The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

K. C. Fung - One of the best experts on this subject based on the ideXlab platform.

  • Some International Policy Issues for China
    Economic and Policy Review, 2015
    Co-Authors: K. C. Fung, Nathalie Aminian, Alicia García-herrero
    Abstract:

    In this paper we provide a simple model of an emerging market like China. We attempt to capture two characteristics of an emerging market: The simultaneous presence of market failures and government failures. In this model, we study the national welfare properties of four types of “governance” or “institutions”: A Benevolent Dictator, Extreme Economic Liberalism, Extreme Economic Nationalism and Factional Fighting with groups pressuring the government. We also discuss a case of the Realistic Dictator, who may switch from adopting liberal policies to nationalistic policies as the domestic market gets larger.

  • The Political Economy of Strategic Trade Policies
    Review of International Economics, 2009
    Co-Authors: K. C. Fung, Chelsea C. Lin, Ray-yun Chang
    Abstract:

    This paper applies Grossman and Helpman's (1994) common agency model to investigate the formation of strategic export subsidy and strategic import tariff under both Cournot competition and Bertrand competition. The results of this paper indicate that even with political pressure, the Grossman–Helpman politically-determined export policy is identical to the rent-shifting export policy, which is export subsidy (export tax) in Cournot (Bertrand) competition. The politically-determined import tariff will be higher than the optimal level. This paper highlights the possibility that lobbying can restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator.

  • Political Economy of Strategic Trade Policy: Menu-Auctions with Imperfect Competition ∗
    2000
    Co-Authors: K. C. Fung, Chelsea C. Lin
    Abstract:

    This paper applies the Dixit-Grossman-Helpman (1994, 1997) common agency model to investigate the formation of strategic export subsidy and strategic import tariff. The results of this paper indicate that even with political pressure, the Dixit-Grossman-Helpman politically-determined export subsidy is identical to the Brander-Spencer rent-shifting export subsidy. The politically-determined import tariff will be higher than the optimal level. This paper highlights the possibility that lobbying can restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator. Joint lobbying is also considered and the results indicate that joint lobbying on export subsidy improves domestic welfare due to the elimination of the resource wasted in lobbying

  • Political Economy of Strategic Trade Policy: Menu-Auctions with Imperfect Competition
    SSRN Electronic Journal, 2000
    Co-Authors: K. C. Fung
    Abstract:

    This paper applies the Dixit-Grossman-Helpman (1994, 1997) common agency model to investigate the formation of strategic export subsidy and strategic import tariff. The results of this paper indicate that even with political pressure, the Dixit-Grossman-Helpman politically-determined ex-port subsidy is identical to the Brander-Spencer rent-shifting export subsidy. The politically-determined import tariff will be higher than the optimal level. This paper highlights the possibility that lobbying can restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator. Joint lobbying is also considered and the results indicate that joint lobbying on export subsidy improves domestic welfare due to the elimination of the resource wasted in lobbying.

  • Political Economy of Strategic Trade Policy: Menu-Auctions with Imperfect Competition ∗
    2000
    Co-Authors: K. C. Fung, Chelsea C. Lin
    Abstract:

    This paper applies Dixit-Grossman-Helpman’s (1994, 1997) common agency model to investigate the formation of export subsidy and import tariff in the presence of interest groups and imperfect competition. The results of this paper indicate that even with political pressure, the Dixit-Grossman-Helpman politically determined export subsidy is identical to the Brander-Spencer rent-shifting export subsidy. The politically determined import tariff will be higher than the efficient level. This paper highlights the possibility that the presence of lobbying may restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator. Joint lobbying is also considered and the results indicate that joint lobbying on export subsidy improves domestic welfare due to the elimination of the resource wasted in lobbying

Chelsea C. Lin - One of the best experts on this subject based on the ideXlab platform.

  • The Political Economy of Strategic Trade Policies
    Review of International Economics, 2009
    Co-Authors: K. C. Fung, Chelsea C. Lin, Ray-yun Chang
    Abstract:

    This paper applies Grossman and Helpman's (1994) common agency model to investigate the formation of strategic export subsidy and strategic import tariff under both Cournot competition and Bertrand competition. The results of this paper indicate that even with political pressure, the Grossman–Helpman politically-determined export policy is identical to the rent-shifting export policy, which is export subsidy (export tax) in Cournot (Bertrand) competition. The politically-determined import tariff will be higher than the optimal level. This paper highlights the possibility that lobbying can restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator.

  • Political Economy of Strategic Trade Policy: Menu-Auctions with Imperfect Competition ∗
    2000
    Co-Authors: K. C. Fung, Chelsea C. Lin
    Abstract:

    This paper applies the Dixit-Grossman-Helpman (1994, 1997) common agency model to investigate the formation of strategic export subsidy and strategic import tariff. The results of this paper indicate that even with political pressure, the Dixit-Grossman-Helpman politically-determined export subsidy is identical to the Brander-Spencer rent-shifting export subsidy. The politically-determined import tariff will be higher than the optimal level. This paper highlights the possibility that lobbying can restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator. Joint lobbying is also considered and the results indicate that joint lobbying on export subsidy improves domestic welfare due to the elimination of the resource wasted in lobbying

  • Political Economy of Strategic Trade Policy: Menu-Auctions with Imperfect Competition ∗
    2000
    Co-Authors: K. C. Fung, Chelsea C. Lin
    Abstract:

    This paper applies Dixit-Grossman-Helpman’s (1994, 1997) common agency model to investigate the formation of export subsidy and import tariff in the presence of interest groups and imperfect competition. The results of this paper indicate that even with political pressure, the Dixit-Grossman-Helpman politically determined export subsidy is identical to the Brander-Spencer rent-shifting export subsidy. The politically determined import tariff will be higher than the efficient level. This paper highlights the possibility that the presence of lobbying may restore the level of trade intervention to a more efficient one in the absence of the Benevolent Dictator. Joint lobbying is also considered and the results indicate that joint lobbying on export subsidy improves domestic welfare due to the elimination of the resource wasted in lobbying

Therese J. Mcguire - One of the best experts on this subject based on the ideXlab platform.

  • Proposition 13 and Its Offspring: For Good or for Evil?
    National Tax Journal, 1999
    Co-Authors: Therese J. Mcguire
    Abstract:

    The answer to the question posed in the title depends on which model of local government behavior is operative. If it is the median-voter/Benevolent-Dictator model, then property tax limits can only be for evil. If it is the Leviathan/budget-maximizing- bureaucratic model, then property tax limits have the potential to improve the welfare of local resident voters. From this perspective, I reassess the empirical literature on state-imposed limits on local property taxes and conclude that the evidence can be interpreted as supportive of the notion that the Leviathan model may be operative and thus that property tax limits have the potential to improve welfare.

Gunther G Schulze - One of the best experts on this subject based on the ideXlab platform.

  • Capital exports to rising Eastern Europe: Would voters go for it?
    1992
    Co-Authors: Gunther G Schulze
    Abstract:

    The paper addresses the question whether voters would favor a restriction on capital exports to the East, and if so, to what extent. In a two country framework, it is shown that an unrestricted capital outflow is not optimal, in the traditional sense of a social welfare maximizing Benevolent Dictator. Furthermore, capital export may lead to unemployment and a loss of output in the exporting country if a minimum wage constraint comes into effect. Nevertheless, it may still be optimal to encounter a certain loss in employment and production because of a positive factor terms of trade effect. The preferences of individuals who are differently endowed with 'capital and labor depend on their endowment ratio: E.g., an individual whose capital-labor endowment ratio exceeds the figure for the economy will prefer less-than-optimal curbing of capital outflow. If the policy concerning capital exports is decided upon via majority voting, it is the median voter's optimal policy that is finally adopted. This implies that the restriction on capital exports would presumably be tighter than in the traditional understanding of a Benevolent Dictator.

  • capital exports to rising eastern europe would voters go for it
    1992
    Co-Authors: Gunther G Schulze
    Abstract:

    The paper addresses the question whether voters would favor a restriction on capital exports to the East, and if so, to what extent. In a two country framework, it is shown that an unrestricted capital outflow is not optimal, in the traditional sense of a social welfare maximizing Benevolent Dictator. Furthermore, capital export may lead to unemployment and a loss of output in the exporting country if a minimum wage constraint comes into effect. Nevertheless, it may still be optimal to encounter a certain loss in employment and production because of a positive factor terms of trade effect. The preferences of individuals who are differently endowed with 'capital and labor depend on their endowment ratio: E.g., an individual whose capital-labor endowment ratio exceeds the figure for the economy will prefer less-than-optimal curbing of capital outflow. If the policy concerning capital exports is decided upon via majority voting, it is the median voter's optimal policy that is finally adopted. This implies that the restriction on capital exports would presumably be tighter than in the traditional understanding of a Benevolent Dictator.

Stefan Van Der Walt - One of the best experts on this subject based on the ideXlab platform.

  • scipy/scipy: SciPy 1.0.0
    2017
    Co-Authors: Pauli Virtanen, Ralf Gommers, Evgeni Burovski, Travis E. Oliphant, David Cournapeau, Warren Weckesser, Pearu Peterson, Stefan Van Der Walt
    Abstract:

    We are extremely pleased to announce the release of SciPy 1.0, 16 years after version 0.1 saw the light of day. It has been a long, productive journey to get here, and we anticipate many more exciting new features and releases in the future. Why 1.0 now? A version number should reflect the maturity of a project - and SciPy was a mature and stable library that is heavily used in production settings for a long time already. From that perspective, the 1.0 version number is long overdue. Some key project goals, both technical (e.g. Windows wheels and continuous integration) and organisational (a governance structure, code of conduct and a roadmap), have been achieved recently. Many of us are a bit perfectionist, and therefore are reluctant to call something "1.0" because it may imply that it's "finished" or "we are 100% happy with it". This is normal for many open source projects, however that doesn't make it right. We acknowledge to ourselves that it's not perfect, and there are some dusty corners left (that will probably always be the case). Despite that, SciPy is extremely useful to its users, on average has high quality code and documentation, and gives the stability and backwards compatibility guarantees that a 1.0 label imply. Some history and perspectives 2001: the first SciPy release 2005: transition to NumPy 2007: creation of scikits 2008: scipy.spatial module and first Cython code added 2010: moving to a 6-monthly release cycle 2011: SciPy development moves to GitHub 2011: Python 3 support 2012: adding a sparse graph module and unified optimization interface 2012: removal of scipy.maxentropy 2013: continuous integration with TravisCI 2015: adding Cython interface for BLAS/LAPACK and a benchmark suite 2017: adding a unified C API with scipy.LowLevelCallable; removal of scipy.weave 2017: SciPy 1.0 release Pauli Virtanen is SciPy's Benevolent Dictator For Life (BDFL). He says: Truthfully speaking, we could have released a SciPy 1.0 a long time ago, so I'm happy we do it now at long last. The project has a long history, and during the years it has matured also as a software project. I believe it has well proved its merit to warrant a version number starting with unity. Since its conception 15+ years ago, SciPy has largely been written by and for scientists, to provide a box of basic tools that they need. Over time, the set of people active in its development has undergone some rotation, and we have evolved towards a somewhat more systematic approach to development. Regardless, this underlying drive has stayed the same, and I think it will also continue propelling the project forward in future. This is all good, since not long after 1.0 comes 1.1. Travis Oliphant is one of SciPy's creators. He says: I'm honored to write a note of congratulations to the SciPy developers and the entire SciPy community for the release of SciPy 1.0. This release represents a dream of many that has been patiently pursued by a stalwart group of pioneers for nearly 2 decades. Efforts have been broad and consistent over that time from many hundreds of people. From initial discussions to efforts coding and packaging to documentation efforts to extensive conference and community building, the SciPy effort has been a global phenomenon that it has been a privilege to participate in. The idea of SciPy was already in multiple people's minds in 1997 when I first joined the Python community as a young graduate student who had just fallen in love with the expressibility and extensibility of Python. The internet was just starting to bringing together like-minded mathematicians and scientists in nascent electronically-connected communities. In 1998, there was a concerted discussion on the matrix-SIG, python mailing list with people like Paul Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, David Ascher, and others. This discussion encouraged me in 1998 and 1999 to procrastinate my PhD and spend a lot of time writing extension modules to Python that mostly wrapped battle-tested Fortran and C-code making it available to the Python user. This work attracted the help of others like Robert Kern, Pearu Peterson and Eric Jones who joined their efforts with mine in 2000 so that by 2001, the first SciPy release was ready. This was long before Github simplified collaboration and input from others and the "patch" command and email was how you helped a project improve. Since that time, hundreds of people have spent an enormous amount of time improving the SciPy library and the community surrounding this library has dramatically grown. I stopped being able to participate actively in developing the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen and Ralf Gommers picked up the pace of development supported by dozens of other key contributors such as David Cournapeau, Evgeni Burovski, Josef Perktold, and Warren Weckesser. While I have only been able to admire the development of SciPy from a distance for the past 7 years, I have never lost my love of the project and the concept of community-driven development. I remain driven even now by a desire to help sustain the development of not only the SciPy library but many other affiliated and related open-source projects. I am extremely pleased that SciPy is in the hands of a world-wide community of talented developers who will ensure that SciPy remains an example of how grass-roots, community-driven development can succeed. Fernando Perez offers a wider community perspective: The existence of a nascent Scipy library, and the incredible --if tiny by today's standards-- community surrounding it is what drew me into the scientific Python world while still a physics graduate student in 2001. Today, I am awed when I see these tools power everything from high school education to the research that led to the 2017 Nobel Prize in physics. Don't be fooled by the 1.0 number: this project is a mature cornerstone of the modern scientific computing ecosystem. I am grateful for the many who have made it possible, and hope to be able to contribute again to it in the future. My sincere congratulations to the whole team! Highlights of this release Some of the highlights of this release are: Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. A set of new ODE solvers and a unified interface to them (scipy.integrate.solve_ivp). Two new trust region optimizers and a new linear programming method, with improved performance compared to what scipy.optimize offered previously. Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. Upgrading and compatibility There have been a number of deprecations and API changes in this release, which are documented below. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). This release requires Python 2.7 or >=3.4 and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features scipy.cluster improvements scipy.cluster.hierarchy.optimal_leaf_ordering, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. scipy.fftpack improvements N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as dctn, idctn, dstn and idstn. scipy.integrate improvements A set of new ODE solvers have been added to scipy.integrate. The convenience function scipy.integrate.solve_ivp allows uniform access to all solvers. The individual solvers (RK23, RK45, Radau, BDF and LSODA) can also be used directly. scipy.linalg improvements The BLAS wrappers in scipy.linalg.blas have been completed. Added functions are *gbmv, *hbmv, *hpmv, *hpr, *hpr2, *spmv, *spr, *tbmv, *tbsv, *tpmv, *tpsv, *trsm, *trsv, *sbmv, *spr2, Wrappers for the LAPACK functions *gels, *stev, *sytrd, *hetrd, *sytf2, *hetrf, *sytrf, *sycon, *hecon, *gglse, *stebz, *stemr, *sterf, and *stein have been added. The function scipy.linalg.subspace_angles has been added to compute the subspace angles between two matrices. The function scipy.linalg.clarkson_woodruff_transform has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions scipy.linalg.eigh_tridiagonal and scipy.linalg.eigvalsh_tridiagonal, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. scipy.ndimage improvements Support for homogeneous coordinate transforms has been added to scipy.ndimage.affine_transform. The ndimage C code underwent a significant refactoring, and is now a lot easier to understand and maintain. scipy.optimize improvements The methods trust-region-exact and trust-krylov have been added to the function scipy.optimize.minimize. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. scipy.optimize.linprog gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. scipy.signal improvements An argument fs (sampling frequency) was added to the following functions: firwin, firwin2, firls, and remez. This makes these functions consistent with many other functions in scipy.signal in which the sampling frequency can be specified. scipy.signal.freqz has been sped up significantly for FIR filters. scipy.sparse improvements Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The tocsr method of COO matrices is now several times faster. The diagonal method of sparse matrices now takes a parameter, indicating which diagonal to return. scipy.sparse.linalg improvements A new iterative solver for large-scale nonsymmetric sparse linear systems, scipy.sparse.linalg.gcrotmk, was added. It implements GCROT(m,k), a flexible variant of GCROT. scipy.sparse.linalg.lsmr now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. scipy.spatial improvements Many distance metrics in scipy.spatial.distance gained support for weights. The signatures of scipy.spatial.distance.pdist and scipy.spatial.distance.cdist were changed to *args, **kwargs in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional out parameter was added to pdist and cdist allowing the user to specify where the resulting distance matrix is to be stored scipy.stats improvements The methods cdf and logcdf were added to scipy.stats.multivariate_normal, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely scipy.stats.wasserstein_distance for the first Wasserstein distance and scipy.stats.energy_distance for the energy distance. Deprecated features The following functions in scipy.misc are deprecated: bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow and toimage. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. scipy.interpolate.interpolate_wrapper and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The fillvalue of scipy.signal.convolve2d will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. scipy.spatial.distance.matching is deprecated. It is an alias of scipy.spatial.distance.hamming, which should be used instead. Implementation of scipy.spatial.distance.wminkowski was based on a wrong interpretation of the metric definition. In scipy 1.0 it has been just deprecated in the documentation to keep retro-compatibility but is recommended to use the new version of scipy.spatial.distance.minkowski that implements the correct behaviour. Positional arguments of scipy.spatial.distance.pdist and scipy.spatial.distance.cdist should be replaced with their keyword version. Backwards incompatible changes The following deprecated functions have been removed from scipy.stats: betai, chisqprob, f_value, histogram, histogram2, pdf_fromgamma, signaltonoise, square_of_sums, ss and threshold. The following deprecated functions have been removed from scipy.stats.mstats: betai, f_value_wilks_lambda, signaltonoise and threshold. The deprecated a and reta keywords have been removed from scipy.stats.shapiro. The deprecated functions sparse.csgraph.cs_graph_components and sparse.linalg.symeig have been removed from scipy.sparse. The following deprecated keywords have been removed in scipy.sparse.linalg: drop_tol from splu, and xtype from bicg, bicgstab, cg, cgs, gmres, qmr and minres. The deprecated functions expm2 and expm3 have been removed from scipy.linalg. The deprecated keyword q was removed from scipy.linalg.expm. And the deprecated submodule linalg.calc_lwork was removed. The deprecated functions C2K, K2C, F2C, C2F, F2K and K2F have been removed from scipy.constants. The deprecated ppform class was removed from scipy.interpolate. The deprecated keyword iprint was removed from scipy.optimize.fmin_cobyla. The default value for the zero_phase keyword of scipy.signal.decimate has been changed to True. The kmeans and kmeans2 functions in scipy.cluster.vq changed the method used for random initialization, so using a fixed random seed will not necessarily produce the same results as in previous versions. scipy.special.gammaln does not accept complex arguments anymore. The deprecated functions sph_jn, sph_yn, sph_jnyn, sph_in, sph_kn, and sph_inkn have been removed. Users should instead use the functions spherical_jn, spherical_yn, spherical_in, and spherical_kn. Be aware that the new functions have different signatures. The cross-class properties of scipy.signal.lti systems have been removed. The following properties/setters have been removed: Name - (accessing/setting has been removed) - (setting has been removed) StateSpace - (num, den, gain) - (zeros, poles) TransferFunction (A, B, C, D, gain) - (zeros, poles) ZerosPolesGain (A, B, C, D, num, den) - () signal.freqz(b, a) with b or a >1-D raises a ValueError. This was a corner case for which it was unclear that the behavior was well-defined. The method var of scipy.stats.dirichlet now returns a scalar rather than an ndarray when the length of alpha is 1. Other changes SciPy now has a formal governance structure. It consists of a BDFL (Pauli Virtanen) and a Steering Committee. See the governance document _ for details. It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous integration has been set up for this build configuration on Appveyor, building against OpenBLAS. Continuous integration for OS X has been set up on TravisCI. The SciPy test suite has been migrated from nose to pytest. scipy/_distributor_init.py was added to allow redistributors of SciPy to add custom code that needs to run when importing SciPy (e.g. checks for hardware, DLL search paths, etc.). Support for PEP 518 (specifying build system requirements) was added - see pyproject.toml in the root of the SciPy repository. In order to have consistent function names, the function scipy.linalg.solve_lyapunov is renamed to scipy.linalg.solve_continuous_lyapunov. The old name is kept for backwards-compatibility. Authors @arcady + @xoviat + Anton Akhmerov Dominic Antonacci + Alessandro Pietro Bardelli Ved Basu + Michael James Bedford + Ray Bell + Juan M. Bello-Rivas + Sebastian Berg Felix Berkenkamp Jyotirmoy Bhattacharya + Matthew Brett Jonathan Bright Bruno Jiménez + Evgeni Burovski Patrick Callier Mark Campanelli + CJ Carey Robert Cimrman Adam Cox + Michael Danilov + David Haberthür + Andras Deak + Philip DeBoer Anne-Sylvie Deutsch Cathy Douglass + Dominic Else + Guo Fei + Roman Feldbauer + Yu Feng Jaime Fernandez del Rio Orestis Floros + David Freese + Adam Geitgey + James Gerity + Dezmond Goff + Christoph Gohlke Ralf Gommers Dirk Gorissen + Matt Haberland + David Hagen + Charles Harris Lam Yuen Hei + Jean Helie + Gaute Hope + Guillaume Horel + Franziska Horn + Yevhenii Hyzyla + Vladislav Iakovlev + Marvin Kastner + Mher Kazandjian Thomas Keck Adam Kurkiewicz + Ronan Lamy + J.L. Lanfranchi + Eric Larson Denis Laxalde Gregory R. Lee Felix Lenders + Evan Limanto Julian Lukwata + François Magimel Syrtis Major + Charles Masson + Nikolay Mayorov Tobias Megies Markus Meister + Roman Mirochnik + Jordi Montes + Nathan Musoke + Andrew Nelson M.J. Nichol Juan Nunez-Iglesias Arno Onken + Nick Papior + Dima Pasechnik + Ashwin Pathak + Oleksandr Pavlyk + Stefan Peterson Ilhan Polat Andrey Portnoy + Ravi Kumar Prasad + Aman Pratik Eric Quintero Vedant Rathore + Tyler Reddy Joscha Reimer Philipp Rentzsch + Antonio Horta Ribeiro Ned Richards + Kevin Rose + Benoit Rostykus + Matt Ruffalo + Eli Sadoff + Pim Schellart Nico Schlömer + Klaus Sembritzki + Nikolay Shebanov + Jonathan Tammo Siebert Scott Sievert Max Silbiger + Mandeep Singh + Michael Stewart + Jonathan Sutton + Deep Tavker + Martin Thoma James Tocknell + Aleksandar Trifunovic + Paul van Mulbregt + Jacob Vanderplas Aditya Vijaykumar Pauli Virtanen James Webber Warren Weckesser Eric Wieser + Josh Wilson Zhiqing Xiao + Evgeny Zhurko Nikolay Zinov + Zé Vinícius + A total of 121 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete