In this paper, we study CTP maps, that is, marked rational maps with constant Thurston pullback mapping. We prove that all the regular or mixing CTP polynomials satisfy McMullen's condition. Additionally, we construct a new class of examples of CTP maps.
Hayley C. Nofi, Graeme E. Addison, Charles L. Bennett
et al.
Studies of cosmic microwave background (CMB) are often limited by foreground contamination. Foreground cleaning is performed either in harmonic or pixel space after data cuts have excluded sky areas of strong contamination. We present a nearly full-sky CMB temperature map with only 1% of pixels masked. To derive this map, we make use of six full-sky template maps at foreground-dominated frequencies from different experiments smoothed to $1^\circ$ and rely on the combination of these weighted maps to trace the morphology of foreground contamination. We do not impose any spectral index constraints, but only fit for template amplitudes at each target frequency. We clean WMAP and Planck maps at a set of target frequencies and conduct quality tests at the level of the maps, pixel histograms and power spectra to select four CMB maps that are cleaned with negligible foreground contamination and only 1% masked pixels and no inpainting. We recommend use of these cleaned CMB maps for low multipole ($\ell < 30$) studies.
Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes
et al.
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic mapping study was conducted involving the selection and analysis of 26 primary studies published in the field. Each study was categorized according to Salas' four key areas: Training Needs Analysis, Antecedent Training Conditions, Training Methods and Instructional Strategies, and Post-Training Conditions. Results: The findings show a predominance of studies focusing on Training Methods and Instructional Strategies. Significant gaps were identified in other areas, particularly regarding Job/Task Analysis and Simulation-based Training and Games. Most studies were experience reports, lacking methodological rigor and longitudinal assessment. Conclusions: The study offers a structured overview of how corporate training is approached in software engineering, revealing underexplored areas and proposing directions for future research. It contributes to both academic and practical communities by highlighting challenges, methodological trends, and opportunities for designing more effective training programs in industry.
We give an expansion in $1/N$ and $β$ of the cumulants of power sums of the particles of the $β$-ensemble. This new expansion is obtained using the tridiagonal model of Dumitriu and Edelman. The coefficients of the expansion are expressed in terms of suitably labelled maps introduced by Bouttier, Fusy, and Guitter. Our expansion is of a different nature than the one obtained by LaCroix in is study of the $b$-conjecture of Goulden and Jackson, and involves only orientable maps. We are able to relate bijectively the first two orders of our expansion to the one of LaCroix using a novel many-to-one mapping that relates suitably labelled planar maps with two minima and maps on the projective plane.
Software architecture is the foundation of a system's ability to achieve various quality attributes, including software performance. However, there lacks comprehensive and in-depth understanding of why and how software architecture and performance analysis are integrated to guide related future research. To fill this gap, this paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focused on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area. These questions addressed: a systematic mapping of related studies based on the high-level research purposes and specific focuses (RQ1), the software development activities these studies intended to facilitate (RQ2), the typical study templates of different research purposes (RQ3), the available tools and instruments for automating the analysis (RQ4), and the evaluation methodology employed in the studies (RQ5). Through these research questions, we also identified critical research gaps and future directions, including: 1) the lack of available tools and benchmark datasets to support replication, cross-validation and comparison of studies; 2) the need for architecture and performance analysis techniques that handle the challenges in emerging software domains; 3) the lack of consideration of practical factors that impact the adoption of the architecture and performance analysis approaches; and finally 4) the need for the adoption of modern ML/AI techniques to efficiently integrate architecture and performance analysis.
Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos
et al.
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for MLOps, there is simultaneously a lack of consolidated architecture knowledge that can inform the architecture design. Objective. Our primary objective is to provide a comprehensive overview of (i) how MLOps architectures are defined across the literature and (ii) which tools are mentioned to support the implementation of each architecture component. Method. We apply the Systematic Mapping Study method and select 43 primary studies via automatic, manual, and snowballing-based search and selection procedures. Subsequently, we use card sorting to synthesize the results. Results. We contribute (i) a categorization of 35 MLOps architecture components, (ii) a description of several MLOps architecture variants, and (iii) a systematic map between the identified components and the existing MLOps tools. Conclusion. This study provides an overview of the state of the art in MLOps from an architectural perspective. Researchers and practitioners can use our findings to inform the architecture design of their MLOps systems.
High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. The traditional method of creating offline HD maps involves labor-intensive manual annotation processes, which are not only costly but also insufficient for timely updates. Recent studies have proposed an alternative approach that generates local maps using online sensor observations. However, this approach is limited by the sensor's perception range and its susceptibility to occlusions. In this study, we propose Neural Map Prior (NMP), a neural representation of global maps. This representation automatically updates itself and improves the performance of local map inference. Specifically, we utilize two approaches to achieve this. Firstly, to integrate a strong map prior into local map inference, we apply cross-attention, a mechanism that dynamically identifies correlations between current and prior features. Secondly, to update the global neural map prior, we utilize a learning-based fusion module that guides the network in fusing features from previous traversals. Our experimental results, based on the nuScenes dataset, demonstrate that our framework is highly compatible with various map segmentation and detection architectures. It significantly improves map prediction performance, even in challenging weather conditions and situations with a longer perception range. To the best of our knowledge, this is the first learning-based system for creating a global map prior.
The ECCE detector has been recommended as the selected reference detector for the future Electron-Ion Collider (EIC). A series of simulation studies have been carried out to validate the physics feasibility of the ECCE detector. In this paper, detailed studies of heavy flavor hadron and jet reconstruction and physics projections with the ECCE detector performance and different magnet options will be presented. The ECCE detector has enabled precise EIC heavy flavor hadron and jet measurements with a broad kinematic coverage. These proposed heavy flavor measurements will help systematically study the hadronization process in vacuum and nuclear medium especially in the underexplored kinematic region.
Jennifer Mankoff, Devva Kasnitz, Disability Studies
et al.
This response to the RFI considers the potential for biometrics to help or harm disabled people2. Biometrics are already integrated into many aspects of daily life, from airport travel to mobile phone use. Yet many of these systems are not accessible to people who experience different kinds of disability exclusion . Different personal characteristics may impact any or all of the physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait, voice) characteristics listed in the RFI as examples of biometric signals.
It is a well-known result due to E. Størmer that every positive qubit map is decomposable into a sum of a completely positive map and a completely copositive map. Here, we generalize this result to tensor squares of qubit maps. Specifically, we show that any positive tensor product of a qubit map with itself is decomposable. This solves a recent conjecture by S. Fillipov and K. Magadov. We contrast this result with examples of non-decomposable positive maps arising as the tensor product of two distinct qubit maps or as the tensor square of a decomposable map from a qubit to a ququart. To show our main result, we reduce the problem to Pauli diagonal maps. We then characterize the cone of decomposable ququart Pauli diagonal maps by determining all 252 extremal rays of ququart Pauli diagonal maps that are both completely positive and completely copositive. These extremal rays split into three disjoint orbits under a natural symmetry group, and two of these orbits contain only entanglement breaking maps. Finally, we develop a general combinatorial method to determine the extremal rays of Pauli diagonal maps that are both completely positive and completely copositive between multi-qubit systems using the ordered spectra of their Choi matrices. Classifying these extremal rays beyond ququarts is left as an open problem.
Katia Romero Felizardo, Érica Ferreira de Souza, Bianca Minetto Napoleão
et al.
Context: Several researchers have reported their experiences in applying secondary studies (Systematic Literature Reviews - SLRs and Systematic Mappings - SMs) in Software Engineering (SE). However, there is still a lack of studies discussing the value of performing secondary studies in an academic context. Goal: The main goal of this study is to provide an overview on the use of secondary studies in an academic context. Method: Two empirical research methods were used. Initially, we conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects. Results: Our results show benefits of using secondary studies in the academic context, such as, providing an overview of the literature as well as identifying relevant research literature on a research area enabling to find reasons to explain why a research project should be approved for a grant and/or supporting decisions made in a research project. Difficulties faced by SE graduate students with secondary studies are that they tend to be conducted by a team and it demands more effort than a traditional review. Conclusions: Secondary studies are valuable to graduate students. They should consider conducting a secondary study for their research project due to the benefits and contributions provided to develop the overall project. However, the advice of an experienced supervisor is essential to avoid bias. In addition, the acquisition of skills can increase student's motivation to pursue their research projects and prepare them for both academic or industrial careers.
Ahmed Alqaraawi, Martin Schuessler, Philipp Weiß
et al.
Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.
Mario Zusag, Sujal Desai, Marcello Di Paolo
et al.
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate follow-up challenging. We propose a novel method to train Convolutional Neural Networks using only regional labels on the presence of pathological signs, to not only detect CPA, but also spatially localize pathological signs. We use average intensity projections within different ranges of Hounsfield-unit (HU) values, transforming input 3D CT scans into 2D RGB-like images. CNN architectures are trained for hierarchical tasks, leading to precise activation maps of pathological patterns. Results on a cohort of 352 subjects demonstrate high classification accuracy, localization precision and predictive power of 2 year survival. Such tool opens the way to CPA patient stratification and quantitative follow-up of CPA pathological signs, for patients under drug therapy.
In this paper, we study three aspects of the $p-$adic Möbius maps. One is the group $\mathrm{PSL}(2,\mathcal{O}_{p})$, another is the geometrical characterization of the $p-$adic Möbius maps and its application, and the other is different norms of the $p-$adic Möbius maps. Firstly, we give a series of equations of the $p-$adic Möbius maps in $\mathrm{PSL}(2,\mathcal{O}_{p})$ between matrix, chordal, hyperbolic and unitary aspects. Furthermore, the properties of $\mathrm{PSL}(2,\mathcal{O}_{p})$ can be applied to study the geometrical characterization, the norms, the decomposition theorem of $p-$adic Möbius maps, and the convergence and divergence of $p-$adic continued fractions. Secondly, we classify the $p-$adic Möbius maps into four types and study the geometrical characterization of the $p-$adic Möbius maps from the aspects of fixed points in $\mathbb{P}^{1}_{Ber}$ and the invariant axes which yields the decomposition theorem of $p-$adic Möbius maps. Furthermore, we prove that if a subgroup of $\mathrm{PSL}(2,\mathbb{C}_{p})$ containing elliptic elements only, then all elements fix the same point in $\mathbb{H}_{Ber}$ without using the famous theorem--Cartan fixed point theorem, and this means that this subgroup has potentially good reduction. In the last part, we extend the inequalities obtained by Gehring and Martin\cite{F.G1,F.G2}, Beardon and Short \cite{AI} to the non-archimedean settings. These inequalities of $p$-adic Möbius maps are between the matrix, chordal, three-point and unitary norms. This part of work can be applied to study the convergence of the sequence of $p-$adic Möbius maps which can be viewed as a special cases of the work in \cite{CJE} and the discrete criteria of the subgroups of $\mathrm{PSL}(2,\mathbb{C}_{p})$.
This paper introduces a class of polynomial maps in Euclidean spaces, investigates the conditions under which there exist Smale horseshoes and uniformly hyperbolic invariant sets, studies the chaotic dynamical behavior and strange attractors, and shows that some maps are chaotic in the sense of Li-Yorke or Devaney. This type of maps includes both the Logistic map and the Hénon map. For some maps in three-dimensional spaces under certain conditions, if the expansion dimension is equal to one or two, it is shown that there exist a Smale horseshoe and a uniformly hyperbolic invariant set on which the system is topologically conjugate to the two-sided fullshift on finite alphabet; if the system is expanding, then it is verified that there is an forward invariant set on which the system is topologically semi-conjugate to the one-sided fullshift on eight symbols. For three types of high-dimensional polynomial maps with degree two, the existence of Smale horseshoe and the uniformly hyperbolic invariant sets are studied, and it is proved that the map is topologically conjugate to the two-sided fullshift on finite alphabet on the invariant set under certain conditions. Some interesting maps with chaotic attractors and positive Lyapunov exponents in three-dimensional spaces are found by using computer simulations. In the end, two examples are provided to illustrate the theoretical results.