the stolen items. In this article, we present an approach that combines swarm-intelligence with a randomized packing heuristic. Our solution approach outperforms existing works on almost all the 432 benchmarking instances, with significant improvements.

Gallium oxide (Ga2O3) is an ultra-wide bandgap material, which has recently attracted widespread attention for holding promising applications in power electronics and solar blind UV photodetectors, outclassing GaN or SiC in terms of a larger bandgap and higher breakdown voltages. The orthorhombic �� phase (also referred to as ��) has sparked particular interest for offering higher symmetry than ��, while featuring ferroelectric behavior paired with a large predicted spontaneous polarization, paving the way to fabricating high-quality two-dimensional electron gases for application in heterostructure field effect transistors. The presently available �� phase samples are characterized by a domain structure, in which orthorhombic domains are rotated 120�� against each other within the c-plane forming a pseudo-hexagonal structure, which has previously often been ascribed to ��-Ga2O3 and incorrectly been viewed as this polymorph's true crystal structure. A detailed investigation into the phonon modes of orthorhombic ��-Ga2O3 provides insights into fundamental material properties such as crystal structure and orientation as well as the vibrational symmetries of Raman active modes. We investigate the Raman active phonon modes of an MBE-grown orthorhombic ��-Ga2O3 thin film featuring the domain structure deposited on (0001)-Al2O3 by experiment and theory: Polarized micro-Raman spectroscopy measurements in conjunction with density functional perturbation theory (DFPT) calculations enable the identification of both the frequencies and vibrational symmetries of the Raman active phonons. Presenting comprehensive Raman spectra of the orthorhombic �� phase, the experimental frequencies of more than 90 Raman modes are determined and correlated with the 117 modes predicted by the calculations. Angular-resolved Raman measurements are utilized to provide an experimental verification of phonon mode symmetries. We present an analytical tool to deal with the domain structure and its effect on the obtained Raman spectra.

This report describes the Data Management Plan (DMP) for the CarE-Service Project. The report aims at establishing the policy framework for information management and confidentiality within the CarE-Service consortium, the members of the Consumers’ and Stakeholders Committees, as well as external stakeholders. The policy framework for data management includes principles ensuring the effective management and confidentiality of data, information and records throughout the related authorities and responsibilities. The main purpose of the data/information management policy is to protect both electronic and paper-version of data/information from any unauthorized use and access with a clear role and responsibilities of those who manage the data/information, while ensuring the greatest possible access of data/information, consistent with legislation and relevant EU/consortium policies. Detailed information will be provided on the procedures that will be implemented for data collection, storage, protection, retention and destruction and confirmation that they comply with national (eg. Bundesdatenschutzgesetz (BDSG) for Germany) and EU legislation (Regulation (EU) 2016/679 (General Data Protection Regulation)). The DMP describes the data management life cycle for the data to be collected, processed and/or generated by CarE-Service Horizon 2020 project. As part of making research data findable, accessible, interoperable and re-usable (FAIR), the DMP includes information on: the handling of research data during & after the end of the project what data will be collected, processed and/or generated which methodology & standards will be applied whether data will be shared/made open access and how data will be curated & preserved (including after the end of the project).

A core operator of evolutionary algorithms (EAs) is the mutation. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform mutation rates. Following up on this area of work, we propose a new mutation operator and analyze its performance on the (1 + 1) Evolutionary Algorithm (EA). Our analyses show that this mutation operator competes with pre-existing ones, when used by the (1 + 1) EA on classes of problems for which results on the other mutation operators are available. We show that the (1 + 1) EA using our mutation operator finds a (1/3)-approximation ratio on any non-negative submodular function in polynomial time. We also consider the problem of maximizing a symmetric submodular function under a single matroid constraint and show that the (1 + 1) EA using our operator finds a (1/3)-approximation within polynomial time. This performance matches that of combinatorial local search algorithms specifically designed to solve these problems and outperforms them with constant probability. Finally, we evaluate the performance of the (1 + 1) EA using our operator experimentally by considering two applications: (a) the maximum directed cut problem on real-world graphs of different origins, with up to 6.6 million vertices and 56 million edges and (b) the symmetric mutual information problem using a four month period air pollution data set. In comparison with uniform mutation and a recently proposed dynamic scheme, our operator comes out on top on these instances.

Resource allocation to different plant tissues is likely to be affected by high investment into fruit production during mast years. However, there is a large knowledge gap concerning species-specific differences in resource dynamics. We investigated the influence of mast years on stem growth, leaf production, and leaf carbon (C), nitrogen (N), and phosphorus (P) concentrations and contents in Fagus sylvatica, Quercus petraea, and Q. robur at continental and climate region scales using long-term data from the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) and similar datasets. We discussed the results in the light of opposing resource dynamics hypotheses: (i) resource accumulation before mast years and exhaustion after mast years (resource storage hypothesis), (ii) shifting resources from vegetative to generative compartments (resource switching hypothesis), and (iii) investing resources concurrently in both vegetative and generative compartments (resource matching hypothesis). Linear mixed-effects modelling (LMM) showed that both stem growth and leaf production were negatively influenced by weather conditions which simultaneously lead to high fruit production. Thus, the impact of generative on vegetative growth is intermixed with effects of environmental factors. Superposed epoch analyses and LMM showed that for mast behaviour in F. sylvatica, there are indicators supporting the resource storage and the resource switching hypotheses. Before mast years, resources were accumulated, while during mast years resources switched from vegetative to generative tissues with reduced stem and leaf growth. For the Quercus species, stem growth was reduced after mast years, which supports the resource storage hypothesis. LMM showed that leaf C concentrations did not change with increasing fruit production in neither species. Leaf N and P concentrations increased in F. sylvatica, but not in Quercus species. Leaf N and P contents decreased with increasing fruit production in all species, as did leaf C content in F. sylvatica. Overall, our findings suggest different resource dynamics strategies in F. sylvatica and Quercus species, which might lead to differences in their adaptive capacity to a changing climate.