Global migration governance from below in times of COVID-19 and “Zoomification”: civil society in „invited “ and „invented “ spaces

The global pandemic has resulted in ad hoc unilateral policies on migration, mobility and border management while at the same time emphasizing the need for global cooperation. For global governance in this field to be effective, it needs to include stakeholders beyond states and international institutions. The Global Compact for safe, orderly and regular Migration (GCM) highlights the role of those groups directly affected by global policies, i.e. migrants and their organisations. The goal of this paper is to analyse the role of civil society in global migration governance in times of COVID-19. It employs a comparative approach between “invented” and “invited” spaces. “Invited spaces” in this context refer to spaces created by international organisations such as the United Nations Network on Migration’s “Stakeholder Listening Sessions” on COVID-19 and the resulting statements. “Invented Spaces” refer to self-organized spaces by civil society actors. The paper will compare these spaces regarding their openness, the central issues and calls for specific policy measures, the stakeholders involved and the strategies they employ. I argue that the pandemic has strengthened the “input” dimension for migrant civil society in global governance. This relates to the structure/format as well as to the content of the participation. “Zoomification” has opened up access to “invited” spaces while pushing forward the creation and scope of “invented” spaces”. There are indicators that the pandemic has also influenced parts of the output dimension, although it is too early to assess whether this will have a lasting effect on policies on the ground.

Detection of Pine Cones in Natural Environment Using Improved YOLOv4 Deep Learning Algorithm

Achieving the rapid and accurate detection of pine cones in the natural environment is essential for yield estimation and automatic picking. However, the complex background and tiny target pose a significant challenge to pine cone detection. This paper proposes a pine cone detection method using the improved You Only Look Once (YOLO) version 4 algorithm to overcome these challenges. First, the original pine cone image data come from a natural pine forest. Crawler technology is utilized to collect more pine cone images from the Internet to expand the data set. Second, the densely connected convolution network (DenseNet) structure is introduced in YOLOv4 to improve feature reuse and network performance. In addition, the backbone network is pruned to reduce the computational complexity and keep the output dimension unchanged. Finally, for the problem of feature fusion at different scales, an improved neck network is designed using the scale-equalizing pyramid convolution (SEPC). The experimental results show that the improved YOLOv4 model is better than the original YOLOv4 network; the average values of precision, recall, and AP reach 96.1%, 90.1%, and 95.8%; the calculation amount of the model is reduced by 21.2%; the detection speed is fast enough to meet the real-time requirements. This research could serve as a technical reference for estimating yields and automating the picking of pine cones.

Mutual information-based multi-output tree learning algorithm

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm.

LOCC protocols with bounded width per round optimize convex functions

We start with the task of discriminating finitely many multipartite quantum states using LOCC protocols, with the goal to optimize the probability of correctly identifying the state. We provide two different methods to show that finitely many measurement outcomes in every step are sufficient for approaching the optimal probability of discrimination. In the first method, each measurement of an optimal LOCC protocol, applied to a [Formula: see text]-dimensional local system, is replaced by one with at most [Formula: see text] outcomes, without changing the probability of success. In the second method, we decompose any LOCC protocol into a convex combination of a number of “slim protocols” in which each measurement applied to a [Formula: see text]-dimensional local system has at most [Formula: see text] outcomes. To maximize any convex functions in LOCC (including the probability of state discrimination or fidelity of state transformation), an optimal protocol can be replaced by the best slim protocol in the convex decomposition without using shared randomness. For either method, the bound on the number of outcomes per measurement is independent of the global dimension, the number of parties, the depth of the protocol, how deep the measurement is located, and applies to LOCC protocols with infinite rounds, and the “measurement compression” can be done “top-down” — independent of later operations in the LOCC protocol. The second method can be generalized to implement LOCC instruments with finitely many classical outcomes: if the instrument has [Formula: see text] coarse-grained final measurement outcomes, global input dimension [Formula: see text] and global output dimension [Formula: see text] for [Formula: see text] conditioned on the [Formula: see text]th outcome, then one can obtain the instrument as a convex combination of no more than [Formula: see text] slim protocols; that is, [Formula: see text] bits of shared randomness suffice.

Computing Classical-Quantum Channel Capacity Using Blahut–Arimoto Type Algorithm: A Theoretical and Numerical Analysis

Based on Arimoto’s work in 1972, we propose an iterative algorithm for computing the capacity of a discrete memoryless classical-quantum channel with a finite input alphabet and a finite dimensional output, which we call the Blahut–Arimoto algorithm for classical-quantum channel, and an input cost constraint is considered. We show that, to reach ε accuracy, the iteration complexity of the algorithm is upper bounded by log n log ε ε where n is the size of the input alphabet. In particular, when the output state { ρ x } x ∈ X is linearly independent in complex matrix space, the algorithm has a geometric convergence. We also show that the algorithm reaches an ε accurate solution with a complexity of O ( m 3 log n log ε ε ) , and O ( m 3 log ε log ( 1 − δ ) ε D ( p * | | p N 0 ) ) in the special case, where m is the output dimension, D ( p * | | p N 0 ) is the relative entropy of two distributions, and δ is a positive number. Numerical experiments were performed and an approximate solution for the binary two-dimensional case was analysed.

Model Input and Output Dimension Reduction Using Karhunen–Loève Expansions With Application to Biotransport

We consider biotransport in tumors with uncertain heterogeneous material properties. Specifically, we focus on the elliptic partial differential equation (PDE) modeling the pressure field inside the tumor. The permeability field is modeled as a log-Gaussian random field with a prespecified covariance function. We numerically explore dimension reduction of the input parameter and model output. Specifically, truncated Karhunen–Loève (KL) expansions are used to decompose the log-permeability field, as well as the resulting random pressure field. We find that although very high-dimensional representations are needed to accurately represent the permeability field, especially in presence of small correlation lengths, the pressure field is not sensitive to high-order KL terms of the input parameter. Moreover, we find that the pressure field itself can be represented accurately using a KL expansion with a small number of terms. These observations are used to guide a reduced-order modeling approach to accelerate computational studies of biotransport in tumors.

Penilaian Status Keberlanjutan E-Ticketing Bus Trans Semarang Mendukung Kota Pintar dengan Pendekatan Multidimensional Scaling

Status Assessment of E-Ticketing Sutainability for Trans Semarang Bus to Support Smart City using Multidimensional Scaling Approaches: One of the public service facilities using IT in the transportation sector is the eticketing of the Trans Semarang Bus. E-ticketing is a cashless bus ticket payment with the aim to facilitate the service process. The implementation of E- ticketing Trans Semarang Bus engine technology sometimes experiences the obstacles due to crowded service conditions, non-conducive network and signals, so the e-ticketing machine is error and unable to quickly detect data balance. The service clerk has not been able to be technically competent to deal with problems that occur suddenly when the e-ticketing machine is in trouble. The aim of conducting research is to assess the sustainability status of e-ticketing Trans Semarang Bus, which is viewed from the dimensions of input, process, and output, in attempt to determine the factors/attributes that influence the sustainability of the e-ticketing Trans Semarang Bus. The research employed descriptive qualitative-quantitative method with Multidimensional Scaling analysis. This research is expected to be an input in the formulation of the smart city development strategy of Semarang City Government, especially in the transportation sector. The results of the research show the sustainability status of e-ticketing Trans Semarang Bus on the good conditions of input dimension because it is supported by an integrated service, device and technology-oriented roadmap; integrated IT system framework in terms of hardware, software and networks; and IT network infrastructure. The sustainability status of e-ticketing Trans Semarang Bus is in good condition in the dimensions of the process which is supported by collaboration and cooperation among stakeholders; operational financing includes IT professionals, operations, maintenance. Meanwhile, the output dimension has a fairly sustainable status. This might be caused by other factors that need to be comprehensively evaluated.Keywords : Sustainability, Bus Trans Semarang, smart city, multidimensional scaling. Salah satu fasilitas layanan publik yang menggunakan IT di sektor transportasi adalah e-ticketing Bus Trans Semarang. Eticketing merupakan pembayaran tiket bus cashless yang bertujuan untuk mempermudah proses pelayanan. Penerapan teknologi mesin e-ticketing Bus Trans Semarang terkadang mengalami kendala akibat kondisi pelayanan yang ramai serta jaringan dan sinyal yang tidak kondusif, sehingga mesin e-ticketing menjadi error sehingga tidak mampu mendeteksi data saldo dengan cepat. Secara kompetensi teknis, petugas pelayanan belum mampu mengatasi permasalahan yang terjadi secara tiba-tiba ketika mesin e-ticketing mengalami gangguan (trouble). Tujuan penelitian mengenai penilaian status keberlanjutan e-ticketing Bus Trans Semarang yang ditinjau dari dimensi input, proses, dan output adalah untuk mengetahui faktor/atribut yang berpengaruh terhadap keberlanjutan e-ticketing Bus Trans Semarang. Penelitian ini menggunakan metode deskriptif kualitatif-kuantitatif dengan analisis Multidimensional Scaling. Penelitian ini diharapkan dapat menjadi masukan dalam perumusan strategi pengembangan kota pintar Pemerintah Kota Semarang khususnya sektor transportasi. Hasil penelitian menunjukkan bahwa status keberlanjutan e-ticketing Bus Trans Semarang pada dimensi input dalam kondisi baik karena didukung dengan roadmap terintegrasi yang berorientasi pada service, device, dan teknologi; framework sistem IT yang terintegrasi dari sisi hardware, software, dan jaringan; dan infrastruktur jaringan IT. Status keberlanjutan e-ticketing Bus Trans Semarang pada dimensi proses dalam kondisi baik karena didukung kolaborasi dan kerjasama antar stakeholders; pembiayaan operasional yang mencakup profesional IT, operasi, pemeliharaan, pelatihan, dan konsultan; dan interoperabilitas platform IT pada sisi aplikasi dan service. Sedangkan dimensi output memiliki status cukup berkelanjutan. Hal ini kemungkinan dapat disebabkan oleh faktor lain yang perlu untuk dievaluasi secara komprehensif.Kata kunci :Keberlanjutan, Bus Trans Semarang, Kota Pintar, Multidimensional Scaling.

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input – the percentage of the total variability in the output attributed to the changes in that input – by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5–40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator–PCA and GAM methods accurately estimated the SIs of the ∼ 2000 methane lifetime outputs but were on average 24 and 37 times faster, respectively.

Internal Audit Quality: Insights from Audit Committee Members, Senior Management, and Internal Auditors

SUMMARY We investigate the concept of internal audit function (IAF) quality from a multi-stakeholder perspective through conducting 36 interviews with key IAF stakeholder groups: audit committee members, senior management, internal audit executives, and outsourced internal audit partners from the major accounting firms. We adapt established quality frameworks that suggest quality is a five-dimensional construct (including input, process, output, outcome, and contextual dimensions) to the internal audit context. We find that the various stakeholder groups focus on different quality dimensions in their evaluation of IAF quality. For example, the groups focus on the process dimension (internal audit executives), output dimension (audit committee members and internal audit partners), or outcome dimension (senior management and internal audit partners). We also find that the five dimensions comprise multiple indicators of IAF quality. We conduct six supplementary interviews with external audit partners to compare their insights on IAF quality to the focal IAF stakeholder groups. External auditors evaluate quality via the output dimension after an ex ante assessment focusing on the input dimension. Finally, we contribute to the IAF quality literature by developing a multi-stakeholder IAF quality framework.

From the 8-Hour Day to the 40-Hour Week: Legitimization Discourses of Labour Legislation between the Wars in France and Belgium

In the interwar period both France and Belgium passed legislation reducing the number of working hours and established a hybrid regulatory regime lending a certain degree of official authority to collective agreements. The paper analyses discourses by scholars who, as experts, were close to the political elites, and who tried to legitimize this kind of co-regulation by pointing out the inefficiency of state intervention and the epistemic authority of non-state actors. Stressing the output dimension of legitimacy and the improved quality of legal norms, these discourses had a technocratic tendency and ultimately argued in favour of a shift of power from the legislative to the administrative branch of government.