This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.
Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.
This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.
Dynamical decoupling represents an active approach towards the protection of quantum memories and quantum gates. Because dynamical decoupling operations can interfere with a system's own time evolution, the protection of quantum gates is more challenging than that of quantum states. In this work, we put forward a simple but general approach towards the realization of higher-order protection of quantum gates and further execute the first cloud-based demonstration of dynamical-decoupling-protected quantum gates at the first order and the second order. The central idea of our approach is to engineer (hence regain the control of) the gate Hamiltonian in coordination with higher-order dynamical decoupling sequences originally proposed for the protection of quantum memories. The physical demonstration on an IBM quantum processor indicates the effectiveness and potential of our approach on noisy intermediate scale quantum computers.
The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.
Software companies have widely used online A/B testing to evaluate the impact of a new technology by offering it to groups of users and comparing it against the unmodified product. However, running online A/B testing needs not only efforts in design, implementation, and stakeholders' approval to be served in production but also several weeks to collect the data in iterations. To address these issues, a recently emerging topic, called "Offline A/B Testing", is getting increasing attention, intending to conduct the offline evaluation of new technologies by estimating historical logged data. Although this approach is promising due to lower implementation effort, faster turnaround time, and no potential user harm, for it to be effectively prioritized as requirements in practice, several limitations need to be addressed, including its discrepancy with online A/B test results, and lack of systematic updates on varying data and parameters. In response, in this vision paper, I introduce AutoOffAB, an idea to automatically run variants of offline A/B testing against recent logging and update the offline evaluation results, which are used to make decisions on requirements more reliably and systematically.
The Caral civilization developed on the north-central coast of Peru and had an occupation period between 2870 and 1970 years BC. The first studies carried out in the field of archaeoastronomy showed evidence of possible astronomical orientations in some buildings of its capital city, the Ciudad Sagrada de Caral. However, methodological issues cast doubt on these conclusions. A recent study carried out a more general statistical analysis, which covered a total of 55 architectural structures distributed in ten urban settlements that were part of this civilization, thus managing to identify topographic and astronomical orientation patterns. Based on this evidence, we propose to carry out a new study focused on the capital city, with the objective of analyzing the orientation pattern of the city, placing emphasis on the analysis of the most important religious and administrative structures in order to determine their functionality and their possible links with relevant astronomical objects. The study will include field work to measure the various structures and the subsequent statistical analysis of the data, using declination histograms, density functions and probability tests.
Samuel M. H. Luk, Hadrien Vergnet, Ombline Lafont
et al.
We demonstrate theoretically and experimentally that a specifically designed microcavity driven in the optical parametric oscillation regime exhibits lighthouse-like emission, i.e., an emission focused around a single direction. Remarkably, the emission direction of this micro-lighthouse is continuously controlled by the linear polarization of the incident laser, and angular beam steering over \unit{360}{\degree} is demonstrated. Theoretically, this unprecedented effect arises from the interplay between the nonlinear optical response of microcavity exciton-polaritons, the difference in the subcavities forming the microcavity, and the rotational invariance of the device.
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research. We consider current assessment techniques for each quality issue and proposed mechanisms to address these issues, where available. Our taxonomy classifies data quality issues into three broad areas: first, characteristics of data that mean they are not fit for modeling; second, data set characteristics that lead to concerns about the suitability of applying a given model to another data set; and third, factors that prevent or limit data accessibility and trust. We identify this latter area as of particular need in terms of further research.
Gias Uddin, Yann-Gael Gueheneuc, Foutse Khomh
et al.
Sentiment analysis in software engineering (SE) has shown promise to analyze and support diverse development activities. We report the results of an empirical study that we conducted to determine the feasibility of developing an ensemble engine by combining the polarity labels of stand-alone SE-specific sentiment detectors. Our study has two phases. In the first phase, we pick five SE-specific sentiment detection tools from two recently published papers by Lin et al. [31, 32], who first reported negative results with standalone sentiment detectors and then proposed an improved SE-specific sentiment detector, POME [31]. We report the study results on 17,581 units (sentences/documents) coming from six currently available sentiment benchmarks for SE. We find that the existing tools can be complementary to each other in 85-95% of the cases, i.e., one is wrong, but another is right. However, a majority voting-based ensemble of those tools fails to improve the accuracy of sentiment detection. We develop Sentisead, a supervised tool by combining the polarity labels and bag of words as features. Sentisead improves the performance (F1-score) of the individual tools by 4% (over Senti4SD [5]) - 100% (over POME [31]). In a second phase, we compare and improve Sentisead infrastructure using Pre-trained Transformer Models (PTMs). We find that a Sentisead infrastructure with RoBERTa as the ensemble of the five stand-alone rule-based and shallow learning SE-specific tools from Lin et al. [31, 32] offers the best F1-score of 0.805 across the six datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.
The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.
With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue -- how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto non-dominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly available. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.
We put forward a scheme based on reservoir engineering to protect quantum coherence from leaking to bath, in which we intensely disturb the Lorentzian bath by N harmonic oscillators. We show that the intense disturbance changes the spectrum of the bath and reduces the qubit-bath interaction. Furthermore, we give the exact time evolution with the Lorentzian spectrum by a master equation, and calculate the concurrence and survival probability of the qubits to demonstrate the effect of the intense bath disturbance on the protection of coherence. Meanwhile, we reveal the dynamic effects of counter-rotating interaction on the qubits as compared to the results of the rotating wave approximation.
Zahra Shakeri Hossein Abad, Oliver Karras, Parisa Ghazi
et al.
Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.
Stefan Wagner, Daniel Méndez Fernández, Michael Felderer
et al.
Requirements engineering (RE) is considerably different in agile development than in more traditional development processes. Yet, there is little empirical knowledge on the state of the practice and contemporary problems in agile RE. As part of a bigger survey initiative (Naming the Pain in Requirements Engineering), we build an empirical basis on such aspects of agile RE. Based on the responses of representatives from 92 different organisations, we found that agile RE concentrates on free-text documentation of requirements elicited with a variety of techniques. Often, traces between requirements and code are explicitly managed and also software testing and RE are aligned. Furthermore, continuous improvement of RE is performed due to intrinsic motivation. Important experienced problems include unclear requirements and communication flaws. Overall, we found that most organisations conduct RE in a way we would expect and that agile RE is in several aspects not so different from RE in other development processes.
The article investigates influence relation between two sets of agents in a social network. It proposes a logical system that captures propositional properties of this relation valid in all threshold models of social networks with the same topological structure. The logical system consists of Armstrong axioms for functional dependence and an additional Lighthouse axiom. The main results are soundness, completeness, and decidability theorems for this logical system.
Real-Time communication are important in all those distributed applications where timing constraints on data proccessing and task executation play a fundamental role. Standards-base software engineering does not yet specify how real-time properties should be integrated into a publish/subscribe middleware. This article describes an approach for integration of priority quality of service in a publish/subscribe middleware. The approach simply leverages the operating system functionalities to provide a framework where specific communication channels can be prioritized at run-time. The quality of service is implemented in YARP (Yet Another Robot Platform) framework and the primarily results of performance tests are presented.