text:
# Efficient Computation of Generalized Ising Polynomials on Graphs with Fixed Clique-Width

Abstract : Graph polynomials which are definable in Monadic Second Order Logic ($\mathrm {MSOL}$) on the vocabulary of graphs are Fixed-Parameter Tractable ($\mathrm {FPT}$) with respect to clique-width. In contrast, graph polynomials which are definable in $\mathrm {MSOL}$ on the vocabulary of hypergraphs are fixed-parameter tractable with respect to tree-width, but not necessarily with respect to clique-width. No algorithmic meta-theorem is known for the computation of graph polynomials definable in $\mathrm {MSOL}$ on the vocabulary of hypergraphs with respect to clique-width. We define an infinite class of such graph polynomials extending the class of graph polynomials definable in $\mathrm {MSOL}$ on the vocabulary of graphs and prove that they are Fixed-Parameter Polynomial Time ($\mathrm {FPPT}$ or $\mathrm {XP}$) computable, i.e. that they can be computed in time $O(n^{f(k)})$, where n is the number of vertices and k is the clique-width.
Type de document :
Communication dans un congrès
Mohammed Taghi Hajiaghayi; Mohammad Reza Mousavi. 1st International Conference on Theoretical Computer Science (TTCS), Aug 2015, Tehran, Iran. Lecture Notes in Computer Science, LNCS-9541, pp.135-146, 2016, Topics in Theoretical Computer Science. 〈10.1007/978-3-319-28678-5_10〉
Domaine :
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https://hal.inria.fr/hal-01446257
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Soumis le : mercredi 25 janvier 2017 - 16:50:39
Dernière modification le : dimanche 31 décembre 2017 - 09:44:02
Document(s) archivé(s) le : mercredi 26 avril 2017 - 14:57:59

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### Citation

Tomer Kotek, Johann Makowsky. Efficient Computation of Generalized Ising Polynomials on Graphs with Fixed Clique-Width. Mohammed Taghi Hajiaghayi; Mohammad Reza Mousavi. 1st International Conference on Theoretical Computer Science (TTCS), Aug 2015, Tehran, Iran. Lecture Notes in Computer Science, LNCS-9541, pp.135-146, 2016, Topics in Theoretical Computer Science. 〈10.1007/978-3-319-28678-5_10〉. 〈hal-01446257〉

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# Simulation of a Gaussian process on $R^2$ with a stationary kernel using the Karhunen-Loève expansion

Assume $X(\omega, t) \sim \mathcal{N}(0, K(\cdot, \cdot))$ is a real-valued, centered Gaussian process on $R^2$, i.e., $X: \Omega \times R^2 \to R$. Let the covariance function of the process be stationary, i.e., $K(t, s) = K(t - s)$, $\forall t, s \in R^2$. More specifically, let it be exponential: $K(t, s) = \exp(-\|t - s\|/L)$ where $\|\cdot\|$ stands for the Euclidean distance, and $L$ denotes the correlation length. Using the Karhunen-Loève expansion, $X$ can be represented as the following summation:

$X(t, \omega) = \sum_{i=1}^\infty \sqrt{\lambda_i} \phi_i(t) \xi_i(\omega)$

where $\xi_i$ are independent, standard Gaussians, and $\phi_i$ and $\lambda_i$ are the eigenfunctions and eigenvalues of the covariance function $K$, which, in our case, can be computed analytically. For practical computations, the expansion above is truncated to keep, say, $n$ terms.

The question is: How to draw samples of the process on $R^2$, i.e., on a two-dimensional grid of fixed points, using the first $n$ eigenfunctions and eigenvalues of $K$? The problem is that the eigenfunctions are somewhat one-dimensional, and I cannot figure out how to properly use them to get realizations of $X$ on a plane. To be honest, the analytical solution that I claimed previously was initially obtained for processes on $R$, so I am not even sure that it applies directly to $R^2$. When I plug in the norms of points' coordinates into $\phi_i$, I get radial patterns, which is presumably not correct.

Also, it is easy to simulate $X$ by constructing the corresponding covariance matrix for a set of points, which boils down to drawing samples from a multivariate Gaussian distribution. I am not interested in this approach.

I would be very grateful for any ideas. Thank you.

-

%choose a kernel (covariance function)

kernel = 2;
switch kernel
case 1; k = @(x,y) 1*x'*y; %linear
case 2; k = @(x,y) exp(-100*(x-y)'*(x-y));
case 3; k = @(x,y) exp(-1*sqrt((x-y)'*(x-y)));
end

%choose points at which to sample
points = (0:0.02:1)';
[U,V] = meshgrid(points, points);
x=[U(:) V(:)]';
n=size(x,2);

%construct the covariance matrix
C = zeros(n,n);
for i = 1:n
for j = 1:n
C(i,j) = k(x(:,i), x(:,j));
end
end
%sample from the Gaussian process at these points
u = randn(n,1);
[A,S,B]= svd(C);
z = A*sqrt(S)*u;

%plot
figure(2); clf;
Z = reshape(z, sqrt(n), sqrt(n));
surf(U,V,Z);

-

Hello There is a video for mathmonk in youtube which explain how to do that: https://www.youtube.com/watch?v=e7as_wY0hUI&list=HL1361329139&feature=mh_lolz Regards

-
Please pay your attention to the part starting from "Also, ..." in my question. This is exactly what Mathematical Monk is showing in the video. I would like to do the same, but using the decomposition above. –  Ivan Feb 20 '13 at 6:47
Please try to describe as much here as possible in order to make the answer self-contained. Links are fine as support, but they can go stale and then an answer which is nothing more than a link loses its value. –  robjohn Feb 20 '13 at 9:21
I thought that the Mathematical Monk used this composition, as I mentioned above. He obtained the eigenvalues and vectors and did the sampling for finite value of n. –  ibrahim Feb 20 '13 at 10:25
%choose a kernel (covariance function) kernel = 2; switch kernel case 1; k = @(x,y) 1*x'*y; %linear case 2; k = @(x,y) exp(-100*(x-y)'*(x-y)); case 3; k = @(x,y) exp(-1*sqrt((x-y)'*(x-y))); end %choose points at which to sample points = (0:0.02:1)'; [U,V] = meshgrid(points, points); x=[U(:) V(:)]'; n=size(x,2); C = zeros(n,n); for i = 1:n for j = 1:n C(i,j) = k(x(:,i), x(:,j)); end end %sample from the Gaussian process at these points u = randn(n,1); [A,S,B]= svd(C); z = A*sqrt(S)*u; %plot figure(2); clf; Z = reshape(z, sqrt(n), sqrt(n)); surf(U,V,Z); –  ibrahim Feb 22 '13 at 0:20

format: plain


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main-content

Open Access 2021 | Open Access | Book

# The Palgrave Handbook of Development Cooperation for Achieving the 2030 Agenda

## Contested Collaboration

Editors: Prof. Sachin Chaturvedi, Heiner Janus, Stephan Klingebiel, Xiaoyun Li, André de Mello e Souza, Elizabeth Sidiropoulos, Dorothea Wehrmann

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This open access handbook analyses the role of development cooperation in achieving the 2030 Agenda in a global context of ‘contested cooperation’. Development actors, including governments providing aid or South-South Cooperation, developing countries, and non-governmental actors (civil society, philanthropy, and businesses) constantly challenge underlying narratives and norms of development. The book explores how reconciling these differences fosters achievement of the Sustainable Development Goals.

Open Access

##### Chapter 1. Development Cooperation in the Context of Contested Global Governance

The 2030 Agenda for Sustainable Development has successfully set a normative framework for global cooperation, including development cooperation. Yet, the implementation of this agenda is characterised by power struggles and unresolved contestations. Hence, it is uncertain whether the 2030 Agenda will be achieved. Therefore, a key question is how different narratives and norms in development cooperation can be reconciled to achieve the 2030 Agenda. As a response and guiding framework, this chapter explores the concept of “contested cooperation”, drawing on research on contested global governance and contested multilateralism. Applying this conceptual perspective not only yields theoretical insights but also helps in better understanding the practical challenges that development actors face in implementing the 2030 Agenda.

Sachin Chaturvedi, Heiner Janus, Stephan Klingebiel, Xiaoyun Li, André de Mello e Souza, Elizabeth Sidiropoulos, Dorothea Wehrmann

#### Global Cooperation for Achieving the SDGs

Open Access

##### Chapter 2. Maximising Goal Coherence in Sustainable and Climate-Resilient Development? Polycentricity and Coordination in Governance

This chapter argues that most efforts to mobilise non-state and subnational actor engagement so far has insufficiently contributed to goal coherence—the balanced implementation of internationally agreed goals. Despite the increased level of attention being given to the polycentric nature of sustainable development and climate governance—especially the role of non-state and subnational actors—the predominant focus of both policy-makers and researchers has been on filling functional gaps, for example closing the global mitigation gap, or financing gaps. As a result, voluntariness and self-organisation in polycentric governance could increase the level of incoherence. Insights on emerging polycentric structures should be combined with tools that map (goal) coherence. The combination of these fields of knowledge could inform supportive policies, for instance in development cooperation to ensure greater coherence in implementing sustainable development priorities.

Sander Chan, Gabriela Iacobuta, Ramona Hägele

Open Access

##### Chapter 3. Development Finance and the 2030 Goals

In their attempt to stimulate an exponential financing rise of “billions to trillions”, the Sustainable Development Goals (SDGs) and the 2030 Agenda are helping to normalise and promote a radical shift in development finance. A growing concern is that private finance is not forthcoming, calling into question the “billions to trillions” model. In this chapter, however, I focus on the risks of “successfully” moving in this direction. The mainstream bilateral and multilateral community appear too sanguine, and even naïve, about the financial sector. Any analysis of the SDGs must be attentive to the possibilities and risks of the emerging development finance regime that they are helping to legitimate.1

Emma Mawdsley

Open Access

##### Chapter 4. Transnational Science Cooperation for Sustainable Development

With the 2030 Agenda, the development paradigm has shifted towards global sustainable development, but modes of cooperation between actors in the Global North and South still cling to traditional patterns of cooperation, reproducing antiquated knowledge hierarchies. Departing from technical cooperation, transnational research cooperation may be a more equitable mode of cooperation with the potential of developing innovative solutions for sustainable development. Yet, its potential is not fully realised. Science policies on the national level and global governance mechanisms need to set a beneficial framework, ensuring that expectations of partnerships and outcomes for global sustainable development can be met. The current incoherence of national science and development cooperation policies may be aggravated by existing gaps in global governance mechanisms in view of sustainability-oriented transnational research cooperation.

Anna Schwachula

#### Development Cooperation: Narratives and Norms

Open Access

##### Chapter 5. An Evolving Shared Concept of Development Cooperation: Perspectives on the 2030 Agenda

With a collective commitment to the Sustainable Development Goals (SDGs), the worlds of development cooperation, in general, and development finance, in particular, are keenly looking for new and innovative sources of financing for effective and timely outcomes. This chapter considers three successful efforts at providing global public goods for the goal of achieving the 2030 Agenda that operationalise development cooperation in a more “shared” manner, thereby opening up space for engagement by multiple stakeholders in a less hierarchical manner. It identifies three common ingredients that make meaningful contributions to the success of these efforts: access to resources, access to participatory institutions, and ensuring multi-stakeholder participation.

Milindo Chakrabarti, Sachin Chaturvedi

Open Access

##### Chapter 6. The Globalisation of Foreign Aid: Global Influences and the Diffusion of Aid Priorities

This chapter explains why bilateral aid donors often look and act alike, despite distinct national interests and histories—a phenomenon previously identified as “the globalisation of foreign aid”. It identifies processes that drive the similarity of aid actors and the diffusion of aid priorities, contributing to the globalisation of aid. The chapter reflects upon: (1) how the isomorphism of aid institutions and the homogenisation of aid policy represent the effects of these common processes of globalisation; (2) the implications of the globalisation of aid on the 2030 Agenda; and (3) how the globalisation of aid contributes to the “contested cooperation” framework woven through this volume.

Liam Swiss

Open Access

##### Chapter 7. The Untapped Functions of International Cooperation in the Age of Sustainable Development

Universality is one of the key novel characteristics of the 2030 Agenda for Sustainable Development. By applying a functional approach to external policies, this chapter challenges traditional notions of development cooperation and shows that the agenda’s means of implementation as well as their application are lopsided towards so-called developing countries. However, achieving the Sustainable Development Goals critically depends on the agenda’s implementation also within the so-called developed countries as well as between them. Therefore, the function of development cooperation to shape conditions within other countries by using cooperative and promotional instruments should be exerted also vis-à-vis “developed countries”. International cooperation for sustainable development needs to become universal, multimodal, mutual, and transformative if it wants to deliver change, not aid.

Open Access

##### Chapter 8. The Difficulties of Diffusing the 2030 Agenda: Situated Norm Engagement and Development Organisations

The adoption of the 2030 Agenda was only the end of the beginning. To realise the goals, the challenge that lies ahead—prompted by its universal nature—is to implement the agenda’s rapid diffusion into national policies and reforms needed all over the world. This is no easy task. The historical legacies of global normative agreements have taught us that global norms rarely bring about the forms of change that we expect them to. How can we explain these apparent challenges of spreading global norms across the world? We argue that the diffusion of the 2030 Agenda and the Sustainable Development Goals is not only challenged because of contemporary political circumstances, but also because of the fundamental situated nature of how actors engage with global norms.

Open Access

##### Chapter 9. Diffusion, Fusion, and Confusion: Development Cooperation in a Multiplex World Order

Development cooperation (DC) is shaped by norms. We aim at filling a gap of research on DC by using the academic debates in international relations on norms. Contrary to interpretations that consider developed countries as norm-makers and developing countries as norm-takers, our analysis provides evidence that—and highlights how—Southern agents have influenced the processes of norm-setting and norm-diffusion for DC. The OECD was the dominant norms “entrepreneur” for a long period of time; more recently, developing countries have played a significant role in setting DC norms. We identify the diverging norms for official development assistance and South-South cooperation and the interrelationship between both norm systems. Thus, norm-making, norm-taking, and norm-diffusion of two competing norm clusters are key terms of the contribution.

Paulo Esteves, Stephan Klingebiel

Open Access

##### Chapter 10. Conceptualising Ideational Convergence of China and OECD Donors: Coalition Magnets in Development Cooperation

This chapter analyses the development discourse on foreign aid to explore areas of convergence between the Organisation for Economic Co-operation and Development (OECD)’s Development Assistance Committee (DAC) donors and Chinese development cooperation. We apply the concept of “coalition magnets”—the capacity of an idea to appeal to a diverse set of individuals and groups, and to be used strategically by policy entrepreneurs to frame interests, mobilise support, and build coalitions. Three coalition magnets are identified: mutual benefit, development results, and the 2030 Agenda. The chapter finds that coalition magnets can be used to influence political change and concludes that applying a discursive approach provides a new conceptual opportunity for fostering closer engagement between OECD-DAC and Chinese development cooperation actors.

Heiner Janus, Lixia Tang

#### Measurements of Development Cooperation: Theories and Frameworks

Open Access

##### Chapter 11. Measuring Development Cooperation and the Quality of Aid

Countries and governments are divided on the fora and mechanisms for agreeing on effective development cooperation. But progress can be made on measuring what countries are doing in different areas of cooperation. This chapter looks at how we can assess the quality of development cooperation. It sets out a framework for measuring development cooperation across three areas: development finance; country policies affecting the exchange of goods, people, ideas, and capital; and global public goods. It considers the availability of measures against that framework and concludes on how these measures can be developed, or where new measures are needed, to provide a holistic assessment of development cooperation.

Ian Mitchell

Open Access

##### Chapter 12. Interest-Based Development Cooperation: Moving Providers from Parochial Convergence to Principled Collaboration

The motives for providing development assistance change according to historical and political trajectories but always combine varying degrees of altruism and selfishness. Currently, we are witnessing disruptive change in development cooperation as political leaders forward an increasingly self-regarding rationale for international assistance. In an empirical analysis of Northern donors’ narratives and aid allocation strategies, we define the meaning of the “national interest” in development cooperation and distinguish its principled and parochial formats. Our empirical analysis suggests Northern donors continue to allocate based on principled norms, though there is a noticeable deterioration in their public spiritedness as they simultaneously seek short-term domestic benefits from overseas giving. Calls for a principled national interest narrative may, however, enable normative collaboration across Northern and Southern actors.

Nilima Gulrajani, Rachael Calleja

Open Access

##### Chapter 13. Monitoring and Evaluation in South-South Cooperation: The Case of CPEC in Pakistan

Pakistan is a key country in China’s Belt and Road Initiative (BRI) where the China–Pakistan Economic Corridor (CPEC) is under implementation. An investment model of financing through loans, grants and private investments, CPEC is an example of South-South cooperation (SSC) having a number of benefits for both countries. Aimed at developing energy, industry, and communication infrastructure, the corridor initially valued at $46 billion but is now worth$62 billion. CPEC is expected to contribute significantly to socio-economic development and regional connectivity and trade. The main research question is, while implementing projects in Pakistan, to what extent China adheres to its avowed principles comprising features such as mutual respect, non-conditionality, equality, building local capacity and addressing actual needs of partner countries. Based mainly on the analysis of primary data collected during fieldwork in Pakistan, this research explores the extent to which the official narrative influences the actual practice of China’s development cooperation on the ground. To critically examine CPEC, this chapter uses a monitoring and evaluation framework developed by the Network of Southern Think Tanks (NeST), which is dedicated to generating systematic and clearly comparable knowledge on SSC (Besharati et al. 2017). The findings illustrate that, as per the five broad dimensions of the SSC framework, the China–Pakistan partnership under CPEC has performed well in the four areas of inclusive national ownership, horizontality, self-reliance and sustainability, and development effectiveness, but it has lagged in accountability and transparency.

Open Access

##### Chapter 14. The Implementation of the SDGs: The Feasibility of Using the GPEDC Monitoring Framework

Achieving the 2030 Agenda for Sustainable Development with its 17 Sustainable Development Goals (SDGs) requires significant behavioural changes from a variety of actors, including actors in development cooperation. Within this context, this chapter discusses important political as well as technical factors that influence the contribution of the Global Partnership for Effective Development Co-operation (GPEDC) and its monitoring framework to the implementation of the SDGs. These are, among other things, the complementarity of the GPEDC monitoring framework to the SDGs; the limited enthusiasm of development partners from the Global South, in particular China and India; the limited attention paid to the platform in general and the monitoring framework in particular by member countries of the Organisation for Economic Co-operation and Development (OECD); as well as the missing interpretative evaluations and follow-up processes in the aftermath of the respective monitoring rounds.

Debapriya Bhattacharya, Victoria Gonsior, Hannes Öhler

Open Access

##### Chapter 15. Counting the Invisible: The Challenges and Opportunities of the SDG Indicator Framework for Statistical Capacity Development

Rolando Avendano (Asian Development Bank), Johannes Jütting, and Manuel Kuhm (PARIS21) focus on the challenges and opportunities of the SDG indicator framework. They analyse three core problems associated with the data demands of the framework: the overburdening of national statistical systems, the increasing number of coordination failures between different actors of the data ecosystem, and a persistent lack of funding for statistical modernisation. The authors argue that the alignment of global requirements with national priorities; new forms of inclusive cooperation fostering trust, mutual learning, and accountability; and a global financing facility for development data are possible solutions to overcome key challenges of tracking progress towards the 2030 Agenda. The chapter concludes with an outlook on the role of data in the future of development cooperation.

Rolando Avendano, Johannes Jütting, Manuel Kuhm

#### Institutional Settings for Development Cooperation

Open Access

##### Chapter 16. Building a Global Development Cooperation Regime: Failed but Necessary Efforts

This chapter addresses two main questions. First, would a global development cooperation regime serve the 2030 Agenda better than current institutions? Second, how can recent failures to build such a regime be explained given attempts to render the GPEDC more inclusive and legitimate? Drawing from mainstream regime theory and examining recent transformations in the nature of global development cooperation, I argue that a broader regime is necessary and preferable to the status quo for reasons of efficiency. I also resort to models of stakeholder participation in governance institutions to suggest that Brazil, India, and China exited the GPEDC not because of incompatibility of interests with OECD countries, but because they did not value the regime sufficiently and found it difficult to exert influence in it.

André de Mello e Souza

Open Access

##### Chapter 17. Failing to Share the Burden: Traditional Donors, Southern Providers, and the Twilight of the GPEDC and the Post-War Aid System

In 2011 the International Community came together in Busan to create the Global Partnership for Effective Development Co-operation (GPEDC) as a common space to promote aid effectiveness. By 2014, however, the main providers of South-South cooperation (China, India, and Brazil) had left the GPEDC. This chapter seeks to understand why. Using an abstract model inspired by game theory to analyse the situation, it concludes that the main cause was that traditional donors and new Southern providers failed to agree on a consensual burden-sharing formula. The episode, meaningful in itself, illustrates the difficulties that the International Community faces to make room for the new emerging Southern powers in the post-war liberal order.

Gerardo Bracho

Open Access

##### Chapter 18. Should China Join the GPEDC? Prospects for China and the Global Partnership for Effective Development Cooperation

The Global Partnership for Effective Development Co-operation (GPEDC) is regarded as being the twenty-first-century epitome of a partnership within a polycentric world in the arena of international development cooperation. The chapter argues that, among the group of emerging economies, the GPEDC is considered to be just another form of the DAC’s recent transformation. That is why the emerging powers are sceptical—they are not a part of it; hence, they are reluctant to join it. However, we also explain why the GPEDC is a valuable platform for continuing the role of development cooperation for global development and implementing the 2030 Agenda. The chapter suggests how different stakeholders—including the emerging ones, particularly China—can work together to make the GPEDC a genuine partnership.

Xiaoyun Li, Gubo Qi

Open Access

##### Chapter 19. South Africa in Global Development Fora: Cooperation and Contestation

Since 1994, post-apartheid South Africa has embraced its Southern and African identity, with its foreign policy reflecting its aim to engage in international fora to advance Southern principles and the African voice. It also developed instruments to undertake South-South cooperation that focused largely on sharing its own lessons from its political transformation with post-conflict countries. This chapter explores the tensions between cooperation and contestation in South Africa’s involvement in global development and in its own development cooperation. South Africa has worked on global reforms with large emerging powers, but their interests are not always aligned with those of Africa. South Africa and Africa have derived leverage from their numbers and the related legitimacy in selecting what initiatives of more influential actors they will support.

Elizabeth Sidiropoulos

Open Access

##### Chapter 20. Middle Powers in International Development Cooperation: Assessing the Roles of South Korea and Turkey

This chapter analyses the foreign aid discourses of South Korea and Turkey in international development cooperation under the framework of middle-power theory. Korea and Turkey make use of their middle-power identity with the aim of increasing their presence around the globe, where foreign aid is used as an important foreign policy tool. They claim to be like-minded peers playing a bridging role between the developed and developing worlds. Despite opting for similar results in global politics, they show divergent strategic and ideational pathways in foreign aid. This creates significant implications for cooperation at the global level, especially because it questions the role of middle-power collaboration in facilitating possible rapprochement between traditional and emerging donors as well as within the Southern providers.

R. Melis Baydag

#### Aligning National Priorities with Development Cooperation/SDGs

Open Access

##### Chapter 21. The SDGs and the Empowerment of Bangladeshi Women

This chapter describes Bangladesh’s successes with advancing gender equality in the period of the Millennium Development Goals (MDGs), locating their origins in elite commitment to including women in the development process, and in the partnerships and aid that built the state and NGO capacity to reach them. The chapter reflects on the lessons of Bangladesh’s innovative and unexpected advances in the light of the new challenges posed by the Sustainable Development Goals (SDGs), notably those of early marriage and the achievement of decent work. The chapter asks whether contemporary conditions suggest that the elite commitment and state capacity that drove progress on the MDGs are up to meeting the more contentious and complex goals of the SDGs.

Naomi Hossain

Open Access

##### Chapter 22. Russia’s Approach to Official Development Assistance and Its Contribution to the SDGs

The chapter looks at the current developments in Russia’s official development assistance (ODA). In spite of its international isolation, the Russian government has expanded its ODA allocations since 2015, which total to about \$1 billion annually. The chapter describes the sectoral and channel distribution of Russian aid. Moreover, it identifies the key challenges of the Russian government in the framework of its ODA activities, such as monitoring and evaluation (M&E), achievement of the Sustainable Development Goals (SDGs), and its engagement with the business sector. Under consideration of the government’s undertakings in its striving to address these issues, the author reflects in conclusion on the prospects of establishing a national ODA M&E system, and options to contribute towards the achievement of the SDGs by 2030.

Yury K. Zaytsev

Open Access

##### Chapter 23. US Multilateral Aid in Transition: Implications for Development Cooperation

The United States, whose leadership through the Marshall Plan created the basis for modern-day development cooperation, has veered abruptly from its traditional role. An analysis of US funding trends shows that it has increasingly shifted from collective to specific interests, even as it has increased its multilateral aid. The United States is now actively shunning multilateral settings as part of its America First foreign policy, even when multilateral policies reinforce the international development priorities of the Trump administration, and its growing geopolitical competition with China is spilling into development assistance. This chapter explores the implications for development cooperation and whether these changes signal a more durable shift in US perspective.

Tony Pipa

#### The Contribution of SSC and Triangular Cooperation to the SDGs

Open Access

##### Chapter 24. “The Asian Century”: The Transformational Potential of Asian-Led Development Cooperation

Asia’s rise is having a profound impact on the policies and practices of development and South-South cooperation (SSC). This chapter describes the contours of these trends in relation to the sustainable development agenda. China and India are dominant players with their large-scale connectivity schemes, increasing multilateralism, and diversified partnerships. The author discusses the controversy, contestation, and opportunities these approaches generate. Finally, this chapter offers a set of recommendations for improving the effectiveness of Asian SSC to deliver the 2030 Agenda for Sustainable Development.

Anthea Mulakala

Open Access

##### Chapter 25. South-South Development Cooperation as a Modality: Brazil’s Cooperation with Mozambique

This chapter investigates the widespread claim that South-South development cooperation (SSDC) differs from North-South cooperation, as it is said to be based on horizontality, to be demand-driven, to create mutualmutual benefits, and to provide “Southern” solutions to development challenges through knowledge exchange. Based on an analysis of Brazil’s cooperation with Mozambique, the chapter shows that cooperation practices do not always follow the narrative around SSDC as a modality contesting established cooperation. The chapter further assesses what this means for the implementation of the 2030 Agenda and for the growing relevance ascribed to SSDC providers in international development cooperation.

Jurek Seifert

Open Access

##### Chapter 26. South Africa as a Development Partner: An Empirical Analysis of the African Renaissance and International Cooperation Fund

As South Africa looks to consolidate its role as a development partner, it remains an open question whether it can maintain a strong presence in Africa while facing significant challenges at home. With the economy struggling to grow and the government increasingly cutting back on expenditure, one has to wonder whether these cuts are translating into a reduction of its role as a development partner in Africa. With the eagerly awaited South African Development Partnership Agency in mind, this chapter examines data from the African Renaissance and International Cooperation Fund (ARF) between 2003 and 2015. It shows empirically that, despite increasing allocations and disbursements in the years following its inception, the global financial crisis and domestic challenges have taken their toll on the ARF’s activities.

Philani Mthembu

Open Access

##### Chapter 27. Triangular Cooperation: Enabling Policy Spaces

In the past decade, a number of studies, reports, and data have been produced on triangular cooperation (TrC). The focus of these publications is mainly on (i) the project level and/or (ii) political relations between stakeholders. I argue that, beyond being an effective modality for the implementation of development projects, TrC is an enabler of policy negotiation spaces. Through TrC, the clashes of traditional principles and practices with a new narrative of Southern providers are loosened, enabling spaces that do not directly confront contested political positions jeopardising the dialogue. The chapter identifies that TrC serves as a bridge for coordination between stakeholders. Findings suggest that it has been used for sharing costs and solutions as well as for the development of joint guidelines and processes.

Geovana Zoccal

Open Access

##### Chapter 28. Achieving the SDGs in Africa Through South-South Cooperation on Climate Change with China

Climate change poses a significant threat to socio-economic development in Africa. Achieving the Sustainable Development Goals (SDGs) will not be possible without fostering low-emission development and addressing the adverse impacts of climatic changes on the continent. China has emerged as a key development partner and is increasingly cooperating with African countries on climate change adaptation and mitigation. This chapter looks at the evolution of South-South cooperation on climate change between African countries and China over the past decades. We argue that South-South cooperation with China has a tremendous potential for African countries to realise their climate action priorities, and thus enable the achievement of their SDGs. However, to realise this potential, both sides need a more ambitious and targeted approach.

Moritz Weigel, Alexander Demissie

Open Access

##### Chapter 29. India as a Partner in Triangular Development Cooperation

The chapter argues that India’s emerging practice of triangular cooperation does not fit easily with established definitions and concepts. India’s special brand of engagement in triangular cooperation has the potential to reshape important aspects of the global architecture of development cooperation and make significant contributions to achieving the Sustainable Development Goals. The chapter suggests how Indian experience can inform the analysis and international practice to increase the value of triangular cooperation for developing countries.

Sebastian Paulo

#### The Role of Non-state Actors to the SDGs

Open Access

##### Chapter 30. Partnerships with the Private Sector: Success Factors and Levels of Engagement in Development Cooperation

Partnerships with private-sector actors are widely considered crucial for achieving the 2030 Agenda for Sustainable Development, but the ways of how to engage best with actors from the private sector in development cooperation are contested. Often it is feared that influential companies will hijack unregulated partnership initiatives for their own benefits. This chapter investigates different levels of engagement for partnerships with private-sector actors and discusses how they can be more successful. We show that it matters whether it is envisioned to incentivise and regulate private-sector engagement at the global or at the country level. The chapter’s main findings support context-specific approaches and emphasise the need to strengthen national development agencies as focal points for private-sector engagement in development cooperation.

Jorge A. Pérez-Pineda, Dorothea Wehrmann

Open Access

##### Chapter 31. The Role and Contributions of Development NGOs to Development Cooperation: What Do We Know?

Research has focussed on the function rather than volume of NGOs’ contributions to development cooperation. That a deeply managerial-driven aid system has constrained NGO effectiveness has been a critical focus, highlighting how this has led NGOs to prioritise service delivery over social justice. Yet, a lack of systematic analysis of development NGO sectors within donor countries means it has made it impossible to measure their full contributions to development cooperation. We explore new evidence of their growing and significant contributions vis-à-vis official development assistance and look at new donor policy innovations that move from a managerial to a transformative ideology for civil society funding. Such innovations have the potential to enhance partnerships and cooperation and reduce the contested nature of NGOs’ contributions to development cooperation.

Nicola Banks

Open Access

##### Chapter 32. Southern Think Tank Partnerships in the Era of the 2030 Agenda

Think tanks are important actors in global policy-making, and those from the Global South are gaining relevance. One premise of this chapter is that the 2030 Agenda’s calls for more effective partnerships and new types of knowledge. A second premise is that partnerships reflect a power distribution among partners; in some cases, these are horizontal, and in others asymmetrical. In this context, the chapter explores the relationships between think tanks from the Global South with each other, their Northern peers, and the broader international community. It concludes that think tanks can enable collaborations with a diversity of actors. To do so, however, think tanks need to adapt their business models and develop trust with other policy actors in order to remain effective.

Andrea Ordóñez-Llanos

Open Access

##### Chapter 33. Conclusion: Leveraging Development Cooperation Experiences for the 2030 Agenda—Key Messages and the Way Forward

This chapter provides an overview of how different narratives and norms in development cooperation can be reconciled towards achieving the 2030 Agenda based on the overall handbook. Drawing on key insights from different handbook chapters, we recap the narratives and norms that are shaping development cooperation, highlight the existing as well as new institutional sites of contestations, and provide examples of how international governance structures can enhance collaboration and cooperation. Looking forward, we conclude that researchers should continue to explore the duality of contestation and cooperation, as it is key to understanding and shaping the policy field of development cooperation.

Sachin Chaturvedi, Heiner Janus, Stephan Klingebiel, Xiaoyun Li, André de Mello e Souza, Elizabeth Sidiropoulos, Dorothea Wehrmann
##### Backmatter
Title
The Palgrave Handbook of Development Cooperation for Achieving the 2030 Agenda
Editors
Prof. Sachin Chaturvedi
Heiner Janus
Stephan Klingebiel
Xiaoyun Li
André de Mello e Souza
Elizabeth Sidiropoulos
Dorothea Wehrmann


text:
Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part I

This is part 1 of a three-part series on the Economics of Ethics.

Here’s the problem with the data and AI ethics conversation – if we can’t measure it, then we can’t monitor it, judge it, or change it.  We must find a way to transparently instrument and measure ethics. And that’ll become even more important as AI becomes more infused into the fabric of societal decisions around employment, credit, financing, housing, healthcare, education, taxes, law enforcement, legal representation, digital marketing and social media, news and content distribution, and more.  We must solve this AI codification and measurement challenge today and that is the objective of the “Economics of Ethics” blog series

Take heed, the day of judgment draweth nigh. Artificial Intelligence (AI) is going to force our hand with respect to how as a society we define and measure ethical behaviors. No longer can we allow short-term pursuits override long-term benefits. The larger benefits of society must be prioritized over individual instant gratification exploitation that is so prevalent today.  With AI’s ability to learn and adapt billions of times faster than humans (thanks to Moore’s Law), society must get the definition and codification of ethical behaviors right. Otherwise, we will end up working for AI versus AI working for us.

The warnings are everywhere about the dangers of poorly constructed, inadequately defined AI models that could run amok over humankind.

“AI, by mastering certain competencies more rapidly and definitively than humans, could over time diminish human competence and the human condition itself as it turns it into data. Philosophically, intellectually — in every way — human society is unprepared for the rise of artificial intelligence.”  – Henry Kissinger, MIT Speech, February 28, 2019

“The development of full artificial intelligence (AI) could spell the end of the human race. It would take off on its own, and re-design itself at an ever-increasing rate. Humans, limited by slow biological evolution, couldn’t compete and would be superseded.” – Stephen Hawking, BBC Interview on Dec 2, 2014

And don’t expect Asimov 3 rules of robotics (AI) to protect us:

• Law #1: A robot (AI) may not injure a human being or, through inaction, allow a human being to come to harm.
• Law #2: A robot (AI) must obey orders given it by human beings except where such orders would conflict with the First Law.
• Law #3: A robot (A) must protect its own existence as long as such protection does not conflict with the First or Second Law.

These laws may make for good books and movies, but they are insufficient in guiding the AI models that are used to make employment, credit, financing, housing, healthcare, education, taxes, law enforcement, legal representation, digital marketing and social media, content and news distribution and many more decisions that directly impact society.

Consequently, codifying “Ethics” is a major challenge in getting Artificial Intelligence (AI) models to do the “right” things in making ethical decisions.  Codifying ethics as variables and metrics is especially important because AI models are continuously learning and adapting – at speeds unfathomable by humans – to maximize “rewards” and minimize “penalties” based upon the variables and metrics that comprise the AI Utility Function (Figure 1).

Figure 1: Understanding How AI Models Work

In my blog “Is God an Economist?”, I suggested that society can achieve positive economic outcomes driven by the ethics of fairness, equality, justice, and generosity taught by the Bible. And the lessons in the Bible are really economic lessons; lessons about the creation and distribution of a more holistic definition of wealth of “value”.

Economics is a framework for the creation and distribution of wealth or value

In one example, a Massachusetts study shows the value potential of a fairness program called “meals for all students”. Massachusetts spends $2.4B annually on student mental health issues, diabetes, obesity, and cognitive development issues. Implementing “School Meals for All” would cost$100-120M annually, an investment that pays 10x dividends in improved health and education outcomes while creating society-contributing humans (Figure 2).

Another example, the National Forum on Early Childhood Policy and Programs found that early childhood programs can yield a $4 –$9 dollar return per $1 invested. In another study (2009 study of Perry Preschool), a childhood program for 3–5-year-olds delivered an estimated$7 – $12 per$1 invested (Figure 3)[1].

Figure 3: Ethical Behaviors Just Make Good Economic Sense

Yes, ethical behaviors are just good economics.  But before we try to “codify” ethical behaviors, we first need to get alignment on what we mean by the word “ethics”.

What are Ethics?  Ethics are Proactive.

Ethics is the moral principles that govern a person’s behavior or actions, where moral principles are the principles of “right and wrong” that are generally accepted by an individual or a social group.

Or as my mom use to say, “Ethics is what you do when no one is watching.”

An important characteristic of ethics is that ethics is proactive, not passive.  Ethics is a proactive endeavor for which one must be held responsible for taking the appropriate action. And if you don’t know the difference between passive and active ethics, then it’s time for a bible lesson refresher – The Parable of the Good Samaritan.

The Parable of the Good Samaritan is told by Jesus about a Jewish traveler who is stripped of clothing, beaten, and left for dead alongside the road. First a priest and then a Levite comes by, but both cross the road to avoid the man. Finally, a Samaritan (Samaritans and Jews despised each other) happens upon the traveler and helps the battered man anyway. The Samaritan bandages his wounds, transports him to an Inn on his beast of burden to rest and heal, and pays for the Samaritan’s care and accommodations at the Inn (Figure 4).

The priest and the Levite operated under the Passive Ethics of “Do No Harm”. Technically, they did nothing wrong. But that mindset is insufficient in a world driven by AI models. Our AI models must embrace Proactive Ethics by seeking to “Do Good”; that is, the AI Utility Function that guides the continuous learning and adapting aspects of the AI model must proactively seek to do good.

Economics of Ethics Summary – Part I

In Part I of the Economics of Ethic series, we talked about economics as a framework for the creation and distribution of society value. With AI’s ability to learn and adapt billions of times faster than humans, society must get the definition and codification of ethical behaviors right. Otherwise, we will end up working for AI versus AI working for us.

In Part II we will discuss the difference between financial and economic measures, and the role of laws and regulations in an attempt to establish societal moral or ethical codes.

[1] “High Return on Investment (ROI)” https://www.impact.upenn.edu/early-childhood-toolkit/why-invest/what-is-the-return-on-investment/


text:
# Sergei Yakovenko's blog: on Math and Teaching

## Thursday, November 20, 2014

### Lecture 6 (Nov. 21, 2014)

Filed under: Analytic ODE course — Sergei Yakovenko @ 8:20
Tags: , ,

## Zeros of solutions of linear equations

Nontrivial (i.e., not identically zero) solutions of linear ordinary differential equations obviously possess certain properties concerning their roots (points where these solutions vanish). The simplest, in a sense paradigmal property, is the following.

Prototheorem. Let $u$ be a nontrivial solution of a sufficiently regular linear ordinary differential equation $Lu=0$ of order $n>0$. Then $u$ cannot have a root of multiplicity greater or equal than $n-1$.

Here by regularity we mean the condition that the operator $L=\partial^n+a_1(t)\partial^{n-1}+\cdots+a_{n-1}(t)\partial+a_n(t)$ has coefficients smooth enough to guarantee that any solution $u(t)$ near any point $a$ in the domain of its definition is uniquely determined by the initial conditions $u(a),u'(s),\dots,u^{(n-1)}(a)$.

Indeed, if $u$ has a root of multiplicity $n$, that is, all first $n-1$ derivatives of $u$ at $a$ vanish, then $u^{(n)}(a)=0$ by virtue of the equation and hence the by the uniqueness $u(t)$ must be identically zero.

In particular, solutions of first order equation $u'+a_1(t)u=0$ are nonvanishing, solutions of any second order equation $u''+a_1(t)u'+a_2(t)u=0$ may have only simple roots etc.

Theorem (de la Vallee Poussin, 1929). Assume that the coefficients of the LODE

$u^{(n)}+a_1(t)u^{(n-1)}+\cdots+a_{n-1}(t)u'+a_n(t)u=0,\qquad t\in[0,\ell],\qquad (\dag)$

are explicitly bounded,  $|a_k(t)|\leqslant A_k\in\mathbb R_+,\ \forall t\in[0,\ell],\ k=1,\dots,n$.

Assume that the bounds are small relative to the length of the interval, i.e.,

$\displaystyle \sum_{k=1}^n \frac{A_k}{k!}\ell^k<1.\qquad (*)$

Then any nontrivial solution of the equation has no more than $n-1$ isolated roots on $[0,\ell]$ .

## Novikov’s counterexample

What about linear systems of the first order?

Consider the system $\dot x=A(t)x$ with $x=(x_1,\dots,x_n)\in \mathbb R^n$ and the norm $\|A(t)\|$ explicitly bounded on $[0,\ell]$. Consider all possible linear combinations $u=\sum_k c_k x_k(t),\ c\in\mathbb R^n$. Can one expect a uniform upper bound for the number of roots of all combinations?

Let $a(t)$ be a polynomial having many zeros on $[0,t]$. Consider the $2\times 2$-system of the form

$\displaystyle \dot x_1=a(t)x_1,\qquad \dot x_2=(\dot a+ a^2)x_1.$

The first equation defines a nonvanishing function $x_1(t)$, the second equation – its derivative which vanishes at all roots of $a(t)$.

By replacing $a(t)$ by $\varepsilon a(t)$ one can achieve an arbitrarily small sup-norm of the coefficients of this system on the segment $[0,\ell]$ (or even any open complex neighborhood of this real segment). Thus no matter how small are the coefficients, the second component will have the specified number of isolated roots.

## Complexification

What about complex valued versions? There is no Rolle theorem for them.

I will describe three possible replacements, Kim’s theorem (1963), nearest in the spirit, and two versions of the argument principle.

Theorem (W. Kim)
Assume that an analytic LODE

$u^{(n)}+a_1(z)u^{(n-1)}+\cdots+a_{n-1}(z)u'+a_n(z)u=0,\qquad z\in D\subseteq\mathbb C$

is defined in a convex compact subset $D$ of diameter $\ell$ and the condition (*) holds. Then this equation is disconjugate in $D$: any solution has at most $n-1$ isolated roots.

This result follows from the interpolation inequality of the following type: if $u(z)$ is a function holomorphic in $D$ and has $n$ isolated roots there, then $\|u\|_D\leqslant \frac{\ell^n}{n!}\|u^{(n)}\|$ (the maximum modulus norm is assumed).

Consider the equation $(\dag)$ on the real interval but with complex-valued coefficients (and solutions). Solutions will be then real parameterized curves $u:[0,\ell]\to\mathbb C$ which only exceptionally rarely have roots. Instead of counting roots, one can measure their rotation around the origin $0\in\mathbb C$, which is defined as $R(u)=|\mathrm{Arg}~u(\ell)-\mathrm{Arg}~u(0)|$ for any continuous choice of the argument.

Theorem. Assume that

$\displaystyle \sum_{k=1}^n \frac{A_k}{k!}\ell^k<\frac12.$

Then rotation of any nontrivial solution $u$ is explicitly bounded: $R(u)<\pi (n+1)$.

If an analytic LODE with explicitly bounded coefficients is defined, say, on a triangle $D$, then application of this result to the sides of the triangle yields an explicit upper bound for the number of isolated roots of analytic solutions inside the triangle.

Reference

S. Yakovenko, On functions and curves defined by differential equations, §2.


text:
06-15-2014, 03:40 AM (This post was last modified: 06-15-2014 03:46 PM by fabila.)
Post: #1
 fabila Junior Member Posts: 25 Joined: Dec 2013
sorry for my English
How to restart a spreadsheet or any app from a program?
thanks
06-15-2014, 04:23 PM (This post was last modified: 06-15-2014 04:23 PM by Angus.)
Post: #2
 Angus Member Posts: 212 Joined: Feb 2014
You can reset an app by pressing <DEL> in the Apps view. That will delete all variables.
06-15-2014, 04:58 PM
Post: #3
 fabila Junior Member Posts: 25 Joined: Dec 2013

but I think I explained wrong,

I want to reset the application using code, not using the buttons

thanks
06-15-2014, 07:00 PM
Post: #4
 Angus Member Posts: 212 Joined: Feb 2014
It don't know of a dedicated reset commands for apps (but I have still a way too limited Knowledge of the device)
However I would find no reason why you should not be able to purge a list of the needed Spreadsheet variables. Coded as a helper function would help to keep your Code clean.
06-16-2014, 02:52 AM
Post: #5
 Dougggg Member Posts: 64 Joined: Dec 2013
The spreadsheet is fairly easy to reset, it would appear that the spreadsheet data is stored as a list so you can clear it in your program with something like for example:

A1:m500={{}};

other apps might be harder to do that way if they have alot of varibles
06-16-2014, 05:50 AM
Post: #6
 cyrille de brébisson Senior Member Posts: 877 Joined: Dec 2013
Hello,

Sorry, but there is no way to do this programaticaly...

Someone already posted a trick that works for the spreadsheet, but it is not universal...

note that the spreadsheet does NOT store it's content as lists.. But only gives you the illusion that it does.

cyrille
06-16-2014, 03:19 PM
Post: #7
 fabila Junior Member Posts: 25 Joined: Dec 2013
thanks for your ANSWERS, I found this post but the solution is not for the fimware 6030

(02-19-2014 02:39 AM)josephec Wrote:  Dear All,

Inside a program you can use this syntax:

In ROM 5106:
(You can use it from command line too)

BUT in ROM 5447 it DOESN´T WORK,
then I asked to Cyrille and no response,... I have a large program with many methods that mix all results in the spreadsheet ... or the user need to ERASE IT BEFORE RUNNING THE PROG.

PLEASE CYRILLE What is the new SYNTAX ??

best regards!!
joseph
06-16-2014, 05:42 PM
Post: #8
 Dougggg Member Posts: 64 Joined: Dec 2013
I wrote a couple of programs that produce data that I felt that putting the data into a spreedsheat would be good, when synced with the connectivity kit you could copy the data to an excel spreedsheet and that works. I put the code for the calcs in the app. I think the best way to use the new empty spreadsheet app I created was to use it as a template so when I need to use the app I select it an save it with a different name and run it and generate the data.

my calculator still freezes when I use the program editor for a long period so hp still has work to do on the firmware, so maybe they could add a reset app command while there at it
06-16-2014, 06:44 PM
Post: #9
 eried Senior Member Posts: 721 Joined: Dec 2013
In RPN, oftenly when you are writing in a cell the calculator hangs and reboots and you lose all the data. But... it is not programatically :/

My website: erwin.ried.cl
06-16-2014, 06:50 PM
Post: #10
 fabila Junior Member Posts: 25 Joined: Dec 2013
the spreadsheet is a good tool to present the output of an external program, because we can interrelate the output data between themselves very easily
but if I can not reset the application from within the prgrama lose some functionality and it becomes tedious and use the programs.
06-17-2014, 05:30 AM
Post: #11
 cyrille de brébisson Senior Member Posts: 877 Joined: Dec 2013
Hello,

BTW, Cell(0,0,-1):={} would suffise...

Will NOT reset the spereadsheet, what it will do is that it will reset the cell 0,0, which correspond to the cell on the top left of the spreadsheet (the one with the HP logo)...

The original poster wants to reset the whole spreadsheet...
The best that I can think of to do this would be A1:ZZ9999(-1):={}

Cyrille
06-25-2014, 08:32 PM
Post: #12
 Jsather Junior Member Posts: 34 Joined: Apr 2014
(06-17-2014 05:30 AM)cyrille de brébisson Wrote:  The best that I can think of to do this would be A1:ZZ9999(-1):={}

The syntax "A1:ZZ9999(-1):={}" and variations referencing other cell ranges usually generate a "bad argument type" error in my attempts. As far as I'm concerned, fabila's very valid question remains unanswered: How can you clear cells of a spreadsheet from a program or Home screen command line?

Clearing would be the equivalent of selecting cells and pressing backspace. Cleared cells should take up no memory space and should return -1 when queried by Cell(R,C,-1). My own efforts to achieve this have been fruitless.
06-26-2014, 05:52 AM
Post: #13
 cyrille de brébisson Senior Member Posts: 877 Joined: Dec 2013
hello

Quote:The syntax "A1:ZZ9999(-1):={}" and variations referencing other cell ranges usually generate a "bad argument type" error in my attempts

Are you sure? it works here for me?
are you not doing somehting like A1:Z99 ? this one would cause an error as Z has to be lower case...

cyrille
06-26-2014, 02:38 PM
Post: #14
 Jsather Junior Member Posts: 34 Joined: Apr 2014
I was using lower case. I've attached some screen shots to show my environment.

Attached File(s) Thumbnail(s)

06-27-2014, 05:50 AM
Post: #15
 cyrille de brébisson Senior Member Posts: 877 Joined: Dec 2013
hello

(06-26-2014 02:38 PM)Jsather Wrote:  I was using lower case. I've attached some screen shots to show my environment.

how strange, it seems to work here for me...
and you are sure that "test" is a spreadsheet app?

can you try switching to the default spreadsheet app and try again?

cyrille
06-27-2014, 07:37 AM
Post: #16
 Thomas_Sch Senior Member Posts: 306 Joined: Dec 2013
Hello,
I tried "A23:D24:={}", this worked.
Combinations like "A23:D24(-1):={}" provided me with "Error: Bad argument type".

What does "..(-1)" mean?
06-27-2014, 02:26 PM
Post: #17
 fabila Junior Member Posts: 25 Joined: Dec 2013
(06-27-2014 05:50 AM)cyrille de brébisson Wrote:  hello

(06-26-2014 02:38 PM)Jsather Wrote:  I was using lower case. I've attached some screen shots to show my environment.

how strange, it seems to work here for me...
and you are sure that "test" is a spreadsheet app?

can you try switching to the default spreadsheet app and try again?

cyrille

This syntax does not work for me, I get the same error as Jsather

creating a loop reset 2 row and 20 columns, this works, but it is very far that we are looking for and also is very slow,

Code:
             FOR B FROM 2 TO SIZE(DIMENSIONES)+1 DO                 FOR C FROM 1 TO 20 DO                     Spreadsheet.Cell(B,C,-1):={"",−1,−1,−1,−1,32768,32768,1,−1,−1,−1};                 END;             END;
06-27-2014, 05:22 PM
Post: #18
 Jsather Junior Member Posts: 34 Joined: Apr 2014
Cyrille: Same "bad arguments" result with Spreadsheet app. At a more basic level I, was able to execute a1(-1):={} and it did not reset a1. Then I tinkered for a while and found things had changed so that "a1(-1):={}" or just entering "a1(-1)" would generate the "bad arguments" error. I do not know how to get out of this state, even starting with a new spreadsheet. Is there any chance you are working with a special calculator or special version of 6030/6031?

Thomas: (-1) references all the attributes of a cell such as formula, name, number format, etc. as described in the user manual page 209. You have managed to null out the cell formula so that blank is displayed and 0 is returned, but all the attributes are active and taking up memory space. Column 0 and Row 0 formulas will not take effect in the cells you have "reset" unless you manually select them and press backspace.

FYI the manual and "Cell" help screen describing foreground and background color indexes are reversed. Index 6 is foreground color and Index 7 is background (or fill) color.

fabila: You have set the value of the "reset" cells to "" which takes up memory and doesn't allow column 0, row 0 formulas to take effect, same as with the formula Thomas tried.

I know two quick ways to tell if a cell is reset. 1) Cell(R,C,-1) will return -1 rather than a list with 11 entries. 2) Enter a simple formula in row 0 or column 0. For example: Is D4 reset? At D0 enter "=Row". The row number will then be displayed in every cell, D1,D2,... that is reset. If any attribute is active, even if a cell just has a fill color, the row number is not displayed.

I do not mean to be dismissive of any efforts but hope everyone keeps trying and someone succeeds. I would greatly like to know why a1:zz9999(-1):={} works for Cyrille but not all of us.
06-27-2014, 05:44 PM
Post: #19
 Thomas_Sch Senior Member Posts: 306 Joined: Dec 2013


text:
# Past Functional Analysis Seminar

14 February 2012
17:00
to
18:37
Ian Short
Abstract
• Functional Analysis Seminar
31 January 2012
17:00
to
18:23
Charles Batty
Abstract
• Functional Analysis Seminar
17 January 2012
17:00
to
18:47
Simon Chandler-Wilde
Abstract
In this talk I will describe recent work by myself and others (E.B. Davies (KCL), M. Lindner (Chemnitz), S. Roch (Darmstadt)) on the spectrum and essential spectrum of bi-infinite and semi-infinite (not necessarily self-adjoin) tri-diagonal random operators, and the implications of these results for the spectra of associated random matrices, and for the finite section method for infinite tri-diagonal systems. A main tool will be limit operator methods, as described in Chandler-Wilde and Lindner, Memoirs AMS, 2011), supplemented by certain symmetry arguments including a Coburn lemma for random matrices.
• Functional Analysis Seminar
29 November 2011
17:00
to
18:20
Elena Ushakova
Abstract
The talk presents a collection of results about mapping properties of the Hardy-Steklov operator $(Hf)(x)=\int_{a(x)}^{b(x)} f(y) dy$ in weighted Lebesgue spaces on the semi-axis. In particular, the explicit boundedness and compactness criteria for the operator are given and a number of applications are obtained. A part of the results is based on a joint paper with Prof. V.D. Stepanov
• Functional Analysis Seminar
15 November 2011
17:00
to
18:37
Jani Virtanen
Abstract
I will briefly discuss boundedness and compactness of Toeplitz operators on Bergman spaces and then describe their essential spectra for several symbol classes (e.g., the Douglas algebra, VMO and BMO type spaces, matrix-valued symbols). I will also list some open problems related to boundedness, compactness and Fredholmness.
• Functional Analysis Seminar
1 November 2011
17:00
to
18:42
David Applebaum
Abstract
• Functional Analysis Seminar
25 October 2011
17:00
to
18:37
Andras Zsak
Abstract
• Functional Analysis Seminar
13 September 2011
17:00
to
18:39
Jussi Behrndt
Abstract
• Functional Analysis Seminar
7 June 2011
17:00
to
18:39
Alistair Bird
Abstract
• Functional Analysis Seminar
17 May 2011
17:00
to
18:39
Michael Levitin
Abstract
• Functional Analysis Seminar


text:
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# Spatial mapping of band bending in semiconductor devices using in situ quantum sensors

## Abstract

Local variations in the charge distribution at semiconductor interfaces can lead to energy level band bending in the structure’s band diagram. Measuring this band bending is important in semiconductor electronics and quantum technologies, but current methods are typically only surface sensitive and are unable to probe the extent of band bending at a depth within the semiconductor. Here, we show that nitrogen–vacancy centres in diamond can be used as in situ sensors to spatially map band bending in a semiconductor device. These nitrogen–vacancy quantum sensors probe the electric field associated with surface band bending, and we map the electric field at different depths under various surface terminations. Using a two-terminal device based on the conductive two-dimensional hole gas formed at a hydrogen-terminated diamond surface, we also observe an unexpected spatial modulation of the electric field, which is attributed to the interplay between charge injection and photo-ionization effects (from the laser used in the experiments). Our method offers a route to the three-dimensional mapping of band bending in diamond and other semiconductors that host suitable quantum sensors.

## Access options

from\$8.99

All prices are NET prices.

## Data availability

The data underlying the present work are available upon request from the corresponding authors.

## References

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Zhang, Z. & Yates, J. T. Band bending in semiconductors: chemical and physical consequences at surfaces and interfaces. Chem. Rev. 112, 5520–5551 (2012).

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Simon, J., Protasenko, V., Lian, C., Xing, H. & Jena, D. Polarization-induced hole doping in wide-band-gap uniaxial semiconductor heterostructures. Science 327, 60–64 (2010).

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Weber, J. R. et al. Quantum computing with defects. Proc. Natl Acad. Sci. USA 107, 8513–8518 (2010).

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Kaviani, M. et al. Proper surface termination for luminescent near-surface NV centers in diamond. Nano. Lett. 14, 4772–4777 (2014).

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Usman, M. et al. Spatial metrology of dopants in silicon with exact lattice site precision. Nat. Nanotech. 11, 763–768 (2016).

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Butler, C. J. et al. Mapping polarization induced surface band bending on the Rashba semiconductor BiTeI. Nat. Commun. 5, 4066 (2014).

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Degen, C. L., Reinhard, F. & Cappellaro, P. Quantum sensing. Rev. Mod. Phys. 89, 035002 (2017).

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Schirhagl, R., Chang, K., Loretz, M. & Degen, C. L. Nitrogen–vacancy centers in diamond: nanoscale sensors for physics and biology. Annu. Rev. Phys. Chem. 65, 83–105 (2014).

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Rondin, L. et al. Magnetometry with nitrogen–vacancy defects in diamond. Rep. Prog. Phys. 77, 056503 (2014).

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Casola, F., Van Der Sar, T. & Yacoby, A. Probing condensed matter physics with magnetometry based on nitrogen–vacancy centres in diamond. Nat. Rev. Mater. 3, 17088 (2018).

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Tetienne, J.-P. et al. Nanoscale imaging and control of domain-wall hopping with a nitrogen–vacancy center microscope. Science 344, 1366–1369 (2014).

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Du, C. et al. Control and local measurement of the spin chemical potential in a magnetic insulator. Science 357, 195–198 (2017).

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Kolkowitz, S. et al. Probing Johnson noise and ballistic transport in normal metals with a single-spin qubit. Science 347, 1129 (2015).

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Tetienne, J.-P. et al. Quantum imaging of current flow in graphene. Sci. Adv. 3, e1602429 (2017).

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Doherty, M. W. et al. The nitrogen–vacancy colour centre in diamond. Phys. Rep. 528, 1–45 (2013).

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## Acknowledgements

We thank M. Barson, D. Simpson and L. Hall for useful discussions. We acknowledge support from the Australian Research Council (grants CE110001027, DE170100129, FL130100119, DP170102735). J.-P.T acknowledges support from the University of Melbourne through an Early Career Researcher Grant. D.A.B, A.T, S.E.L and C.T.-K.L are supported by an Australian Government Research Training Program Scholarship. This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF).

## Author information

Authors

### Contributions

NV measurements and analysis were performed by D.A.B and J.-P.T, with inputs from M.W.D. The devices were fabricated by N.D and D.A.B, H-terminated by A.T and A.S, and electrically characterized by C.T-K.L and B.C.J. The band bending model was developed by N.D with inputs from D.A.B, J.-P.T, A.S and L.C.L.H. All authors contributed to interpreting the data and writing the manuscript.

### Corresponding authors

Correspondence to L. C. L. Hollenberg or J.-P. Tetienne.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Supplementary information

### Supplementary Information

Supplementary Tables 1 and 2, Supplementary Figures 1–15, Supplementary Methods 1 and 2, and Supplementary Data 1–3

## Rights and permissions

Reprints and Permissions

Broadway, D.A., Dontschuk, N., Tsai, A. et al. Spatial mapping of band bending in semiconductor devices using in situ quantum sensors. Nat Electron 1, 502–507 (2018). https://doi.org/10.1038/s41928-018-0130-0

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• ### Nanoscale electric-field imaging based on a quantum sensor and its charge-state control under ambient condition

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• , Wentian Zheng
• , Xianzhe Zeng
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• , Rainer Stöhr
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• , Sen Yang
• , Jörg Wrachtrup
•  & Ying Jiang

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• ### Single-Photon Sources Based on Novel Color Centers in Silicon Carbide P–I–N Diodes: Combining Theory and Experiment

• Igor A. Khramtsov
•  & Dmitry Yu. Fedyanin

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• ### In-situ measurements of fabrication induced strain in diamond photonic-structures using intrinsic colour centres

• Sebastian Knauer
•  & John G. Rarity

npj Quantum Information (2020)

• ### Coherent electrical readout of defect spins in silicon carbide by photo-ionization at ambient conditions

• Matthias Niethammer
• , Matthias Widmann
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• ### Coulomb-driven single defect engineering for scalable qubits and spin sensors in diamond

• Tobias Lühmann
• , Roger John
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Nature Communications (2019)


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## How to Calculate and Solve for Normally Occupied Positions | Ceramics

The image above represents normally occupied positions.

To compute for normally occupied positions, four essential parameters are needed and these parameters are Avogadro’s number (NA), Density (ρ), Atomic weight (AK) and Atomic weights (AG).

The formula for calculating normally occupied positions:

N = NAρ / (AK + AG)

N = Normally Occupied Positions
AK, AG = Atomic Weights
ρ = Density

Let’s solve an example;
Find the normally occupied positions when the avogadro’s number is 6.022e+23, atomic weight is 12, atomic weight is 16 and the density is 10.

This implies that;

NA = Avogadro’s Number = 6.022e+23
AK, AG = Atomic Weights = 12, 16
ρ = Density = 10

N = NAρ / (AK + AG)
N = (6.022e+23)(10)/(12 + 16)
N = (6.022e+24)/(28)
N = 2.15e+23

Therefore, the normally occupied positions is 2.15e+23.

Calculating the Density when the Normally Occupied Positions, the Avogadro’s Number, the Atomic Weights is Given.

ρ = N (AK + AG) / NA

Where;

ρ = Density
N = Normally Occupied Positions
AK, AG = Atomic Weights

Let’s solve an example;
Given that normally occupied positions is 20, the avogadro’s number is 6.022e+23, atomic weights is 18, 6.

This implies that;

N = Normally Occupied Positions =20
NA = Avogadro’s Number = 6.022e+23
AK, AG = Atomic Weights = 18, 6

ρ = N (AK + AG) / NA
ρ = 20 (18 + 6) / 6.022e+23
ρ = 20 (24) / 6.022e+23
ρ = 480 / 6.022e+23
ρ = 7.970e-22

Therefore, the density is 7.970e-22.


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Home How to load partial razor page into razor view

# How to load partial razor page into razor view

user2188
1#
user2188 Published in June 22, 2018, 11:30 am
 I am trying to replace my view components with razor pages but it seems that it's not possible to load a partial razor page because a model is expected to be passed yet it is my understanding that the model for a razor page should be declared in the OnGetAsync method. Here is my code... Razor Page @page "{id:int}" @model _BackgroundModel
Name:
 Razor Page Code-Behind public class _BackgroundModel : PageModel { private readonly IDataClient _dataClient; public _BackgroundModel(IDataClient dataClient) { _dataClient = dataClient; } [BindProperty] public BackgroundDataModel Background { get; set; } public async Task OnGetAsync(int id) { Background = await _dataClient.GetBackground(id); } public async Task OnPostAsync() { if (ModelState.IsValid) { await _dataClient.PostBackground(Background); } } }  Razor View
@await Html.PartialAsync("/Pages/Client/_Background.cshtml", new { id = 1 })
 Page Load Error InvalidOperationException: The model item passed into the ViewDataDictionary is of type '<>f__AnonymousType0`1[System.Int32]', but this ViewDataDictionary instance requires a model item of type 'WebApp.Pages.Client._BackgroundModel' In this example (as per MS recommended approach in their docs) the model is set inside the OnGetAsync method which should be run when the page is requested. I have also tried @await Html.RenderPartialAsync("/Pages/Client/_Background.cshtml", new { id = 1 }) but the same error result. How can I load the razor page into my existing view?
 You need to login account before you can post.
Processed in 0.389435 second(s) , Gzip On .


text:
# What is the difference between a mixture model and a multimodal distribution?

A distribution that is a "Mixture model" has a very similar definition as a "multimodal" distribution.

Wikipedia Says:

a multimodal distribution is a continuous probability distribution with two or more mode

Now to compare:

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.

What is the difference between the two? They sound like they are the same thing!

To complement @matt's answer, you can also consider the beta distribution with $\alpha = .5$ and $\beta = .5$. It is illustrated by the red line in the figure below (copied from Wikipedia). As you can see, it is multimodal (viz., bimodal), but it isn't a mixture distribution:


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### How to write Math equation On Blogger

The Math Equations in this post are generated by MathJax, it's so beautifully rendered and simple to configure, without installing any plugins or special fonts. Just one line in the template header, and type your TeX code  within double dollar signs.

When $$a \ne 0$$, there are two solutions to $$ax^2 + bx + c = 0$$ and they are $$x = {-b \pm \sqrt{b^2-4ac} \over 2a}.$$


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Calculus with Applications (10th Edition)

$$f(x)=e^{x},$$ To find the second derivative of find the first derivative, and then take its derivative. $$f^{\prime}(x)=e^{x},$$ $$f^{\prime\prime}(x)=e^{x},$$ $$f^{\prime\prime\prime}(x)=e^{x},$$ and so on $$f^{(n)}(x)=e^{x},$$
$$f(x)=e^{x},$$ To find the second derivative of find the first derivative, and then take its derivative. $$f^{\prime}(x)=e^{x},$$ $$f^{\prime\prime}(x)=e^{x},$$ $$f^{\prime\prime\prime}(x)=e^{x},$$ and so on $$f^{(n)}(x)=e^{x},$$


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# Tag Info

3

The given dihydroborinine is indeed anti-aromatic if is it fully conjugated and planar, but counting pi electrons in a conjugated ring is not as simple as it seems when you have boron in the ring. If the boron is not joined to a ligand, as in (mono)boronine or unhydrogenated diborinine, it could pass its electron deficiency into the sigma bonding framework ...

1

The energy is the potential energy of all electrons in the molecule. Sometimes the term also includes the potential energy of the nuclei but in most cases it doesn't. As reference or zero energy you can imagine the case where the electrons are non interacting or infinitely far apart from each other. In this sense it tells you the energy required to "...

2

When electrons are removed from an atom, those in the outermost orbital is removed first. The p orbitals are most away from the nucleus, then the s orbitals, and finally the d orbitals. In fact, the closeness of d orbitals to the nucleus accounts for higher energy of the 4d orbitals than the 5s orbital. Because electrons are closer, there is a stronger ...

3

The NIST Atomic Spectra Database,[1] which is as authoritative as it gets, shows that $\ce{5d^9 6s^1}$ is the correct ground-state electronic configuration for platinum. Various inorganic textbooks also say the same. Wikipedia cites Meissler and Tarr for this table: https://en.wikipedia.org/wiki/Electron_configuration#Other_exceptions_to_Madelung's_rule ...

Top 50 recent answers are included


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# Finding overall p-value for GLS model

Does anyone know how I can find/calculate an overall p-value for a GLS multiple regression model (made with nlme)?

-
Welcome to SO. Your question is actually better suited for crossvalidated.com . Probably one of the mods will move it there soon enough. Next to that, I'd advise you to add some toy dataset and a reproducible example so people can play around a bit as well. It shows you did some effort, and people will be more keen to help you. On how to make a reproducible example, see this question – Joris Meys Aug 4 '11 at 15:36

## migrated from stackoverflow.comAug 4 '11 at 15:58

The way one is expected to do this in gls() is to use a likelihood ratio test between two models via the anova() methods for "gls" objects. This is a general way of comparing two nested models with one another, but because of the way these models are fitted (REML estimates are used by default as one is generally fitting variance parameters for the correlation or weights arguments, but to compare models with different fixed effects we need ML estimates), you can't really get the significance of the overall model - it depends what you are wanting to test - the fixed effects or the variance parameters for the variance-covariance matrix?

Here is an example using data from the nlme package.

require(nlme)
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
correlation = corAR1(form = ~ 1 | Mare))

There are two things to test in that model:

1. The fixed effects terms
2. The need for the correlation term

Lets assume the interest is in the fixed effects terms. We need an appropriate Null model that does not include these terms but does include the intercept and the same correlation estimate from the candidate model above.

fm0 <- gls(follicles ~ 1, Ovary,
correlation = corAR1(value = 0.7532079, form = ~ 1 | Mare,
fixed = TRUE))

(I got the value by looking at intervals(fm1). Both these models have been fitted using REML, which is not useful in testing models with different fixed effects, however, REML gives unbiased estimates of variance parameters (for the corAR1() bit, hence we used that first.)

To check if the fixed effect terms are significant, we use anova() on the updated models that are fitted using ML:

fm0.ml <- update(fm0, . ~ ., method = "ML")
fm1.ml <- update(fm1, . ~ ., method = "ML")
anova(fm0.ml, fm1.ml)

which gives

> anova(fm0.ml, fm1.ml)
Model df      AIC      BIC    logLik   Test  L.Ratio p-value
fm0.ml     1  2 1588.169 1595.629 -792.0844
fm1.ml     2  5 1574.387 1593.037 -782.1934 1 vs 2 19.78195   2e-04

As I used the same estimated correlation structure in both models (based on REML estimate from fm1) the difference between the two is isolated to the two fixed effects. However, the difference in degrees of freedom is not right here; because I fixed the value of the AR(1) parameter, the software doesn't treat that as a degree of freedom used, hence it thinks that the models differ by 3 instead of 2 degrees of freedom.

To address this we could just compute the p-value for the L.Ratio shown using the correct d.f.:

> pchisq(19.78195, 2, lower.tail = FALSE)
[1] 5.062956e-05

Or we could update fm1.ml to use a fixed estimate of the AR(1) parameter and redo the LRT:

fm1.ml <- update(fm1.ml, . ~ .,
correlation = corAR1(value = 0.7532079, form = ~ 1 | Mare,
fixed = TRUE))

> anova(fm0.ml, fm1.ml)
Model df      AIC      BIC    logLik   Test  L.Ratio p-value
fm0.ml     1  2 1588.169 1595.629 -792.0844
fm1.ml     2  4 1572.449 1587.369 -782.2243 1 vs 2 19.72008   1e-04

The reason for the difference in p-values between the two is the slightly lower L.Ratio for fm1.ml when fixing the AR(1) parameter.

If you want to check the need for the correlation structure, we need to use the REML estimated models:

fm2 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary)
anova(fm2, fm1)

> anova(fm2, fm1)
Model df      AIC      BIC    logLik   Test  L.Ratio p-value
fm2     1  4 1804.868 1819.749 -898.4340
fm1     2  5 1571.455 1590.056 -780.7273 1 vs 2 235.4135  <.0001

-
 Gavin - very nice explanation and examples, with code. At first I was confused by the need to fix the correlation parameter $\rho$, but then I thought of an analogy with getting a likelihood ratio test for one of the regression parameters in the OLS univariate linear model case. Do you fix $\sigma^2$ when comparing the two models, or do you let if float? [I'm ignoring the fact that we have an exact $F$ test in this case.] – Frank Harrell Aug 5 '11 at 12:50 @Frank Thanks. My first stab allowed $\rho$ to be re-estimated, but then I realised that the LRT would be comparing models with not just different fixed effects but different correlation structures. $\sigma^2$ was re-estimated. I'm not aware of a canned way with gls() to allow fixing that. I came at this from a practical point of view - re your comment in [...], what is the exact $F$? Showing my limited statistical knowledge now I'm afraid. – Gavin Simpson Aug 5 '11 at 13:06 You're doing fine - this is a tricky issue that I wish I had figured out years ago. Someone can probably point us to a good reference about what parameters to "freeze" when doing likelihood ratio tests, in general. – Frank Harrell Aug 5 '11 at 18:59


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# Thread: Calc 1 Semester Project

1. ## Calc 1 Semester Project

I am having issues with these two math problems. I have to do the math along with write the math into words pretty much stating how I did this. I am terrible at turning math into words. Can anyone help me with this problem?

1. Airline Revenue. An airline company flies about 25,000 passengers from JFK NY to Canada monthly at the price of $530 per round-trip ticket. Current marketing research predicts that for each$5 increase in ticket price the number of passengers will drop by 150. Find the equation which gives the number of passengers in terms of the ticket price. Find the revenue equation. Find the price for which the revenue is maximized. Create a strong argument against having price set at $729.99. (You might want to make a table and present few values assigning one column to the price of the ticket, then the number of passengers, and finally revenue.) I have the math done for this but I have no clue on what to write to explain what I did...it needs to be pretty detailed. IF anyone can help please replyy! 2. ## Re: Calc 1 Semester Project At what price is the revenue maximized? Is it$730, or some other value?

At $729.99, if I punched in the number correctly, the revenue would be$13,888,810.00. At $530, it would be (or is, since that's the current price)$13,250,000.00, so I guess the company would gain revenue at the higher price. But is that maximum?

3. ## Re: Calc 1 Semester Project

i believe it is the maximum.


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# How to add frequency constraint to filter design example?

Consider the Chebychev filter example from the homepage: http://cvxr.com/cvx/examples/filter_design/html/fir_chebychev_design.html#source

How to add a frequency constraint such that the filter only gets optimized within w_pass and w_stop?

The relevant lines are

% rule-of-thumb frequency discretization (Cheney's Approx. Theory book)
m = 15*n;
w = linspace(0,pi,m)'; % omega

Just modify the linspace command to cover the frequencies of interest. The units are expressed in radians; given a frequency f and a sample rate F_s, the corresponding radian frequency is 2\pi f/F_s.

Make sure you sample your frequency range finely, as we’re doing here.