|
Brooklyn | |||||||
| FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | METHOD | DETAIL: FIELD | METHOD |
@SuppressWarnings("rawtypes") public interface LocationRegistry
The registry of the sorts of locations that brooklyn knows about. Given a {@LocationDefinition} or a java.lang.String representation of a spec, this can be used to create a Location instance.
| Method Summary | |
|---|---|
boolean
|
canResolve(java.lang.String spec)
Returns true/false depending whether spec seems like a valid location |
LocationDefinition
|
getDefinedLocation(java.lang.String id)
|
LocationDefinition
|
getDefinedLocationByName(java.lang.String name)
|
java.util.Map
|
getDefinedLocations()
|
java.util.Map
|
getProperties()
|
void
|
removeDefinedLocation(java.lang.String id)
removes the defined location from the registry (applications running there are unaffected) |
Location
|
resolve(LocationDefinition l)
returns fully populated (config etc) location from the given definition |
Location
|
resolve(java.lang.String spec)
See resolve(String, Map) (with no options) |
Location
|
resolve(java.lang.String spec, java.util.Map locationFlags)
Returns a location created from the given spec, which might correspond to a definition, or created on-the-fly. |
java.util.List
|
resolve(java.lang.Iterable spec)
As resolve(String) but works with a collection of location specs. |
void
|
updateDefinedLocation(LocationDefinition l)
adds or updates the given defined location |
| Method Detail |
|---|
public boolean canResolve(java.lang.String spec)
public LocationDefinition getDefinedLocation(java.lang.String id)
public LocationDefinition getDefinedLocationByName(java.lang.String name)
public java.util.Map getDefinedLocations()
public java.util.Map getProperties()
public void removeDefinedLocation(java.lang.String id)
public Location resolve(LocationDefinition l)
public Location resolve(java.lang.String spec)
public Location resolve(java.lang.String spec, java.util.Map locationFlags)
public java.util.List resolve(java.lang.Iterable spec)
Usually given a collection of string specs. Also supports comma-separated lists as a single spec.
For legacy compatibility this also accepts nested lists, but that is deprecated (and triggers a warning).
public void updateDefinedLocation(LocationDefinition l)
Brooklyn Multi-Cloud Application Management Platform
brooklyncentral.github.com. Apache License. © 2012.


Abstract. We consider the verification of lock-free data structures that manually manage their memory with the help of a safe memory reclamation (SMR) algorithm. Our first contribution is a type system that checks whether a program properly manages its memory. If the type check succeeds, it is safe to ignore the SMR algorithm and consider the program under garbage collection. Intuitively, our types track the protection of pointers as guaranteed by the SMR algorithm. There are two design decisions. The type system does not track any shape information, which makes it extremely lightweight. Instead, we rely on invariant annotations that postulate a protection by the SMR. To this end, we introduce angels, ghost variables with an angelic semantics. Moreover, the SMR algorithm is not hard-coded but a parameter of the type system definition. To achieve this, we rely on a recent specification language for SMR algorithms. Our second contribution is to automate the type inference and the invariant check. For the type inference, we show a quadratic-time algorithm. For the invariant check, we give a source-to-source translation that links our programs to off-the-shelf verification tools. It compiles away the angelic semantics. This allows us to infer appropriate annotations automatically in a guess-and-check manner. To demonstrate the effectiveness of our type-based verification approach, we check linearizability for various list and set implementations from the literature with both hazard pointers and epoch-based memory reclamation. For many of the examples, this is the first time they are verified automatically. For the ones where there is a competitor, we obtain a speed-up of up to two orders of magnitude.
CCS Concepts: • Theory of computation → Program verification; Type theory; Shared memory algorithms; Concurrent algorithms; Program analysis; Invariants; • Software and its engineering → Memory management; Model checking; Automated static analysis.
Additional Key Words and Phrases: lock-free data structures, safe memory reclamation, garbage collection, linearizability, verification, type systems, type inference
ACM Reference Format:
Roland Meyer and Sebastian Wolff. 2020. Pointer Life Cycle Types for Lock-Free Data Structures with Memory Reclamation. Proc. ACM Program. Lang. 4, POPL, Article 68 (January 2020), 36 pages. https://doi.org/10.1145/3371136
~/Desktop/pw.txt
).
The artifact is located at ~/popl20artifact/
.
The folder ~/popl20artifact/seal/
contains an installation of our tool seal, the back-end verifier, and all examples.
cd ~/popl20artifact/seal/ && python3 benchmark.py
to run all benchmarks.
Our paper presents an analysis for verifying lock-free data structures that use safe memory reclamation (SMR) algorithms. We follow a compositional approach which factors out the verification of the SMR implementation MeyerW19. That is, we work with a specification of the SMR algorithm rather than its implementation. The SMR specification is given in form of an SMR automaton (also called observer). This automaton is a parameter in our theory/tool (not hard-coded). To verify the data structure wrt. the SMR specification, we perform a type check. A successful typing implies that the data structure is memory safe. As a consequence, our theory allows to perform the actual verification task under garbage collection (GC) semantics—that is, we can ignore the memory management for the verification task. A major design decision in our type system is the fact that we do not track shape information. Instead, we rely on simple, light-weight annotations in the program. As we show, these annotations can be discharged under GC as well. Altogether, this means that the entire verification can soundly be conducted with an off-the-shelf GC verifier.
We implemented the proposed analysis in our tool seal. As such, it performs the following tasks:
The novelty of our approach lies in the ability to perform the last two tasks under garbage collection, using an off-the-shelf verifier. For our tool we use Vafeiadis' CAVE Vafeiadis09, Vafeiadis10a, Vafeiadis10b as a back-end solver. To help the type check go through, our tool comes with an experimental rewrite engine to automatically add invariant annotations and apply Lipton's Lipton75 mover types.
In the this page we discuss how to reproduce our experiments. Therefore, we walk through the installation, compilation, and benchmarking. In the end, we briefly link the concepts and techniques from the paper to our implementation.
This section describes how to setup your system to repeat the benchmarks from the paper.
The source code can be downloaded from GitHub here:
GitHub release with tag POPL20
To compile this project you need CMake (>3.5), a C++17 compatible compiler, Java JRE (>1.6), OCaml, and Z3 (v4.8.5). Compilation was tested with Apple LLVM v.9.1.0, Apple LLVM v.10.0.1, and Clang v.8.0. To run the benchmarking script, you need Pyton 3 (>3.5).
To build the project, do the following:
cd path/to/Seal-POPL20
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/path/to/installation -DCMAKE_CXX_COMPILER=/path/to/CXX-compiler
make
make install
This creates a folder /path/to/installation/seal
at the specified location.
This folder contains all binaries, scripts, and examples needed to reproduce the evaluation from the paper.
We recommend to use the -DCMAKE_INSTALL_PREFIX
to specify a location where to install the tool.
Omitting this flag will install the tool somewhere like /usr/local/
.
You can omit the -DCMAKE_CXX_COMPILER
option to use your system's default compiler.
AntLR
, however, your system might miss some dependencies for it to build successfully.
On Linux (tested with Ubuntu 18.04), make sure you have uuid
, uuid-dev
, and pkg-config
installed.
clang
and libc++
on Linuxclang
to build seal, you might run into problems compiling and linking AntLR
.
To solve this (tested with Ubuntu 18.04), install libc++-dev
and libc++abi-dev
.
Then, use the clang++-libc++
compiler wrapper, i.e., pass -DCMAKE_INSTALL_PREFIX=clang++-libc++
to cmake
; it properly sets up clang++
to use libc++
.
We describe how to repeat the benchmarks from the paper for verifying lock-free data structures relative to an SMR specification.
seal comes packaged with an SMR automata for Epoch-Based Reclamation (EBR) Fraser04, McKenneyS98 and Hazard Pointers (HP) Michael02b The HP specification assumes two hazard pointers per thread. The SMR automata can be found here:
We evaluated our tool on Treiber's Stack Treiber86, Michael02b, Michael&Scott's Queue MichaelS96, Michael02b, the DGLM queue DohertyGLM04, Vechev&Yahav's DCAS Set VechevY08, Vechev&Yahav's CAS Set VechevY08, the ORVYY Set OHearnRVYY10, and Michael's Set Michael02a using Hazard Pointers and Epoch-Based Reclamation.
For our experiments, we transformed the programs in order for the type check to go through. To that end, we added annotations and applied Lipton's mover types. For the code consider the following list.
To run the benchmarks go to the installation folder and execute the benchmark script like so:
cd path/to/installation
python3 benchmark.py
Make sure to use python3
.
Our test script uses the timeout feature of the subprocess
library which is available only in version 3.5 and newer.
The benchmark.py
script runs every task (type check, annotation check, linearizability check) for every example program with a default timeout of 12 hours.
You can modify the timeout in seconds by passing an optional argument to benchmark.py
:
python3 benchmark.py <timeout_in_seconds>
The benchmark script will output a table with the following columns:
We repeat our evaluation. We used an Intel<EMAIL_ADDRESS>machine with 16GB of RAM running MAC OS 10.12. The timeout was set to 12h. We mark successful tasks with , failed tasks with , and timed out tasks with .
| Program | SMR | Types | Annotations | Linearizability |
|---|---|---|---|---|
| Treiber's Stack | EBR | 0.6s | 10s | 1s |
| HP | 0.7s | 12s | 1s | |
| Opt. Treiber's Stack | HP | 0.5s | 11s | 1s |
| Michael&Scott's Queue | EBR | 0.7s | 16s | 5s |
| HP | 0.6s | 12s | 4s | |
| DGLM Queue | EBR | 0.7s | 1s* | 6s |
| HP | 0.6s | 1s* | 5s | |
| Vechev&Yahav's DCAS Set | EBR | 0.8s | 38s | 200s |
| HP | 1.2s | 13s | 98s | |
| Vechev&Yahav's CAS Set | EBR | 0.8s | 7h | 42m |
| HP | 1.2s | 3.5h | 42m | |
| ORVYY Set | EBR | 0.8s | 7h | 47m |
| HP | 1.2s | 3.2h | 47m | |
| Michael's Set | EBR | 0.2s | 22s* | t/o |
| HP | 1.2s | 90s* | t/o |
* False-positive due to imprecision in the back-end verifier.
Instead of using the benchmark script, you can directly interact with our tool seal.
You invoke seal like so:
./seal [-t] [-l] [-a] [-s] [-e] <path_to_program> <path_to_smr>
The flags and their arguments are listed in the following table (excerpt).
| Flag / Argument | Optional | Default | Description |
|---|---|---|---|
<path_to_program> |
no | — | Input program file to analyze. For examples, See Benchmark Programs |
<path_to_smr> |
no | — | Input SMR automaton file for SMR specification. For examples, See SMR Specification |
--checktypes |
yes | true |
Performs type check. |
--checkannotations |
yes | true |
Perform annotation check. |
--checklinearizability |
yes | true |
Perform linearizability check. |
--simple |
yes | false |
Simple type check. Turns of experimental rewriting&retrying (atomicity abstraction) upon type errors. |
--eager |
yes | false |
Lets the experimental rewriting&retrying check every annotation for validity before adding (deactivates heuristics). |
Besides the above, --version
prints the tool's version, -h
and --help
print the full usage description.
Our tool comes with an experimental rewrite engine for hazard pointers that fully automatically applies moverness arguments and inserts active annotations upon a failed type check.
This rewrite engine is turned on by default (turned off by the -e
eager flag).
For an example, one can execute seal on Micheal&Scott's non-preprocessed queue (Michael&Scott's Queue):
cd path/to/seal
./seal -t examples/HP/MichaelScottQueue.cola examples/hp.smr
Doing so, seal will start with the type check and soon report on a failure due using a potentially invalid pointer. It will then attempt to fix the issue based on integrated heuristics and repeat the type check. The output will give insights on the exact failure during the type check and the fix that is applied. Note that fixing an issue may take some time as seal may invoke the back-end solver to test if an annotation can be added.
At the end, seal outputs the rewritten program and the number of rewrites performed. For Michael&Scott's queue, 12 rewrites are necessary.
The following table links the techniques presented in the paper to the implementation.
| Paper | Implementation | Description |
|---|---|---|
| Programs | src/cola/ast.hpp |
Abstract syntax tree for programs. Supports a more powerful language than the one used in the paper. |
| Program Simplification | src/types/preprocess.hpp |
Translates a given program into a simpler one following the command language given in the paper. |
| SMR automata (aka observers) | src/cola/observer.hpp |
SMR automata/observer implementation. Like in the paper, the implementation assumes that non-specified transitions are intended to be self-loops. |
| Types | src/types/types.hpp |
Type implementation, storing the SMR automata locations that it represents as well as flags whether the type contains 𝔸, 𝕃, or 𝕊. The 𝔼 guarantees are implicit. Intuitively, our implementation computes the most precise 𝔼 on-the-fly. |
| Post Image on Observers | src/types/sobserver.hpp |
Our implementation uses a symbolic observer.
It implements the cross-product on SMR automata, their completion wrt. to non-specified/implicit transitions, and uses more powerful formulas as transition guards that ordinary SMR automata from src/cola/observer.hpp .
For computing the post image, we use Z3 to check the satisfiability of guards.
|
| Type Transformer Relation | src/types/types.hpp |
As described in the paper, relies on the post image for observers. Additionally, properly computes how 𝔸, 𝕃, and 𝕊 are maintained/lost/added. |
| Type Check | src/types/checker.hpp |
Implements the type rules from the paper. In addition, performs on-the-fly loop-peeling for better convergence. |
| Invariant Check | src/types/cave.hpp |
Implements the source-to-source translation from the paper.
Here, however, we the destination program is one that can be fed to the CAVE tool (differs slightly from src/cola/ast.hpp ).
|
| Linearizability Check | src/types/cave.hpp |
Uses CAVE to check for linearizability.
This step requires to translate a program into one that can be fed to CAVE .
|
| Program Transformation (Annotations, Mover Types) | src/types/rmraces.hpp |
Based on failed type checks, uses heuristics to add annotations or apply mover types. If necessary, annotations are checked for correctness before being added. (For example, active annotations on shared variables are typically not checked.) Mover types are applied if no helpful annotation can be found. Then, a sequence is checked for right-moverness and if applicable moved into an atomic block. |


---
layout: post
esbma_id: 1430
date: 2001-11-26 12:00:00
keywords: cornsharp
title: Blog entry on 26 Nov 2001
---
Gurley: "Over the five-year period from 1996 to 2000, the Internet era trained many young executives on precisely how not to succeed with a start-up." Excellent piece.


This is the partial for view 2.
form = {{entry | json}}
master = {{master | json}}
Showing of 'interpolate' filter: {{ 'Current version is v%VERSION%.' | interpolate }}


About
DNA2Drug is a powerful tool leveraging computational genomics and precision medicine to augment cancer diagnostics and deliver personalized treatment plans to oncologists instantly.
Our platform takes patient coding DNA sequences, normally provided from commercial DNA sequencing companies to oncologists, identifies specific genes and the location of individual mutations, the level of risk they pose to the patient, and determines a patient-specific drug treatment plan informed by personalized genetics, drug interactions, and side effects.
DISCLAIMER: DNA2Drug does NOT offer medical diagnoses or advice, nor
do we promise such. We are not liable for any actions taken or
damages occurring as a result of or coincident to accessing, viewing,
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POT: Python Optimal Transport¶
This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
Website and documentation: https://PythonOT.github.io/
Source Code (MIT): https://github.com/PythonOT/POT
POT provides the following generic OT solvers (links to examples):
OT Network Simplex solver for the linear program/ Earth Movers Distance [1] .
Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] , stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU implementation (requires cupy).
Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
Sinkhorn divergence [23] and entropic regularization OT from empirical data.
Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
Non regularized Wasserstein barycenters [16]) with LP solver (only small scale).
Gromov-Wasserstein distances and GW barycenters (exact [13] and regularized [12])
Fused-Gromov-Wasserstein distances solver and FGW barycenters [24]
Stochastic solver for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
Non regularized free support Wasserstein barycenters [20].
Unbalanced OT with KL relaxation and barycenter [10, 25].
Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations).
POT provides the following Machine Learning related solvers:
Optimal transport for domain adaptation with group lasso regularization, Laplacian regularization [5] [30] and semi supervised setting.
Linear OT mapping [14] and Joint OT mapping estimation [8].
Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
JCPOT algorithm for multi-source domain adaptation with target shift [27].
Some other examples are available in the documentation.
Using and citing the toolbox¶
If you use this toolbox in your research and find it useful, please cite POT using the following reference:
Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library,
Website: https://pythonot.github.io/, 2017
In Bibtex format:
@misc{flamary2017pot,
title={POT Python Optimal Transport library},
author={Flamary, R{'e}mi and Courty, Nicolas},
url={https://pythonot.github.io/},
year={2017}
}
Installation¶
The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:
Numpy (>=1.16)
Scipy (>=1.0)
Cython (>=0.23)
Matplotlib (>=1.5)
Pip installation¶
Note that due to a limitation of pip, cython
and numpy
need to
be installed prior to installing POT. This can be done easily with
pip install numpy cython
You can install the toolbox through PyPI with:
pip install POT
or get the very latest version by running:
pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root)
Anaconda installation with conda-forge¶
If you use the Anaconda python distribution, POT is available in conda-forge. To install it and the required dependencies:
conda install -c conda-forge pot
Post installation check¶
After a correct installation, you should be able to import the module without errors:
import ot
Note that for easier access the module is name ot instead of pot.
Some sub-modules require additional dependences which are discussed below
ot.dr (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
pip install pymanopt autograd
ot.gpu (GPU accelerated OT) depends on cupy that have to be installed following instructions on this page.
obviously you need CUDA installed and a compatible GPU.
Examples¶
Short examples¶
Import the toolbox
import ot
Compute Wasserstein distances
# a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix Wd=ot.emd2(a,b,M) # exact linear program Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector
Compute OT matrix
# a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix T=ot.emd(a,b,M) # exact linear program T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT
Compute Wasserstein barycenter
# A is a n*d matrix containing d 1D histograms # M is the ground cost matrix ba=ot.barycenter(A,M,reg) # reg is regularization parameter
Examples and Notebooks¶
The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on https://PythonOT.github.io/.
Acknowledgements¶
This toolbox has been created and is maintained by
The contributors to this library are
Alexandre Gramfort (CI, documentation)
Laetitia Chapel (Partial OT)
Michael Perrot (Mapping estimation)
Léo Gautheron (GPU implementation)
Nathalie Gayraud (DA classes)
Stanislas Chambon (DA classes)
Antoine Rolet (EMD solver debug)
Erwan Vautier (Gromov-Wasserstein)
Kilian Fatras (Stochastic solvers)
Vayer Titouan (Gromov-Wasserstein -, Fused-Gromov-Wasserstein)
Hicham Janati (Unbalanced OT)
Romain Tavenard (1d Wasserstein)
Mokhtar Z. Alaya (Screenkhorn)
Ievgen Redko (Laplacian DA, JCPOT)
This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
Gabriel Peyré (Wasserstein Barycenters in Matlab)
Nicolas Bonneel ( C++ code for EMD)
Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)
Contributions and code of conduct¶
Every contribution is welcome and should respect the contribution guidelines. Each member of the project is expected to follow the code of conduct.
Support¶
You can ask questions and join the development discussion:
On the POT Slack channel
On the POT mailing list
You can also post bug reports and feature requests in Github issues. Make sure to read our guidelines first.
References¶
[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).
[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), Mapping estimation for discrete optimal transport, Neural Information Processing Systems (NIPS).
[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519.
[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063.
[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), Gromov-Wasserstein averaging of kernel and distance matrices International Conference on Machine Learning (ICML).
[13] Mémoli, Facundo (2011). Gromov–Wasserstein distances and the metric approach to object matching. Foundations of computational mathematics 11.4 : 417-487.
[14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of distributions <https://link.springer.com/article/10.1007/BF00934745>`__, Journal of Optimization Theory and Applications Vol 43.
[15] Peyré, G., & Cuturi, M. (2018). Computational Optimal Transport .
[16] Agueh, M., & Carlier, G. (2011). Barycenters in the Wasserstein space. SIAM Journal on Mathematical Analysis, 43(2), 904-924.
[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).
[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) Stochastic Optimization for Large-scale Optimal Transport. Advances in Neural Information Processing Systems (2016).
[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. International Conference on Learning Representation (2018)
[20] Cuturi, M. and Doucet, A. (2014) Fast Computation of Wasserstein Barycenters. International Conference in Machine Learning
[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). Convolutional wasserstein distances: Efficient optimal transportation on geometric domains. ACM Transactions on Graphics (TOG), 34(4), 66.
[22] J. Altschuler, J.Weed, P. Rigollet, (2017) Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31
[23] Aude, G., Peyré, G., Cuturi, M., Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018
[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). Optimal Transport for structured data with application on graphs Proceedings of the 36th International Conference on Machine Learning (ICML).
[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). Learning with a Wasserstein Loss Advances in Neural Information Processing Systems (NIPS).
[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport, Advances in Neural Information Processing Systems 33 (NeurIPS).
[27] Redko I., Courty N., Flamary R., Tuia D. (2019). Optimal Transport for Multi-source Domain Adaptation under Target Shift, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.
[28] Caffarelli, L. A., McCann, R. J. (2010). Free boundaries in optimal transport and Monge-Ampere obstacle problems, Annals of mathematics, 673-730.
[29] Chapel, L., Alaya, M., Gasso, G. (2019). Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning, arXiv preprint arXiv:2002.08276.
[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching, NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.


Saturday, November 10th to Sunday, November 11th (noon-noon)
A 24-hour hackathon for high school coders. Work together to build something amazing!
Byte Club students will be chaperoned as we take the train to and from the event. More details available below.


Setup¶
site config¶
Config files are formatted with the toml mini language.
output_dir = "output/limassol/"
plot_dir = "plots/limassol"
traj_dir = "trajectories/limassol/"
partposit_dir = "flexpart_partposit/limassol/"
# geonames that should be used
# name is defined in geonames_config.toml
# and links the names to the .kml files
geonames = 'standard'
[time]
# begin and end definitions only relevant if
# gen_hysplit_input.py is used
#begin = '2018-03-14_00'
#end = '2018-03-25_00'
begin = '2015-09-09_00'
end = '2015-09-14_00'
# time step for which trajectories are calculated
step = 3
# duration of each trajectory
tr_duration = -240
[flexpart]
no_particles = 500
[plotmap]
maptype = 'miller'
timeinterval = 6
heights = [1500.0, 3000.0, 4500.0, 6000.0]
bounds = [-120, 80, 0, 85]
centerlon = -10
[height]
top = 12000
plottop = 10250
# reception heights that are used
# 'md' mixing depth, '2.0' in km without unit
reception = ['md', '2.0', '5.0']
[station]
name = 'Limassol, Cyprus'
short_name = 'limassol'
lat = 34.677
lon = 33.038
altitude = 10
FLEXPART simulations¶
FLEXPART simulations are controlled with with template files in flexpart_simulations/
. Some settings are predefined in the *_template
files, ${var} are adaped for each run with the python template library.
Per default, the particle positions during the backward simulation are dumped every 3 hours.
HYSPLIT simulations¶
HYSPLIT backward trajectories are required, a 10-day 27-member ensemble setup is recommended. Please note that HYSPLIT itself is not provided within this package (The binary to run is hyts_ens with the respective CONTROL file).
Meteorological input data for HYSPLIT are taken from the GDAS1 dataset (<https://www.ready.noaa.gov/gdas1.php>) provided by the Air Resources Laboratory (ARL) of the U.S. National Weather Service’s National Centers for Environmental Prediction (NCEP).
An trajectory is calculated every 3h in steps of 500m. Conveniently the input trajectories are placed in the trajectories
directory.
Following setting provide a starting point:
SETUP.CFG
&SETUP
KMSL=0,
tm_rain=1,
tm_tpot=0,
tm_tamb=1,
tm_mixd=1,
tm_relh=1,
tm_terr=1,
dxf=0.4,
dyf=0.4,
dzf=0.008,
/
TRAJ.CFG
&SETUP
tratio = 0.75,
delt = 0.0,
mgmin = 10,
khmax = 9999,
kmixd = 0,
kmsl = 0,
k10m = 1,
nstr = 0,
mhrs = 9999,
nver = 0,
tout = 60,
tm_pres = 1,
tm_tpot = 0,
tm_tamb = 1,
tm_rain = 1,
tm_mixd = 1,
tm_relh = 1,
tm_sphu = 0,
tm_mixr = 0,
tm_dswf = 0,
tm_terr = 1,
dxf = 0.40,
dyf = 0.40,
dzf = 0.01,
messg = 'MESSAGE',
/


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--- layout: docpage title: "Documentation" is_homepage: false is_sphinx_doc: true doc-parent: "Contributing to Cassandra" doc-title: "Release Process" doc-header-links: ' ' doc-search-path: "../search.html" extra-footer: ' ' ---
Contents
Attention
The steps for Release Managers to create, vote and publish releases for Apache Cassandra.
While a committer can perform the initial steps of creating and calling a vote on a proposed release, only a PMC can complete the process of publishing and announcing the release.
A debian based linux OS is required to run the release steps from. Debian-based distros provide the required RPM, dpkg and repository management tools.
To create a GPG key, follow the guidelines. Include your public key in:
https://dist.apache.org/repos/dist/release/cassandra/KEYS
Publish your GPG key in a PGP key server, such as MIT Keyserver.
Any committer can perform the following steps to create and call a vote on a proposed release.
Check that no open jira tickets are urgent and currently being worked on. Also check with a PMC that there’s security vulnerabilities currently being worked on in private.
Run the following commands to generate and upload release artifacts, to a nexus staging repository and distribution location:
cd ~/git
git clone https://github.com/apache/cassandra-builds.git
# Edit the variables at the top of `cassandra-builds/cassandra-release/prepare_release.sh`
# After cloning cassandra-builds repo, the prepare_release.sh is run from the actual cassandra git checkout,
# on the branch/commit that we wish to tag for the tentative release along with version number to tag.
# For example here <version-branch> might be `3.11` and <version> `3.11.3`
cd ~/git/cassandra/
git checkout cassandra-<version-branch>
../cassandra-builds/cassandra-release/prepare_release.sh -v <version>
If successful, take note of the email text output which can be used in the next section “Call for a Vote”.
The prepare_release.sh
script does not yet generate and upload the rpm distribution packages.
To generate and upload them do:
cd ~/git/cassandra-build
docker build -f docker/centos7-image.docker docker/
docker run --rm -v `pwd`/dist:/dist `docker images -f label=org.cassandra.buildenv=centos -q` /home/build/build-rpms.sh <version>-tentative
rpmsign --addsign dist/*.rpm
For more information on the above steps see the cassandra-builds documentation. The next step is to copy and commit these binaries to staging svnpubsub:
# FIXME the following commands is wrong while people.apache.org is still used instead of svnpubsub and dist.apache.org
cd ~/git
svn co https://dist.apache.org/repos/dist/dev/cassandra cassandra-dist-dev
mkdir cassandra-dist-dev/<version>
cp cassandra-build/dist/*.rpm cassandra-dist-dev/<version>/
svn add cassandra-dist-dev/<version>
svn ci cassandra-dist-dev/<version>
After committing the binaries to staging, increment the version number in Cassandra on the cassandra-<version-branch>
cd ~/git/cassandra/ git checkout cassandra-<version-branch> edit build.xml # update `<property name=”base.version” value=”…”/> ` edit debian/changelog # add entry for new version edit CHANGES.txt # add entry for new version git commit -m “Update version to <next-version>” build.xml debian/changelog CHANGES.txt git push
Fill out the following email template and send to the dev mailing list:
I propose the following artifacts for release as <version>.
sha1: <git-sha>
Git: https://gitbox.apache.org/repos/asf?p=cassandra.git;a=shortlog;h=refs/tags/<version>-tentative
Artifacts: https://repository.apache.org/content/repositories/orgapachecassandra-<nexus-id>/org/apache/cassandra/apache-cassandra/<version>/
Staging repository: https://repository.apache.org/content/repositories/orgapachecassandra-<nexus-id>/
The distribution packages are available here: https://dist.apache.org/repos/dist/dev/cassandra/${version}/
The vote will be open for 72 hours (longer if needed).
[1]: (CHANGES.txt) https://git1-us-west.apache.org/repos/asf?p=cassandra.git;a=blob_plain;f=CHANGES.txt;hb=<version>-tentative
[2]: (NEWS.txt) https://git1-us-west.apache.org/repos/asf?p=cassandra.git;a=blob_plain;f=NEWS.txt;hb=<version>-tentative
Any PMC can perform the following steps to formalize and publish a successfully voted release.
Run the following commands to publish the voted release artifacts:
cd ~/git
git clone https://github.com/apache/cassandra-builds.git
# edit the variables at the top of `finish_release.sh`
# After cloning cassandra-builds repo, `finish_release.sh` is run from the actual cassandra git checkout,
# on the tentative release tag that we wish to tag for the final release version number tag.
cd ~/git/cassandra/
git checkout <version>-tentative
../cassandra-builds/cassandra-release/finish_release.sh -v <version> <staging_number>
If successful, take note of the email text output which can be used in the next section “Send Release Announcement”. The output will also list the next steps that are required. The first of these is to commit changes made to your https://dist.apache.org/repos/dist/release/cassandra/ checkout.
- Login to Nexus repository again.
- Click on “Staging” and then on the repository with id “cassandra-staging”.
- Find your closed staging repository, right click on it and choose “Promote”.
- Select the “Releases” repository and click “Promote”.
- Next click on “Repositories”, select the “Releases” repository and validate that your artifacts exist as you expect them.
Run the following command:
cd ~/git
# FIXME the next command is wrong while people.apache.org is used instead of svnpubsub and dist.apache.org
svn mv https://dist.apache.org/repos/dist/dev/cassandra/<version> https://dist.apache.org/repos/dist/release/cassandra/
# Create the yum metadata, sign the metadata, and sign some files within the signed repo metadata that the ASF sig tool errors out on
svn co https://dist.apache.org/repos/dist/release/cassandra/redhat/ cassandra-dist-redhat
cd cassandra-dist-redhat/<abbreviated-version>x/
createrepo .
gpg --detach-sign --armor repodata/repomd.xml
for f in `find repodata/ -name *.bz2`; do
gpg --detach-sign --armor $f;
done
svn co https://dist.apache.org/repos/dist/release/cassandra/<version> cassandra-dist-<version>
cd cassandra-dist-<version>
cassandra-build/cassandra-release/upload_bintray.sh cassandra-dist-<version>
See `docs https://svn.apache.org/repos/asf/cassandra/site/src/README`_ for building and publishing the website. Also update the CQL doc if appropriate.
Release the JIRA version.
- In JIRA go to the version that you want to release and release it.
- Create a new version, if it has not been done before.
Edit and commit build.xml
so the base.version property points to the next version.
Wait for the artifacts to sync at http://www.apache.org/dist/cassandra/
Fill out the following email template and send to both user and dev mailing lists:
The Cassandra team is pleased to announce the release of Apache Cassandra version <version>.
Apache Cassandra is a fully distributed database. It is the right choice
when you need scalability and high availability without compromising
performance.
http://cassandra.apache.org/
Downloads of source and binary distributions are listed in our download
section:
http://cassandra.apache.org/download/
This version is <the first|a bug fix> release[1] on the <version-base> series. As always,
please pay attention to the release notes[2] and let us know[3] if you
were to encounter any problem.
Enjoy!
[1]: (CHANGES.txt) https://git1-us-west.apache.org/repos/asf?p=cassandra.git;a=blob_plain;f=CHANGES.txt;hb=<version>
[2]: (NEWS.txt) https://git1-us-west.apache.org/repos/asf?p=cassandra.git;a=blob_plain;f=NEWS.txt;hb=<version>
[3]: https://issues.apache.org/jira/browse/CASSANDRA
cassandra
Slack roomTweet the new release, from the @Cassandra account
As described in When to Archive. Also check people.apache.org as previous release scripts used it.


JUNGLE 2 JUNGLE A film review by James Berardinelli Copyright 1997 James Berardinelli
RATING (0 TO 10): 2.5 Alternative Scale: * out of ****
United States, 1997 U.S. Release Date: 3/7/97 (wide) Running Length: 1:45 MPAA Classification: PG (Nothing offensive, so to speak) Theatrical Aspect Ratio: 1.85:1
Cast: Tim Allen, Sam Huntington, JoBeth Williams, Lolita Davidovich, Martin Short, David Ogden Stiers, Bob Dishy Director: John Pasquin Producer: Brian Reilly Screenplay: Bruce Evans and Raynold Gideon Cinematography: Tony Pierce-Roberts Music: Michael Convertino U.S. Distributor: Walt Disney Pictures
Before I start lambasting this motion picture, let me recap my feelings about the French film upon which JUNGLE 2 JUNGLE is based.  Not for one moment was it cute, funny, or likable.  The predictable result is a bad movie -- the kind of pointless tripe that the Mouse Company continues to foist upon us in the name of "family entertainment."
When I first heard that Allen was going to star in the LITTLE INDIAN reworking, my reaction was that it couldn't possibly be worse than the original. Ultimately, I was right, although just barely.  It is slightly more energetic than LITTLE INDIAN, and that's the only reason why I have given it a marginally better rating.
Allen plays Michael, a top-of-the-line Wall Street trader. He's engaged to an airheaded bimbo named Charlotte (Lolita Davidovich), but before they can tie the knot, he has to fly to Venezuela, find his current wife, Patricia (JoBeth Williams), and get a divorce (they've been separated for more than a dozen years). When he arrives in Patricia's little village, he learns that the thirteen year old white kid (Sam Huntington) in a loin cloth is actually his son. Furthermore, for reasons that are never made clear, Michael is obligated to bring the boy, Mimi, back to New York to experience life in the big city. CROCODILE DUNDEE this ain't.
Everything in JUNGLE 2 JUNGLE is hackneyed. There's never a whiff of innovation or a hint that the film makers might be interested in trying something different. And, like all "good" family films these days, there are several obligatory flatulence jokes. I think we're supposed to be touched by the growing bond between Michael and Mimi, and tickled by the slapstick-y antics of Allen and Martin Short (who plays one of Michael's cohorts). Neither of these aspects work. There's nothing emotionally solid or satisfying about the father/son relationship, and the duo's attempts at comedy seem more desperate than funny. The most amusing moment in JUNGLE 2 JUNGLE is entirely unintentional. During a scene when an incredibly fake spider is scurrying around the floor of Michael's boss' office, you can see the strings being used to manipulate it.
I for one am sick and tired of the unappealingly unoriginal fare that Disney releases on a constant basis. It's getting to the point where a visit to the dentist sounds like a reasonable alternative to sitting through another plot-by-numbers family film from the studio of plastic characters and happy endings. At least this time they've managed to do something of dubious note: turn one of 1996's worst motion pictures into one of 1997's. Congratulations, Disney. I hope Walt is turning in his grave.
- James Berardinelli e-mail<EMAIL_ADDRESS>ReelViews web site: http://www.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Czech-Chinese ties hit new bump as university shuts center
PRAGUE (Reuters) - Prague’s Charles University has closed its Czech-Chinese Centre, rector Tomas Zima said on Wednesday, after local media reports that some of its staff had failed to declare payments from the Chinese embassy intended to support its programmes.
The reports, published in October, prompted the secretary of the center to resign. Zima made his announcement in a statement posted on the university’s website.
The Czech Republic has recently been trying to attract Chinese money after years of criticizing China’s human rights record. However, the push has not generated the expected billions of dollars in Chinese investment.
Last month, Prague city council canceled a partnership agreement with Beijing.
And a year ago, the Czech cyber-security watchdog warned against using equipment made by China’s Huawei and ZTE in critical infrastructure.
The Czech-Chinese Centre had been intended to promote academic and cultural exchange through some of the Charles University’s faculties.


Linux Distribution & Version
cat /etc/issue
cat /etc/*-release
32 o 64 bit?
cat /proc/version
uname -a
uname -mrs
rpm -q kernel
dmesg | grep Linux
Environment Variables
cat /etc/profile
cat /etc/bashrc
cat ~/.bash_profile
cat ~/.bashrc
cat ~/.bash_logout
cat ~/.nano_history
cat ~/.atftp_history
cat ~/.mysql_history
cat ~/.php_history
cat /var/mail/root
cat /var/spool/mail/root
env
Printer?
lpstat -a


Doctor Search
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Search for Doctors by ailment
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Find Doctor by Name
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Doctors:


typer
Module
Module Summary
Description
TypEr shows type information for Erlang modules to the user. Additionally, it can annotate the code of files with such type information.
Using TypEr from the Command Line
TypEr is used from the command-line. This section provides a brief description of the options. The same information can be obtained by writing the following in a shell:
typer --help
Usage:
typer [--help] [--version] [--plt PLT] [--edoc] [--show | --show-exported | --annotate | --annotate-inc-files] [-Ddefine]* [-I include_dir]* [-pa dir]* [-pz dir]* [-T application]* [-r] file*
* denotes that multiple occurrences of the option are possible.
Options:
- --r
-
Search directories recursively for .erl files below them.
- --show
-
Print type specifications for all functions on stdout. (This is the default behaviour; this option is not really needed.)
- --show-exported (or show_exported)
-
Same as --show, but print specifications for exported functions only. Specs are displayed sorted alphabetically on the function's name.
- --annotate
-
Annotate the specified files with type specifications.
- --annotate-inc-files
-
Same as --annotate but annotates all -include() files as well as all .erl files. (Use this option with caution - it has not been tested much).
- --edoc
-
Print type information as Edoc @spec comments, not as type specs.
- --plt
-
Use the specified dialyzer PLT file rather than the default one.
- -T file*
-
The specified file(s) already contain type specifications and these are to be trusted in order to print specs for the rest of the files. (Multiple files or dirs, separated by spaces, can be specified.)
- -Dname (or -Dname=value)
-
Pass the defined name(s) to TypEr. (**)
- -I
-
Pass the include_dir to TypEr. (**)
- -pa dir
-
Include dir in the path for Erlang. This is useful when analyzing files that have -include_lib() directives or use parse transforms.
- -pz dir
-
Include dir in the path for Erlang. This is useful when analyzing files that have -include_lib() directives or use parse transforms.
- --version (or -v)
-
Print the TypEr version and some more information and exit.
** options -D and -I work both from the command line and in the TypEr GUI; the syntax of defines and includes is the same as that used by erlc(1).


|
density
C++11 library for paged memory management, function queues, heterogeneous queues and lifo memory management
|
In this single-thread tests all the function queues of density are compared to a std::vector
and a std::queue
of std::function
s. In every test a function queue is created, filled by many trivial functions, and then all the functions are consumed on-by-one. Only in the case of std::vector
the queue is cleared in one time, rather than removing the functions one by one while they are invoked. This is because there is no constant-time function to remove the first element of a std::vector
.
In the first test the function queues of density are instantiated with the default template parameters. In the second test they exploit all the available optimizations, that is:
All the tests are compiled with Visual Studio 2017 (version 15.1), and executed on Windows 10 with an int Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz, 2808 Mhz, 4 cores, 8 logical processors. The benchmarks of the whole library are shuffled and interleaved at the granularity of a single invocation with a given cardinality, and every invocation occurs 8 times.
Even unnecessarily paying the cost of multithreading, concurrent function queues are in general better than std::queue
of std::function
. lf_function_queue is much faster in the case of single consumer and single producers, as it can reduce considerably the number of atomic memory operations.


Beans are written, updated and deleted from the database using the org.bhavaya.db.Persister class.
A Persister object relates to a given database connection and will optionally broadcast the sql used to modify the database. Its simplest use is as follows:
Persister(String dataSourceName, NotificationPublisher broadcaster)
The dataSourceName is one defined in application.xml and the broadcaster can be null. The relevant methods for persisting beans are:
| method | return type | description |
|---|---|---|
| deleteObject(Object bean) | void | Remove the entry from the database for the specified bean |
| insertObject(Object bean) | void | Adds an entry for the specified bean to the database. It is up to the user to ensure that the correct primary key has been set on the bean. Alternatively, the primary key can be generated automatically. This will happen if the idFactory attribute of primaryKey has been set in schema.xml |
| updateObject(Object bean, String[] propertiesToSet) | void | Updates the entry in the database for the specified bean, updating the properties specified in propertiesToSet. |
| commit() | void | Commit any transactions initiated using this persister instance. Necessary if the datasource has autocommit set to false in application.xml |
| rollback() | void | Rollback to the last commit - can be used if autocommit is set to false in application.xml. |
An example of updating the price property of an instrument follows:
BeanFactory instrumentFactory = BeanFactory.getInstance(Instrument.class); Instrument instrument = (Instrument) instrumentFactory.get(new Integer(3)); instrument.setPrice(5.99); Persister persister = new Persister("example2Database",null); try { persister.updateObject(instrument,new String[]{"Price"}); } catch (Exception e) { System.err.println("Exception!"); }
and each of the methods is exercised in Example2b.java.


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sign up
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1- you should read the address of this web site .
2- you should read this phrase .
3- you should read this too !!! :D .
4- finally : you should sign up .
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?NEW
YORK
BOTANICAL
GARDEN
530081
botanical
PLANTS
OF
HAITI
y
Vl6fnity
of
Mole
St.
Nicolas
/ÈÔ?I
A?
EMERY
C.
LEONARD
ã
GENEVIEVE
M.
LEONARD)
*cctors
Feb
13-19,
\
01530081
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
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medium
high
very high


{% extends "base.html" %}
{% block page_title %}Resume{% endblock page_title %}
{% block meta_tags %}
{% endblock meta_tags %}
{% block body_id %}id_home{% endblock body_id %}
{% block body_data_controller %}home{% endblock body_data_controller %}
{% block body %}
I'm a developer, software engineer, mechanical engineer, leader I am a Software Engineer with a focus on web development and testing. Recently, I've been working in the fields of backend and frontend web development and testing. I love trying out opensource software and building software which makes our everyday life easy
Full name:
Age:
Location:Profile
About me
General information
Phani Nuthaky
{{ age }} years old
Morrisville, NC
My education and experience
2013 – 2015
Master's degree in Business Administration
Master's Degree in Business Administration
2008 – 2010
Master's degree in Engineering
Master's Degree in Mechanical Engineering
2001 – 2005
Bachelor of Technology
Bachelors Degree in Mechanical Engineering.
Oct 2015 – Present
Network Engineer
Involved in the development of Test Plan and Test cases. Performed automation for Functional and Regression Testing using Selenium WebDriver and Calabash. Authored about 40 test cases to handle different call flows using the Cisco Spark app on different platforms and maintained them on bitbucket. Configuring static IPs on cisco gateways and configuring static reservations on Windows DNS servers. Deploying VMs on vCenter and supporting development teams with the access to the testbed. Wrote APIs in python to simulate input via keypad on a Cisco phone and generating HTML report.
Dec 2012 – Mar 2013
Software Engineer
Worked on a migration project where the systems in the organization have upgraded from Windows XP to Windows 7. Tested about 200 applications for compatibility on Windows7. Repackaged the tested applications for deployment.
Sep 2012 – Dec 2012
Software Engineer
Worked on a migration project where the systems in the organization have upgraded from Windows XP to Windows 7. Tested about 200 applications for compatibility on Windows7. Repackaged the tested applications for deployment.
Jun 2012 – Sep 2012
Software Engineer
Participated in web design meetings and analyzed user needs to determine technical requirements. Developed and maintained websites or web applications involving multiple programming languages. Designed coherent user interfaces consistent with modern web design practices. Wrote and maintained unit tests and design documentation for subsystems and components. Modified, repaired, or expanded programs to correct errors, increase efficiency, upgrade interfaces, or improve performance. Performed testing and analysis on web and systems to identify problems. Evaluated effectiveness of Web applications and systems or potential/existing vendor components.
Dec 2011 – Jun 2012
Programmer Analyst
Extracted data from different sources Oracle, SQL Server and Flat files implemented business logic and loaded into the target Warehouse (Oracle). Studied the PL/SQL code developed to relate the source and target mappings. Designed and developed the jobs for Extracting, Transforming, Integrating, and Loading Data using DataStage Designer. Modified existing program logic to improve overall system performance.
Mar 2011 – Dec 2011
Programmer Analyst
Worked on an internal payroll application to suggest and implement improvements. Acquired proficiency with the various data base resources and data base management. Ameliorated performance of communication systems, software storage, security, hardware, network, and others. Acquired as well as developed test data, revising program(s) resulting in improved performance.
Jun 2005 – Dec 2007
Program Instructor and Office Assistant
Assisted students with their lab work. Helped undergraduate students with their final project work. Scheduled lab timings for different batches of students.
I can say I am good at
Python
Django Framework
Flask Framework
Javascript
HTML & CSS
Bourne shell scripting
Java
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In my spare time I like to
Technology facinates me, I spend a lot of time on my laptop exploring new advances in the areas of web and computer services which are intended to the professional world or the public.
I like learning new tricks on programming by writing some programs. I like building stuff reusing already built ones.
I am a hobbiest photographer. I love taking portraits of my daughter and capturing beautiful landscapes.
I love reading articles on the web and some SiFi thillers.
I love traveling to places with historical importance and places of natural beauty.


Liping Zhang's Github Portfolio
This is Liping Zhang's placeholder page.

Coding is always the life!
Geeks for geeks!


I love to cook and make cheese. I'm also a home brewer and distiller.
I make cheese because even though it's convenient to buy, store bought cheese doesn't taste nearly as good. I also like to cook unusual or interesting food, particularly learning new techniques and trying out new flavour combinations.


Junior Software Engineer (Front-end / JavaScript)
Anton Chyvikov
I'm an detail-oriented Software Engineer (Front-end / JavaScript) who seeking an entry-level position with Company to use my skills in coding, troubleshooting complex problems, and assisting in the timely completion of projects. I am inviting You to get acquainted with my skills and pet projects. Feel free!
Completed tests
LinkedIn Skills Assessment Badge
HTML
2020LinkedIn Skills Assessment Badge
CSS
2020LinkedIn Skills Assessment Badge
JavaScript
2020LinkedIn Skills Assessment Badge
React
2021LinkedIn Skills Assessment Badge


This app uses the Yahoo! Weather API and Spotify API. When you enter your zipcode, and click the button, a GET request is sent to the Yahoo! Weather API to get the weather for that zipcode. The app will then use description as keywords to send another GET request to the Spotify API to return results of songs titles that match that keyword. This app was built using the Angular JS javascript framework and Bootstrap responsive framework.


Kraken
A lightweight, mobile-first boilerplate for front-end web developers.
A lightweight, mobile-first boilerplate for front-end web developers.
Designed to be lightweight and style agnostic, Kraken includes just the essentials.
Lightweight, style-agnostic components to kick-start your next project.
Kraken is powered by Gulp.js, a build system that minifies and concatenates your Sass and JavaScript, auto-prefixes your CSS, runs unit tests on your scripts, and generates SVG sprites. It also includes a documentation builder to help you quickly bring your team or clients up-to-speed.
While the base boilerplate is deliberately lightweight, a growing collection of add-ons lets you make Kraken as robust—or simple—as you want it to be. Create custom-built sites and applications faster.
Learn what's new in Kraken 4 →
Built a site with Kraken? Let me know!


{% extends 'base.html' %} {% import 'related_list_macro.html' as relist with context %} {% block main %}
Neuroinformatics is concerned with developing computational techniques for understanding the brain. Specifically, processing and analysing experimental data, and simulating models. This work is becoming increasingly important as neuroscientists study larger and more detailed systems, requiring the use of high performance computational techniques which are not, as yet, easily accessible for non-experts. Much of our research is about leveraging modern, heterogeneous forms of computing such as GPUs and FPGAs, but making them accessible to neuroscience researchers.
Our main ongoing area of research is in simulating spiking neural networks: on the design of the Brian simulator and related software packages.
If you are interested in working on these types of problems, please see our openings page and the list of suggested research topics. See also the list of neuroinformatics publications below for an idea of the sort of research we have done in this field in the past.
{{ relist.related_by_class(categories['sensory'].things) }} {% endblock %}


--- layout: post title: Google's public DNS date: '2009-12-04T00:57:00.001Z' author: solrevdev tags: modified_time: '2009-12-04T00:57:08.079Z' blogger_id: tag:blogger.com,1999:blog-31446756.post-8636029995747235577 blogger_orig_url: http://www.solrevdev.com/2009/12/google-public-dns.html ---
Just switched from using OpenDNS to Google's new public DNS and unless I am very much mistaken it is much much quicker at resolving stuff and everything seems a lot snappier as a result.


{% extends "layout.html" %} {% set title = 'Overview' %} {% block body %}
Onyx is a tool that makes it easy to create intelligent and beautiful experiments. It was originally created to translate the whiteboard sketches of speech scientists into running experiments. It has excellent support for the streaming processing of conceptually unbounded amounts of data for machine-learning and statistical inference applications.
Onyx uses Python as its programming language, and many of its strengths come from the power and simplicity of Python itself, and from Python's high-performance numerical extensions.
Onyx was originally written by Hugh Secker-Walker and Ken Basye at the Johns Hopkins University. Onyx is licensed under the Apache 2 license.
|
Installation and User guide Tutorials |
Reference documentation Developer guide |
|
Complete Module Index General Index Glossary |
Complete Table of Contents Search page |
Onyx is hosted at SourceForge. There are released files (tarballs) and the latest released version of this documentation. See the Installation and User guide for getting started using released tarballs.
{% endblock %}


Instructions
Use your keyboard to guess the letters of a randomly chosen common fruit! You have five more guesses than the number of letters in the fruit.


TSIDs to update (one per line)
Render Streets
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Upload a new/updated piece of scenery
Upload


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Alabama
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Personal Image Sharing and Embedding
I’ve been looking at writing more of my blog posts on my iPad. However, up until I’ve recently been using Flickr for storing all my images and displaying them on my site. This has worked well in the past but has been a long winded - on the iMac, the image is copied into iPhoto and then uploaded to Flickr via that. Adding the image to the blog post is achieved by inserting the image using MarsEdit.
The issue is attaching the image inline on the iPad. Whilst I can happily upload images onto Flickr, I can’t then get the inline link attachment from the Flickr app itself. I could mess about with mobile Safari and the Flickr website, but the non mobile Flickr site is pretty poor on the iPad, so I’ve decided to go another way.
Transmit
Earlier in 2014, Panic released the excellent Transmit FTP app onto the iOS app store. My decision to move in this direction was influenced in no uncertain terms by Macdrifter. This guide helped me set out the path that I was to take.
I use the excellent Fastmail to host my email, which also comes with FTP space (5GB on my plan). This allows me to host a very basic website (csalter.me.uk and vox.me.uk) on the Fastmail servers - my sites aren’t popular enough to reach the bandwidth limits set, so it’s a good way of hosting a static site. However, this space wasn’t being used and and as I can host from this location, I decided that I’d upload images to here and have them linked to on the blog.
However, after some playing around with the basic idea from Macdrifter, it needed some tweaking to suit me, my host and my blogs.
Uploading and Resizing
When uploading to Fastmail for testing, I found that the images I was uploading were a bit big and were taking a while to download - the downside of hosting it off the Fastmail servers are that they’re great at email, but perhaps I was asking to much, hosting 5 x 5MB images per post on the server and getting Tumblr to resize them dynamically for me. So the best bet seemed to be to resize them before uploading them to the Fastmail servers.
On the Mac, I currently use ImageMagick to resize it with the commands:
<code>mogrify -resize 307200@ -format JPG *.JPG
mogrify -resize 307200@ -format jpg *.jpg
mogrify -resize 307200@ -format jpeg *.jpeg
mogrify -resize 307200@ -format png *.png
mogrify -resize 307200@ -format PNG *.PNG
</code>
This gives me a image that is about 640 x 480 (though if it’s in portrait, it’ll be 480 x 640 - the pixel count resizes approximately to this size and keeps the photo ratio). This is uploaded onto the Fastmail server with Transmit.
This allows me to copy and paste the URL of the image and paste it directly into my text editor.
I can now achieve the same on the iPad with the recently released Workflow app. The workflow can be found here. It basically takes a photo from your photo roll, resizes it to 640 x 480 and then allows you to open it using an extension, which Transmit has. This then lets you upload to the Fastmail server, using a previously saved bookmark.
Currently, it’s very basic as the iPhone/iPad version shown doesn’t take into account the orientation of the photo and so it’ll always output as 640 x 480, thus ruining portrait photos (like the image of the workflow above would be). I think this could be done in Python within the workflow app, but I would need to learn more Python for that!
Conclusions
With Workflow, I can easily take and upload pictures from my iPhone and upload them to Fastmail’s web service where I can then copy and paste the URL from Transmit into my Editorial app where I’m writing. Unlike my previous workflow of using Flickr, this allows me to upload to the same location whether I’m using my iPad or my iMac (or even my Windows work laptop, as occasionally happens!) In addition, I’m also retaining control of the images and in theory, I should be able to keep the URL’s the same in the future, even if I move from Fastmail as it’s on one of my domain names.
Overall, I’m pretty happy with the change. The only issue I do have is that Flickr would link to the full size image - my current setup only shows the smaller picture. However, I don’t think this is to much of a loss and it saves on the bandwidth on the Fastmail servers.
In the future, I could potentially look at using Hazel to automate some of the resize tasks on the iMac. Likewise, I have a small server with BuyVM - workflow allows me to run a script on something via SSH, so I could potentially have Workflow (and Hazel) upload an image to there, run a script to resize the image using ImageMagick and then move it to my Fastmail folder. But that’s for the future as it’s currently working fine at the minute.


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Policy paper
Parliamentary minute outlining how outstanding Local Government Pension Scheme (LGPS) liabilities will be guaranteed when an academy closes.
A parliamentary minute by the Secretary of State for Education, Michael Gove, laid on 2 July 2013 in the Houses of Commons and Lords.
The department guarantees that any outstanding Local Government Pension Scheme (LGPS) liabilities on the closure of an academy will be met by the department.


Select Team :
| P# Number | OPPM Number | Project Description | Release | Estimated Hours | Assigned | Action |
| {{projectByTeam.pNumber}} | {{projectByTeam.oppm}} | {{projectByTeam.projectDesc}} | {{projectByTeam.monthRelease}}-{{projectByTeam.yearRelease}} | {{projectByTeam.hours}} | {{projectByTeam.assigned}} |
| {{teamMember.lastName}}, {{teamMember.firstName}} ({{teamMember.soeid}}) | {{individualHour}} |


{% extends 'layouts/master.html' %} {% import './macros/ads.html' as ads %} {% import './macros/paginate.html' as paginate %} {% block embed %} {{ ads.topResponsive() }}
↑
The folder system is
a work in progress.
It doesn't do much, yet.
You haven't moved any convos into this folder, yet
Page is empty.
In the future I'll prevent you from landing on an empty page.
← Back to Page 1


---
layout: theorems
---
Two homogeneous equations have the same solution if they are row equivalent
For two $m\times n$ matrices $A$, $B$, $Ax=0$, $Bx=0$, the two have the same solutions if $A, B$ are row equivalent.
Concepts
Coming soon
Hypothesis
We require that $Ax=0$ and $Bx=0$ as we need a solution to $Ax=0$ and assume that $B$ is obtained from $A$ by a single elementary row operation. Row operations on a column of $0$ remain the same column of $0$.
We require $A$ and $B$ to be row equivalent for $Ax=0$ and $Bx=0$ to be equivalent, in order to call on another theorem. For $A$ and $B$ to be row equivalent, there exists elementary matrices $E_1, \dots, E_k$ such that $B=E_k\cdot \dots E_1A$.
Results
If two systems have the same solution, we can solve one system by solving the other.
Proof
Suppose $B$ is obtained from $A$ using a finite number of elemenetary row operations. That is,
$$\begin{align*}
A=A_0\to A_1\to\dots\to A_k=B.
\end{align*}$$
It suffices to show that $A_{i}x=0$ and $A_{i+1}x=0$ have solutions for $0\leq 1\leq k-1$ (yeah idk what's going on there). We can also assume that $B$ is obtained from $A$ by a single elemenetary row operation.
Since elementary row operations are all invertible using elementary row operations, then rows of $A$ are linear combinations of rows of $B$, meaning $Ax=0$ and $Bx=0$ are equivalent. By a a theorem that equivalent matrices have the same solution, $A$ and $B$ have the same solution.


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From:Carpenter Nancy To:Weitekamp James ; Barnes Caroline Sent:2017-01-11T04:36:36.0000000Z Subject:FW: Gas Logistics 2001 C.E. vs 2002 Plan
FYI...the 2002 budget for Communications (111721) is in Gas Logistics in Adaytum.
Please let me know if you need it moved.
Thanks,
Nancy
-----Original Message-----
From: Barnes, Caroline
Sent: Wednesday, October 31, 2001 10:50 AM
To: Carpenter, Nancy
Subject: RE: Gas Logistics 2001 C.E. vs 2002 Plan
We need to let Tracy and James know that so they can move it where ever it belongs. cvb
-----Original Message-----
From: Carpenter, Nancy
Sent: Wednesday, October 31, 2001 8:58 AM
To: Barnes, Caroline
Subject: RE: Gas Logistics 2001 C.E. vs 2002 Plan
No...We included it in Adaytum because we weren't sure where it would go, and wanted to be sure the costs were included in the Plan.
-----Original Message-----
From: Barnes, Caroline
Sent: Wednesday, October 31, 2001 8:51 AM
To: Carpenter, Nancy
Subject: RE: Gas Logistics 2001 C.E. vs 2002 Plan
Communications for 2002 is moving out of Gas Logistics into somewhere else. Did we remove it from Gas Logistics? cvb
-----Original Message-----
From: Carpenter, Nancy
Sent: Tuesday, October 30, 2001 5:22 PM
To: Weitekamp, James; Geaccone, Tracy
Cc: Scott, Donna; Barnes, Caroline
Subject: Gas Logistics 2001 C.E. vs 2002 Plan
Importance: High
James,
I believe the Communications for 2002 should be $.2MM in NNG's Gas Logistics...
Anyway...
NNG 2002 increase is due primarily to:
EPSC $.3
Less Capitalized $.4
Increase in Benefits $.15
Increase in Salaries $.1
Hope this helps.
Nancy
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EDRM Enron Email Data Set has been produced in EML, PST and NSF format by ZL Technologies, Inc. This Data Set is licensed under a Creative Commons Attribution 3.0 United States License <http://creativecommons.org/licenses/by/3.0/us/> . To provide attribution, please cite to "ZL Technologies, Inc. (http://www.zlti.com)."
***********
California


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