I am a highly proficient Technologist with over 12+ years commercial experience in designing (Cloud & On-Prem), building and managing global DevOps projects for major blue-chip clients.
Excellent architecture, management, technical skills and experience using various tools, technologies, and techniques.
Exemplary skills and capabilities in designing and delivering Enterprise-wide DevOps & Container orchestration Programmes using agile software development programmes and initiatives, with experience driving the adoption of new technology capabilities at the enterprise level.
Pioneer in Designing CI/CD DevOps practices and engineering native cloud applications.
Having hands on experience in Deploying CI/CD Systems(Jenkins, Bamboo), Binary management(Artifactory, Nexus) and ALM Tools(Jira,Confluence, Bitbucket) and etc.
In addition to my technical expertise, I possess impeccable written and verbal communication skills, excellent interpersonal skills, and outstanding management and problem-solving abilities.
With a strong background in Product Management and Project Management, I have successfully led cross-functional teams, delivering projects within scope, timeline, and budget.
I have a proven ability to coach and mentor team members, fostering their professional growth and driving high performance.
My strong leadership and people management skills have enabled me to effectively lead and collaborate with diverse teams, ensuring a cohesive and productive work environment."
✔ Led the automation initiatives within the team, driving the implementation of automation tools and processes to reduce manual work and streamline operations.
✔ Led a team of DevOps professionals as the Interim Manager, overseeing the Enterprise DevOps platforms and ensuring their smooth operation.
✔ Successfully managed and coordinated a global team, including the Mexico Team, in implementing standardized processes and ensuring consistent delivery of DevOps services.
✔ Successfully upgraded multiple platforms with a user base of over 25K, ensuring minimal disruption and enhanced performance.
✔ Demonstrated exceptional leadership during platform crises, effectively resolving issues and minimizing downtime to ensure business continuity.
✔ Mentored and coached team members, fostering a culture of continuous learning and growth, resulting in improved performance and morale.
✔ Applied comprehensive knowledge of product management and project management methodologies to drive successful project delivery within scope, timeline, and budget.
✔ Spearheaded the design and implementation of CI/CD systems (Jenkins, Bamboo), binary management tools (Artifactory, Nexus), and ALM tools (Jira, Confluence, Bitbucket), enabling streamlined software development and release processes.
✔ Played a pivotal role in the adoption of agile software development practices, driving the implementation of DevOps best practices and ensuring successful transformation at the enterprise level.
✔ Leveraged expertise in cloud technologies, deploying cloud-native applications and optimizing cloud platforms (AWS, Azure, GCP) for efficient and scalable operations.
✔ Spearheaded the development and implementation of a Configuration Management Database (CMDB), enabling effective tracking and management of infrastructure and application assets.
✔ Promoted clear documentation practices using FAIR (Findable, Accessible, Interoperable, and Reusable) principles, ensuring that documentation was comprehensive, well-organized, and easily accessible to the team.
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✔ Actively participated in change management activities, ensuring smooth transitions during software releases and minimizing the impact on end-users.
✔ Fostered a culture of continuous improvement by encouraging and implementing feedback loops, driving innovation and optimizing processes.
✔ Acted as a key liaison with stakeholders, providing regular updates on project status, risks, and mitigation plans.
✔ Demonstrated excellent problem-solving and communication skills, collaborating effectively with cross-functional teams and stakeholders to achieve project goals and deliver high-quality solutions.
✔ Proactively identified opportunities for process improvements and implemented measures to enhance operational efficiency, resulting in cost savings and increased productivity.
✔ Led the adoption of GitHub Enterprise at scale as the Github Product Lead, driving the implementation of the platform across the organization and ensuring its successful integration into existing workflows.
✔ Designed and delivered end-to-end automation for the migration of Bitbucket repositories to GitHub, including stakeholder management, approvals, and permissions replication using issue ops methodologies.
✔ Served as the DevOps Technical Lead, re-engineering and streamlining existing DevOps services to improve efficiency and effectiveness.
✔ Acted as the DevSecOps Community Lead, enforcing Static Application Security Testing (SAST) and vulnerability scans to ensure the security and integrity of software deliverables.
✔ Successfully managed operational services as an Operational Service Manager, ensuring adherence to best practices and procedures for smooth operations.
✔ Led DevOps Steering Groups, driving collaboration and decision-making on DevOps initiatives and strategies.
✔ Developed and implemented robust processes for continuous integration, continuous delivery, and continuous deployment (CI/CD) to accelerate software delivery and increase release frequency.
✔ Championed the implementation of infrastructure-as-code (IaC) practices, leveraging tools such as Terraform and Ansible to automate infrastructure provisioning and configuration management.
✔ Collaborated with cross-functional teams to identify and implement improvements in application performance, scalability, and reliability, resulting in enhanced user experience and reduced downtime.
✔ Actively participated in capacity planning and scalability initiatives, ensuring infrastructure resources were optimized to meet growing demands.
✔ Implemented monitoring and alerting systems to proactively identify and resolve issues, minimizing impact on end-users and improving system availability.
✔ Fostered a culture of continuous learning and knowledge sharing, organizing workshops and training sessions to enhance team members' skills and expertise in DevOps practices and tools.
✔ Received recognition for outstanding performance and contributions to achieving organizational goals.
✔ As a Technical Lead, Defining the DevOps strategy for the organization and creating(implementing)
the DevOps platform for enabling DevOps practices for various technology stacks.
✔ Designing and Implementing the end to end ALM Solutions( Bitbucket, Jira, Confluence, and Crowd ).
✔ Designing and Implementing the end to end Continous Integration (CI) and
Continous Deployment (CD) platform using Jenkins, Sonarqube and Nexus.
✔ Designing and Implementing the container orchestration platform based on Kubernetes.
✔ Driving the key projects in the organization towards the Containers.
✔ Designed created and delivered a multi-cloud native container DevOps (PAAS)
platform for application development and deployment resulting in the
streamlining of application delivery for the enterprise. The platform uses Docker
as the container technology and deployed onto Kubernetes on AWS and
VMware with a switch that can be used to deploy to either location. The platform
has the end to end CI/CD pipeline, centralized logging and monitoring with
target availability of 99.99%.
✔ Designed created and delivered an Enterprise Level CI/CD Environment using
Jenkins, Artifactory, SonarQube, and other tools with failover mechanism
achieved using Terraform
✔ Designed and delivered enterprise-wide CI/CD Pipelines for Spring boot, Java,
Python, Node JS, PHP, and Html applications using Packer and Artifactory
tools.
✔ Designed end to end CI/CD Pipelines for Mule Soft Api Deployments.
✔ Defined delivered and championed the adoption of Enterprise-wide Application
Lifecycle Management Tooling (ALM) based on Jira, Confluence, Bitbucket (Git).
Delivered this to over 8,000 users within IT and the business resulting in
operational efficiency and project delivery transparency including using Jira for
delivering validated (GxP) projects.
✔ Independently designed and executed company catalog for DevOps support and
development.
✔ Designed DevOps master page and page layouts, serving as company's main
DevOps support for all technical complications.
✔ Travelled to Mexico and provided in-person training for global team members &
development teams for seamless development and deployments of enterprise
applications.
✔ Leading the enterprise Continuous Integration practice. Providing advisory
services in enterprise continuous Integration, Continuous Delivery to key
projects around the organization. Collecting and documenting experiences in the
internal DevOps COE.
✔ I was part of the R&D team to find the best CI/CD tool of organization. InR&D, I & my team evaluated various tools such as Jenkins, Bamboo, Thoughtworks GO, UDeploy, and other tools. ✔ I was part of the Cloud Smurfs team, where I was primarily worked for designing and delivering internal Cloud (VMware Based Cloud) & Docker Platform for different regions of the organization. ✔ I was designed CI/CD Pipelines for Projects which have Continuous Integration, Continuous Testing, Code Quality, Continuous Deployment, and Continous Security Testing Phases. ✔ I have been part of the team which delivered organization-wide discussion forum using Discouse.io. ✔ I was administrated Continuous Integration(Jenkins), Source Code Management(Bitbucket & Gitolite), Binary management Systems(Artifactory), Code Quality management Systems(Sonarqube). ✔ I was designed and delivered Jenkins pipeline for the integration of Selenium Framework in the CI/CD Pipelines. ✔ I was designed and implemented best Source code branching strategies (Bitbucket and Gitolite) to enable parallel development for multiple releases. ✔ I was provided training and support different employees in source control, build automation, merge resolution, CI, test automation. ✔ I was taken care of backup, restore, mirroring of the codebase to multiple locations according to policies and standards. ✔ I was developed, tested, and implemented necessary automation scripts on need-based. ✔ I was Written, distributed, and maintained the required technical documents according to policies and standards.
| Expertise Area | Tool Name |
|---|---|
| DevOps Stack | Jenkins, Docker, Packer, Terraform, Bamboo, Artifactory, Nexus, Sonarqube |
| Container Orchestration | Kubernetes(On-Prem, Kubeadm, Kops, EKS, GCP),Nomad,Openshift |
| Cloud Technologies | AWS,GCP,Heroku |
| CI/CD | Jenkins,Bamboo |
| Programming Skills | Python |
| Build Tools | Maven, Gradle |
| Configuration Management | Ansible,Chef |
| Containers/AMI/Images Creation | Packer,Dockerfile |
| IaC | Terraform,CloudFormation |
| Container Technology | Docker |
| Secret Management | HashiCorp Vault, Azure Key Vault |
| ALM/SCM/DCM | Jira, Rally,Git( Bitbucket, Github, Gitlab), Confluence |
| Binary management/Container Registry | Artifactory, Nexus, Gitlab,Docker Hub |
| Code Quality | Sonarqube |
| Service Discovery | ETCD, Consul |
| Service Mesh | Istio |
| Container Networking | Contiv, Flannel, and Calico |
| Scripting | Bash/Shell Scripting |
| OS | Linux/Unix |
| Web Servers/ Application Servers | Nginx, Apche Tomcat |
| Logging Solutions | ELK, Zabbix, Sumologic,Wavefront |
GPA: 6.80
GPA: 9.13


The Kaldi keyword search system is based mostly on a conventional LVCSR pipeline. We have three main sub-systems, which separately decode the data; we then use conventional system combination techniques. The four systems are:
Our keyword search pipeline is based on lattice-indexing as described in [5]; the lattices are generated using the "exact" lattice generation method described in [6]. To handle out of vocabulary (OOV) keywords, we use the method [4] which constructs for an OOV keyword sequence, proxy keyword sequences consisting of word sequences which are phonetically similar. This year we added a "lexicon expansion" method, in which we generate plausible new words using a syllable-level language model and add them to the lexicon and language model when decoding (see Section 4.4). (This even slightly improves the WER). We actually add the original and expanded-lexicon versions of each system to the final system combination, but including non-expanded decodings in the system combination is not really necessary.
The code and scripts used for the main Kaldi system are available as part of Kaldi; see svn://svn.code.sf.net/p/kaldi/code/trunk/. The scripts we used this year are located in the directory egs/babel/s5b.
Something else that is new is the p-norm neural networks [8]. This is a new nonlinearity type that is related to maxout (in that it is a dimension-reducing nonlinearity). This gave us around 1% absolute improvement compared with our old, tanh-based networks. On top of this, for LimitedLP we introduce an ensemble training method . Imagine training four networks from different random seeds. We can average the scores from all of them to get an improvement (around 2% absolute). But we don't like to have to use multiple networks in test time. Our ensemble method introduces a term in the objective function to train the networks' outputs towards each other, to make them more similar, so that in test time we can pick just one of the networks to test with. This gives us three quarters of the improvement from the simple method of averaging the scores, but does not slow us down in test time. We only do this for limitedLP because it slows down training too much to be practical for FullLP.
Our bottleneck feature system is heavily modified since last year, and has improved. Firstly, we implemented it all in Kaldi, as opposed to last year's system which was a hybrid between Kaldi and Theano. This makes the training faster, since Kaldi supports parallelized neural network training, using multiple GPUs. The basic recipe is basically the same as last year-- a DNN with a 42-dimensional bottleneck, appending these features with the baseline fMLLR features, splicing across 3 frames and doing LDA dimension reduction to 60 dimensions, then training an SGMM system on these features. However, results seemed a little better with the Kaldi implementation, perhaps 0.5\% absolute. It's hard to say why, as there are too many differences. The thing that is new is that we implemented semi-supervised training in the LimitedLP case. We use the 1-best output from decoding as supervision for the untranscribed data, but only train on a frame if the state-level posterior is above a threshold (we use a low threshold of 0.35 for this case).
Our point process model system ( Section 4.16), while it get only around half the ATWV of our conventional system by itself, is giving us large improvements in combination with our conventional system, of around 3 to 4% ATWV. This is an unconventional "exemplar-based" approach.
Our expanded lexicon (Section 4.4) also new. This method takes as input the provided lexicon, and uses it to hypothesize likely new words and their pronuciations, along with their probabilities. We generate 2 million extra words, with associated probabilities, and we allocate the "unknown-word" probability mass of our language model to these words. Our method is "backwards", in that we first generate the phonetic sequences, and then work out the spellings. The improvement this gives is extremely variable. For Bengali and Assamese, it makes essentialy no difference. But for Zulu LimitedLP using the development keywords on the development data, it improved the Kaldi-only ATWV from 0.20 to 0.28.
The output of the decoding above is used as the input to the following algorithm. First we map the frames of the decoder best path to one of three classes: speech, noise or silence. The segmentation algorithm is as follows:
k a t _1
would become the phone sequence k_1 a_1 t_1
Formally, each tone version of a phone is a separate phone, but see
our explanation of contenxt dependency below .
We noticed that in some languages, the original lexicon seemed to have been expanded
with some kind of script where some original phone was mapped to two alternative
phones. That was the case for Vietnamese last year and Zulu this year, and it
was helpful to reverse this mapping. Our mapping for Zulu is as follows:
| k_> | g_< |
| 3 | e |
| R | l |
| o | O |
| b_< | b |
| t_> | th |
We have four phones in our inventory apart from those that appear in words; they are all modeled in a context independent way and using a different topology (5 states, where the middle 3 states all have transitions to each other). These are for silence, noise, vocalized-noise and unknown-words. The difference between vocalized noise and unknown-words is that vocalized noise models things like coughs and laughs, whereas the unknown-word phone models words whose pronunciation is not known (mainly so we can align them during training).
The lexicon expansion procedure works as follows (but note that lexicon expansion is not the only thing we do to handle OOVs; see also Section 4.15). We first take all the entries in our original lexicon and view them as sentences, where each syllable corresponds to one symbol (we ignore the spelling). We train an ARPA language model on this with SRILM; a 3-gram "modified Kneser-Ney with interpolation" seemed to work the best. We then generate a large number of "sentences" from this language model: 20 million or so, For each unique sentence in the generated sentences, we compute its language model probability; we then exclude the sentences that correspond to words in the original lexicon, take the 2 million best ones, and these will become the pronunciations of our lexicon entries.
A lexicon entry needs a spelling as well as a pronunciation, and to do this we
use the g2p tool from Sequitur in reverse to produce the most likely
spellings for each pronunciation. We reverse it by taking each lexicon entry,
e.g.
hi h iy
and reversing it to produce something like
hiy h i
Actually we don't do it exactly this way because we want iy
to appear as a single
symbol on the left, rather than as a sequence of two symbols. So we map the phones
to ASCII symbols first. When doing so we treat tags (e.g. tones) separately, so each tag
has its own ASCII symbol, and a phone with a tag would be rendered as two ASCII symbols.
We use g2p to generate a list of the top few likely spellings for each of the generated pronunciations. We take the pronunciations we generated and the probabilities of their spellings, and convert them into a list of words with probabilities on the words, and a list of pronunciations for each word with asociated pronunciation probabilities. This is the output of the lexicon expansion and it is used to create the lexicon and language model that we decode with.
We ran two versions of each system, one with and one without the lexicon expansion, because we wanted to see how much effect it was having. Because we had them both available, we decided to combine both versions for the final system combination, but this combination made very little difference to the results and we could equally well have submitted just the expanded-lexicon systems.
The main tunable parameters of the SGMM training are given below:
| Num-gauss-UBM | Num-leaves | Num-substates | |
|---|---|---|---|
| LimitedLP | 750 | 5000 | 18000 |
| FullLP | 800 | 10000 | 80000 |
During decoding, we pick just one of the systems arbitrarily. Since it has been trained towards the other networks, it acts a little bit like an ensemble of networks, even though it is just one network. This gives us about 1.5% WER improvement.
For feature extraction we remove the part after the 42-dimensional bottleneck, including the tanh nonlinearity, and append it with the baseline 40-dimensional features, giving an 82-dimensional feature vector. This is appended across ±1 frame and the dimension is reduced with LDA to 60 dimensions. (Note: we don't commence training on these features from scratch but start with alignments from our SAT-trained GMM-based system).
From this point we train an SGMM+BMMI system. Because the feature dimension is higher the number of parameters would increase if we left the rest of the configuration of the system the same, so we use the following reduced configuration values:
| Num-gauss-UBM | Num-leaves | Num-substates | |
|---|---|---|---|
| LimitedLP | 500 | 5000 | 10000 |
| FullLP | 5500 | 10000 | 50000 |
| Stage | Num-leaves/gauss | Num-leaves/gauss | Feature type |
|---|---|---|---|
| (LimitedLP) | (FullLP) | ||
| mono | n/a | n/a | delta+delta-delta |
| tri1 | 1000/10k | 1000/10k | delta+delta-delta |
| tri2 | 2500/36k | 1000/20k | delta+delta-delta |
| tri3 | 2500/36k | 6000/75k | delta+delta-delta |
| tri4 | 2500/36k | 6000/75k | LDA+STC |
| tri5 | 2500/36k | 6000/75k | LDA+STC+fMLLR |
The lattices from the speaker-independent decoding are used with the speaker-adapted "tri5" model to compute initial fMLLR transforms, which are used with the speaker-adapted model to rescore the lattices to get better posteriors and estimate the fMLLR transforms a second time. Then another lattice generation pass is done with the speaker-adapted model and adapted features, and the fMLLR transforms are estimated a third time and the lattices rescored with those features.
Note: we don't include silence frames in the fMLLR computation. Since the lattice generates soft counts, this is accomplished via per-frame weights, not a hard cutoff.
The decoding of the later models-- the SGMMs, DNNs and bottleneck feature based SGMMs-- all depend on the "tri5" decoding because they use the fMLLR transforms generated there.
Once we have these transforms, the DNN decoding is single-pass, but for the discriminatively trained DNNs we first decode with the basic DNN and then rescore the lattices with four different versions of the final DNN system, one for each epoch. This is so that we can choose the best epoch to use.
The SGMM decoding naturally has two passes: one using a speaker-independent version of the SGMM system (speaker-independent because it doesn't have speaker vectors, although we do have fMLLR features), and then another pass of decoding after estimating the speaker vectors. However, we only generate the lattice once. In order to ensure an accurate final lattice, we dump the state-level lattice from the first pass of decoding and don't do the final lattice-determinization until after estimating the speaker vectors. See [6] if the term "state-level lattice" is confusing.
We build a separate index for each language model scale in a predetermined range (e.g. 10, 12, 13, 14, 15), so that we can separately run the keyword search pipeline for each scale, and pick the scale with the best ATWV on the dev data. (Note: since there is only one dev set, all our numbers reported on the dev set have these scales optimized on that set, and the same applies for WER numbers).
The most non-obvious aspect of this is the per-keyword normalization of the scores. The Term Weighted Value (TWV) metric, after ignoring constant terms and doing a few manipulations, may be expressed as follows:
TWV = const + sum-over-keywords ( 1/K ( Ntrue-hit / Ntrue - beta/duration NFA ) )
Here, sum-over-keywords is taken over all keywords that were actually seen in the test set being considered. The values in the equation may be defined as follows:
| Name | Definition |
|---|---|
| K | Number of keywords that appear in this test set |
| Ntrue-hit | Number of occurrences of this keyword that we correctly spotted. |
| Ntrue | Number of times this keyword actually occurred in this test set. |
| NFA | Number of incorrect hits of this keyword that we produced. |
| beta | A constant equal to exactly 999.9 (don't ask) |
| duration | The total number of seconds of audio in the test set: a constant we know exactly. |
TWV-contribution = 1/K ( Ntrue-hit / Ntrue-estimate - beta/duration NFA )
Here, Ntrue-estimate and beta/duration are both known quantities. Consider one putative hit, i.e. one location in time where we have a nonzero posterior and we might want to produce a hit. Let the posterior of the keyword in the lattice be p. Let's assume that p is a reasonable estimate of the probability that the keyword actually exists there, which is reasonable assuming model correctness. As an aside, note that we scale down the acoustics in our lattices while computing the posteriors, so the probabilities are quite well calibrated; also, we have plotted the (posterior in our lattice) versus (probability that the word was actually there) and it's within spitting distance of a straight line. Anyway, back to the task at hand. We can write, for this putative hit,
expected-TWV-contribution = 1/K ( p / Ntrue-estimate - beta/duration (1-p) ) .
Here, all but one of the quantities in the equation are known. K is not known, because we don't know how many keywords were actually seen in the test set, but because we only care about the sign of this quantity we don't actually need to know K. For a putative hit, the equation above gives us all we need to know in order to know whether to say "yes" or "no": if it's positive, "yes", else "no". We want to keep the hit if this is positive, i.e. if.
p / Ntrue-estimate - beta/duration (1-p) > 0
p (1/Ntrue-estimate + beta/duration) - beta/duration > 0
p > (beta/duration) / (1/Ntrue-estimate + beta/duration)
p > Ntrue-estimate / (duration/beta + Ntrue-estimate)
Let's call the value above the "threshold", i.e.
threshold = Ntrue-estimate / (duration/beta + Ntrue-estimate)
(there is a different threshold for each keyword). In order to make it easier to choose
the cutoff point for when to stop producing hits, we would to produce the output
as normalized scores that are all somehow comparable to each other. That way we can tune a global threshold.
We would like to normalize our scores in such a way that they are still all between zero and one.
We do this by converting p to a log-ratio, i.e. q = log(p / (1-p)), computing a similar log-ratio for the
threshold, i.e. t = log(threshold / (1-threshold)), and then subtracting t from q,
i.e. q' = q - t, to produce a normalized log-ratio q' (so if q' > 0, then p > threshold).
Then we convert back from a log-ratio to an actual
probability, call this p'. When we work out the equations for this, it comes out to
p' = (1-threshold) * p / ((1-threshold)*p + (1-p)*threshold)
We have described our basic proxy search procedure in [4] so we will not repeat it at length here. The basic idea is to use a learned phone confusion matrix to find a list of in-vocabulary word sequences that are phonetically close to the sequence we want, with associated penalties for being too distant. As a special case, we don't penalize the proxy sequences for having extra phones at their beginning and end (so, for instance, if the pronunciation of a searched-for word appeared as part of a longer word, we would allow that without penalty).
As background, our index lookup is actually done by FST composition, where one of the things to be composed is the "query FST" (normally with a linear structure) and one is the huge index. In our proxy search method, we represent the set of proxy keywords, and their associated weights, as an FST, and to the keyword search pipeline it looks no different from a linear sequence (since the input is just an FST).
There is something new about our proxy keyword search pipeline this year. After implementing the "expanded lexicon", we noticed that the process of generating proxy keywords was very slow. This procedure involves various operations of composition and determinization, where the inputs are a linear sequence consisting of the OOV keyword (as phones), a phone-edit-distance FST, and a lexicon. When we made the lexicon much bigger, it became slow. In order to make it fast again, we had to rearrange the order of composition and determinization, and implement an "on-demand" FST pruning procedure for OpenFST (as part of the Kaldi extensions to OpenFST).
The point process model (PPM) for keyword search [9] is a whole-word, event-based acoustic modeling and phonetic search technique. It operates on sparse phonetic event streams extracted from the speech signal using a frame-level subword acoustic model. In our Babel system, we use our Kaldi Deep Neural Network acoustic models described above to generate posteriorgrams over context-dependent states. We subsequently sum posterior dimensions sharing the same center phone to produce monophone posteriorgrams for each utterance. After applying the matched filter smoothing of [10], local maxima of each posterior trajectory define phonetic event times. The set of phonetic events for the search collection defines the index for subsequent keyword search; this construction, which is performed entirely independent of the keyword set, is our only use of the test audio.
The next stage is point process model construction. For
in-vocabulary words, we perform MAP estimation of the Poisson rate
parameters for each word in the lexicon [11]. This takes advantage of
any exemplars present in the training data, but falls back on
dictionary-based model priors (the simple variant, see [11] for details)
if no exemplars are available. For OOV keywords, we use Sequitur G2P
pronunciations to construct the dictionary models. Multi-word keyword
models are constructed by concatenating MAP estimated unigram PPMs, with
the overall duration distributions derived using the Monte Carlo
techniques from [12]. Search for each keyword is performed using an
optimized detection function calculation scheme that is 500,000 times
faster than realtime. We consider the PPM system performance both in
isolation and in combination (at the kwslist level) with the Kaldi LVCSR
search engine outputs.
Our systems are not cross-adapted, unless you count the fact that they all use the fMLLR transforms from the shared "tri5" stage. For transcription purposes, the only form of combination we use in the Kaldi sub-system is a combination procedure based on Minimum Bayes Risk decoding, as described in [1]. We view this as a more principled way to do confusion network combination (CNC) [18], without the various heuristics that are used to produce confusion networks. There is one aspect of this that we should explain, which relates to the language-model weight. Normally when decoding, we do a linear sweep over the language model weights over some range (e.g. 10, 11, 12, ... 18), and select the best one. We do the same when combining systems, except that sometimes the different systems will require substantially different language model weights and there is no one weight that is good for all of them; it's not practical to try all possible combinations of weights. When combining systems, we apply a different offset to the language-model weights for each system. This offset is determined by the beginning of the language-model-weight range that we sweep for each system, which in turn was determined by us when setting up the configuration files for our system. So for instance, if we start the regular SGMM system at offset 10, and the bottleneck+SGMM system at 15, then there would be an offset of 5 between the two systems when we do the combination.
We don't bother applying weights to the different systems when combining, but on occasion we do leave out some of the worse systems from the combination. This is decided by a human operator, based on trying different combinations on the dev set. The identities of the systems that were combined will be noted in the individual submissions.
The Kaldi-internal combination for keyword search is based on averaging across systems the unnormalized putative hits (i.e. the lattice posteriors extracted from the index), before normalizing the averaged posteriors using the normalization method described in Section 4.14.. Note that in order to do this averaging, we have to have some notion of when multiple hits are "at the same time". This is pretty obvious (hits are the same if they overlap in time), so we won't refer to it further. If one system did not have a hit at a particular time, it's identical to it having a posterior of zero.
We do not do a conventional average (i.e. a mean).
We wanted to implement something that was in between a mean and a geometric mean. We
used the notion that a geometric mean is a mean of logs, and a log is like a power of
x, (1/p) xp, as p approaches zero. So if we take the mean of xp
for some power p between zero and one, and take the result to the power 1/p,
this is somewhere between a mean and a geometric mean. So this is what we do.
Suppose we have three scores: a, b and c. We choose a power p (say, p=0.5, but it's tuned
per language). Then we let
average = (ap + bp + cp)1/p .
Actually we extend this to a weighted average, i.e.
average = (waap + wbbp + wccp)1/p
where the weights sum to one. The weights are determined manually in small scale
experiments on one of the languages, as the result is not very sensitive to the
weights. We used weights that are fairly close to each other, but with better
systems having larger weights.
We apply the normalization method of Section 4.14. after taking the weighted mean.
DATADEF:==BaseLR{204LimitedLP}:AM{204LimitedLP},LM{204LimitedLP},PRON{204LimitedLP},AR{None}
Ingestion Elapsed Time (hh:mm:ss) - 151:29:03 Ingestion Total CPU Time (hh:mm:ss) - 9546:33:38 Ingestion Total GPU Time (hh:mm:ss) - 92:23:16 Ingestion Maximum CPU Memory (gbytes) - 192 Ingestion Maximum GPU Memory (gbytes) - 16 Search Elapsed Time (hh:mm:ss) - 12:39:08 Search Total CPU Time (hh:mm:ss) - 427:17:22 Search Total GPU Time (hh:mm:ss) - 0:00:00 Search Maximum CPU Memory (gbytes) - 32 Search Maximum GPU Memory (gbytes) - 16


I recently penned the following for my local
No2ID mailing list (the Camden & Islington
branches)...
For what it's worth, I worked briefly on one of the failed Government IT
projects mentioned in the Guardian
article
referenced on this list a couple of days back. I just wanted to share a
brief perspective of that, in case it's useful or interesting for anyone
contemplating the possible woes of the Government's national ID card and
database projects...
The consultants on projects like this have tailored their behaviour to
maximise profits in the environment they find themselves in. That is to
be expected - so long as they operate within the law they can be neither
blamed for that nor expected to change of their own accord.
The stratagems they use to maximise profits include a thorough sabotage
of project technical deliverables, such that projects appear to make
progress towards achievable goals, thus avoiding cancellation, but in
actuality will require endless rounds of deep rework and extra funding
in order to deliver a system actually does anything useful.
Every extra pound spent in this way is an extra pound billed to the
client (the Government), and every pound billed earns markups and
ongoing overheads. So for the contractors, the way to make the most
money is to drag the project on for as long possible, spending as much
money as they can along the way, without ever actually delivering
anything that works. The results of incentivising contractors this way
speak for themselves.
Of course, if such abuses were too explicit, that would give the client
a means to sue and wriggle out of contracts, jeopardising future income.
Fortunately for the contractors, the process of producing large (or even
medium-sized) IT systems is difficult and murky enough for the desired
disruption to be practically self-organising, given the correct
environment for incubation. Badly-judged technical architecture,
addiction to proprietary standards, wishy-washy technical leadership,
and good old-fashioned hiding of incompetence using imaginative
misrepresentation, these will pretty much do the job. Black-ops meetings
in smoke-filled rooms are undoubtedly only used as a last resort, and
did not, during the period of my employment, produce any requirement to
directly solicit the services of my own pay grade as a saboteur.
Presumably this state of affairs persists because Government actors are
not being held accountable for the success or otherwise of projects
under their control. If that were to change, then the Government could
possibly improve the situation by lowering the barriers to entry for
competing IT consultants and contractors, and showing some negotiating
guts to restructure the way contractors are rewarded, thus introducing
some real competition amongst technology providers to deliver something
that actually works.
That this hasn't already happened says to me that Government actors are
ensnared in some sort of corruption - by which I don't necessarily mean
an explicitly dishonest kind. The situation could charitably be
interpreted to include what I term 'passive
corruption' - the kind of
poorly aligned incentives or incompetence which leads to similar
outcomes as traditional corruption. (For more thoughts on how passive
corruption of this kind affects society, see the excellent writings of
Lawrence Lessig.)
While it's in all our interests for this situation to get fixed in order
to improve our Government's effectiveness, I see no hope that this will
happen soon. Fortunately for No2ID this sad state of affairs provides us
with the ammunition that the ID card and database projects are unlikely
to work well, and are likely to cost significantly more than current
projections, which I agree should be exploited as has been outlined
adroitly by others, on this list and elsewhere.


Every Second Is Valuable
Find Your Current Location and Nearby Mosque and Madrasha
Find Your Location
Find Madrasha
Find Your Nearby Mosque
Search Mosque
Quiz On General knowledge About Islam
Better understending of Islam
Can choice level
Question will appeared Randomly
Learn Quran Lession without any cost, just need your on desire
Lession will be webcam
Lession will be sometime video record
Limited Student with limited time
All rights reserved by Atiqur Rahman Pranto; 2021


Get Stag Leap Contemporary Dance Pictures. (though the timing of this second one isn't the best, her front leg should be higher). Dancing+tumbler | contemporary #dance #dancer #friend #stag leap #werk #inspirations.
Requirements remember practice makes perfect and in to time you'll be able to coordinate enough to do a stag leap. Последние твиты от stag leap (@stagleap). Jazz dance instructor, roger bennett riggle, demonstrates advanced jazz dancing including how to do a stag leap.
Some dancers prefer to start with both.
I learned how to do them in jazz class. It has a more pedestrian feel of movement while using shapes, space and levels incorporated into it's choreography. Stag leap is located ten minutes from the downtown brick streets of nacogdoches and quaint antique shops and museums galore. I learned how to do them in jazz class.


| label_number_auto {scales} | R Documentation |
Switches between number_format()
and scientific_format()
based on a set of
heuristics designed to automatically generate useful labels across a wide
range of inputs
label_number_auto()
Other labels for continuous scales:
label_bytes()
,
label_dollar()
,
label_number_si()
,
label_ordinal()
,
label_parse()
,
label_percent()
,
label_pvalue()
,
label_scientific()
# Very small and very large numbers get scientific notation demo_continuous(c(0, 1e-6), labels = label_number_auto()) demo_continuous(c(0, 1e9), labels = label_number_auto()) # Other ranges get the numbers printed in full demo_continuous(c(0, 1e-3), labels = label_number_auto()) demo_continuous(c(0, 1), labels = label_number_auto()) demo_continuous(c(0, 1e3), labels = label_number_auto()) demo_continuous(c(0, 1e6), labels = label_number_auto()) # Transformation is applied individually so you get as little # scientific notation as possible demo_log10(c(1, 1e7), labels = label_number_auto())


Lesson 1: Making Your First Viz¶
I believe that in order to firmly grasp a concept, it requires some application.
HR has provided me with some sample data that many of you will, or have worked with. I’m going to build some tasks for your to try on your own using the skills you learned during the class.
Exercise 1: Connecting to the data and asking questions¶
I’ll provide you with the sample data given to me by one of your colleagues. We will be using the excel spreadsheet named compensation.xlsx
.
Please download it at the link below stored on my google drive here.
Connect Tableau to the
compensation
excel sheet.Convert the
CTC
column to a number.Go to sheet 1. Did tableau generate any columns that were not there before? If so, which ones?
How many geographical dimensions are there?
How many date measures are there?
Are there any questions that you have from this dataset? Write down a few questions you want to explore in the data.
Exercise 2: Making a Bar Plot¶
We want to know how many employees there are in each country. Use the appropriate columns to generate a bar plot.
Sort these values in descending order.
Which country has the largest number of employees?
Which country has the least amount of employees?
Can you transpose your plot?
Change the colour of your plot to a colour of your choice.
Try decreasing the width of the bars.
Exercise 3: Map¶
Make a new worksheet.
Create a world map using the latitude and longitude fields.
We want to now explore what is the AVERAGE CTC is of each country. Do some countries have higher a higher CTC value than others? We are first going to have to drag the
Country
column to theDetail
option on the Marks card. Without doing this, Tableau will not know to give an average to any of the countries since that field is not used in the plot!Let’s specify AVERAGE CTC by colour. Drag this column to the Color Mark.
Is this giving the average value? Can you change it to reflect the average stip percentage now?
Which countries have higher average CTC values?


A TIME TO KILL A film review by Scott Renshaw Copyright 1996 Scott Renshaw
Starring: Matthew McConaughey, Sandra Bullock, Samuel L. Jackson, Kevin Spacey, Brenda Fricker, Oliver Platt, Charles S. Dutton, Ashley Judd. Screenplay: Akiva Goldsman. Director: Joel Schumacher. Reviewed by Scott Renshaw. Opens Wednesday, July 24 in wide release.
This just in: Matthew McConaughey has already won the 1997 Best Actor Oscar, will be on the cover of every magazine published in the English language, and has been named Time's Man of the Year. Those suggestions should be absurd, but the way Warner Bros. is pushing John Grisham's anointed one, they're merely the next logical step. It's one thing to be the Flavor of the Month, but McConaughey has become the Flavor of Next Month, a star without a previous starring role.  Leading an exceptional cast, Matthew McConaughey helps create some riveting moments in an occasionally melodramatic story.
McConaughey plays Jake Brigance, a young lawyer in Canton, Mississippi struggling to keep his practice afloat. With bills to pay and a family to support, Jake needs a big case, and he is about to find one.   Jake takes the case, assisted only by law student Ellen Roark (Sandra Bullock), but a lack of resources may be the least of his problems when the brother of one of the murdered men (Kiefer Sutherland) sets out to teach Jake -- and anyone close to him -- a lesson.
 White supremacists are the Nazis of the 90s; film makers love them because they know we love to hate them, and because they don't have to worry about anyone protesting if they are portrayed as hissable cartoons. But somehow it detracts from the racial tensions at the heart of A TIME TO KILL to have cross-burning, linen-wearing good ol' boys at the center of them. There are plenty of complex issues involved in the story of A TIME TO KILL, foremost among them the criminal justice system's entrenched racial inequities, but director Joel Schumacher and his frequent screenwriting collaborator Akiva Goldsman can't resist the impulse to be crowd-pleasers, providing plenty of moments for audience members to applaud Klansmen being beaten, spit upon, arrested or otherwise mocked, all underscored by Elliot Goldenthal's strident strings.
  Goldsman gives him a great speech late in the film in which he forces Jake to confront his own racist feelings, and what makes Jackson's delivery so potent is that there is so little anger in it; he has come to an understanding of the gulf race places between them. Sandra Bullock, given an extremely misleading first billing, turns in what may be her best work yet as the cocky legal prodigy who smells a ticket to notoriety in the much-publicized case, and there are memorable supporting turns by Oliver Platt (as a lovably unscrupulous friend and colleague of Jake's) and Chris Cooper (as a sheriff's deputy caught in Carl Lee's line of fire) among a handful of others.
Then there is Matthew McConaughey. He is being compared to everyone from Paul Newman to Marlon Brando to Kevin Costner, but none of those comparisons really does justice to his unique appeal. There is an absolute ease to his acting which you almost never see in actors his age (McConaughey is a ripe old 26), along with a screen presence you rarely see in actors of any age. His Jake is a bit of an idealist and a bit of a scoundrel; it's hard to get inside his head, but it's easy to like him. And in a film which features a few too many loud and obvious scenes, McConaughey is absolutely electrifying in a scene which is emotionally crippling in its quiet power: Jake describing the rape of Carl Lee's daughter to the jury in graphic detail. It is almost possible to accept that Spacey's savvy prosecutor doesn't object to the obviously inflammatory remarks. Perhaps he, like us, was simply transfixed by witnessing the birth of a star.
On the Renshaw scale of 0 to 10 opening statements: 7.
-- Scott Renshaw Stanford University http://www-leland.stanford.edu/~srenshaw
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


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--- layout: publication_data permalink: /Research/Data/2017Zdraljevic/ stub: 2017Zdraljevic subtitle: Zdraljevic et al. ---
Many chemotherapeutic drugs are differentially effective from one patient to the next. Understanding the causes of this variability is a critical step towards the development of personalized treatments and improvements to existing medications. Here, we investigate sensitivity to a group of anti-neoplastic drugs that target topoisomerase II using the model organism Caenorhabditis elegans. We show that wild strains of C. elegans vary in their sensitivity to these drugs, and we use an unbiased genetic approach to demonstrate that this natural variation is explained by a methionine-to-glutamine substitution in topoisomerase II (TOP-2). The presence of a non-polar methionine at this residue increases hydrophobic interactions between TOP-2 and its poison etoposide, as compared to a polar glutamine. We hypothesize that this stabilizing interaction results in increased genomic instability in strains that contain a methionine residue. The residue affected by this substitution is conserved from yeast to humans and is one of the few differences between the two human topoisomerase II isoforms (methionine in hTOPIIα and glutamine in hTOPIIβ). We go on to show that this amino acid difference between the two human topoisomerase isoforms influences cytotoxicity of topoisomerase II poisons in human cell lines. These results explain why hTOPIIα and hTOPIIβ are differentially affected by various poisons and demonstrate the utility of C. elegans in understanding the genetics of drug responses.


Choose Directory
(Default root is public Directory)
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Downloads
Code
The current release is PASQUAL v1.0.
Data
Simulated data sets
Real data sets
Reads generated from the sequences
$> gen_reads -i s_suis.fa -o s_suis_c30_l100.fa -c30 -l100.fa
The current release is PASQUAL v1.0.
$> gen_reads -i s_suis.fa -o s_suis_c30_l100.fa -c30 -l100.fa


France sees 2 billion euro payoff as lottery IPO launched
PARIS: France's state-owned lottery monopoly kicked off its shift to private hands Thursday (Nov 6), opening the sale of shares in the company that is likely to bring nearly two billion euros (US$2.2 billion) to government coffers.
The Francaise des Jeux (FDJ) set the indicative price for shares in the operator of 16.50 to 19.90 euros, which would value the company at up to 3.8 billion euros.
The initial public offering of the 52 per cent stake aims to rekindle demand for stocks among French savers, many of whom have stuck massively with ultrasafe but low-interest savings accounts since the 2007-2008 financial crisis.
"It's an opportunity to renew interest in the market with a simple product. When you're an individual, you need to invest for the long term," FDJ's chief executive, Stephane Palloz, told BFM television on Thursday.
"We offer an attractive yield for a less risky product," she said.
Around a third of the offering will be reserved for private investors, who will get a free share for every 10 bought if they hold them for at least 18 months.
The subscription period runs to Nov 20, and trading will begin the next day.
France is one of few countries to enable such investment opportunities for individuals. Would-be investors in most other countries have to wait for the first day of public trading to buy.
Prime Minister Edouard Philippe's government has vowed to partially privatise a number of entities, including the airports operator ADP and the energy behemoth Engie, to feed a 10 billion euro fund to kickstart technological innovation.
FDJ came into being as the successor of a national lottery founded in 1933 to help soldiers disfigured in World War I and struggling farmers.
It is a comparatively rare case of a French public enterprise in robust financial health, and is well known by the public, who made 16 billion euros' worth of flutters on assorted games of chance last year.
It is the second-biggest betting company in Europe and the fourth in the world.
The state will retain a 20 per cent stake in the company, and will benefit from a yearly public contribution of some 3.5 billion euros, as well as a 380 million euro annual payment for a 25-year monopoly license.
But critics and opposition parties have assailed the move to offload a rare money-making state enterprise, saying it is tantamount to selling off "the family jewels".


3 references to QueryRunspaces
System.Management.Automation (3)
engine\remoting\commands\ConnectPSSession.cs (2)
1036
new
QueryRunspaces
(),
1106
private readonly QueryRunspaces _queryRunspaces = new
QueryRunspaces
();
engine\remoting\commands\getrunspacecommand.cs (1)
540
private readonly QueryRunspaces _queryRunspaces = new
QueryRunspaces
();


Introduction
Settlers of Catan is a game made by German game maker Klaus Teuber. Imagining how European Viking settlers sailed to and settled on near by islands inspired Mr. Teuber to create this trading and building strategy game.
Each player is the leader of a tribe starting with two settlements. Using the resources of the island--mainly brick/clay, wood/lumber, wheat, sheep/wool, and ore-- players build roads, settlements, and cities, purchase soldliers/knights, steal from other players, and trade resources in order to become the ruling Lord/Lady of the island of Catan.
General actions per turn
After the island has been set up, starting player has been decided, starting settlements have been placed, and starting resources have been alocated, the game officially begins with the starting player. For more information about set up, please refer to the Catan instruction manual.
On each turn, the player would roll 2 dice. If the number rolled is not a 7, each player that has a settlement next to a resource tile with the corresponding dice roll would gain one of that resource per settlement next to the rolled number tile. Likewise, each player that has a city next to a resource title with the corresponding dice roll will get 2 of that resourece per city next to rolled number tile. For example, if some rolled a 4 on their turn, and I have a city next to an ore tile with a 4 on it and a city next to a sheep tile with a 4 on it, I would get 2 ore cards and 1 sheep card from that person's roll. If a player rolls a seven, any one who has 8 or more resource cards, will have to discard half their hand. After discards are completed, the player who rolled the seven, can move the humanoid grey (edition 2 or 3) or black (edition 1) pawn on the board known as the robber on to a resource tile, which prevents resources from being produced from that tile, until the robber is moved again. Additionally, if there are settlements or cities next to robber, the player who moved the robber, can steal one random resorce from a player whose settlement or city is next to the robber.
After everyone has collected their resources via dice roll or stealing via the robber, then the player who rolled, can either build cities, roads, or settlements, purchase or play development cards, or trade with other players or the bank. Roads cost a brick/clay and a wood/lumber and settlements cost a brick/clay, a wood/lumber, a sheep/wool, and a wheat. Settlements must always be connected to roads, and settlements must always be at least 2 road spaces away from other settlements. One can upgrade a settlement into a city using three ore cards and two wheat cards. As mentioned in the previous paragraph, settlments produce 1 resource per roll and cities produce 2 resources per roll. Additionally, setllements are worth 1 victory point and cities are worth 2 victory points. The first person who builds an uninterupted route of 5 road pieces claims the "Longest Road" award, which is worth 2 victory points. The "Longest Road" award can only be taken, if a player builds a longer uninterepted road. For more information about victory points, see the How to win section below.
In addition to purchasing/building roads, settlements, and cities, players can purchase development cards for the cost of one ore, one wheat, and one sheep/wool per development card. Players can purchase as many development cards at once, but they can only play one development card, per turn. Among the 25 development cards in a 3-4 person game, there are these cards:
- 2 monopoly cards, which allows the player steal all the resources of one type from all other players
- 2 road building cards, which allows the player to build 2 free roads
- 2 year of plenty cards, which allows the player to take 2 free resources from the bank of any type
- 5 victory point cards, which each are worth 1 victory point. (See the How to win section below for more details on victory points)
- 14 soldlier/knight cards
Roads, settlements, cities, and development cards cost resoruces, and at times, it is hards to obtain the resources to get them. Thus, trading with other players, the bank, and the ports are esential in doing well in the game. When it comes to trading between players, trades either have to involve the active player (the person who rolled the dice for that turn). (e.g. if you want to trade with someone, it has to be that person's turn or your turn). Additionally, one could trade with the bank, trading in four of one resource for one desired resource. If a person is next to one of the nine ports, he/she/they can make a reduced deal with the bank according to the Catan instruction manual.
How to win
To claim to the victorious title of Lord/Lady of Catan, one must be the first player to create a kingdom worth 10 victory points. Each settlement is one victory point; however, due to supplies, the maximum number of settlements one could have is 5 settlements. Players can upgrade settlements to cities, which each city is worth 2 points, allowing players to reclaim settlement pieces to build another settlement; however, each player can build up to a maximum of 4 cities.
In addition to settlements and cities one could gain victory points through awards. The first to build an uninterupted route of 5 road pieces can claim the Longest Road award. If anyone builds a longer uninterupted route than the holder of the Longest Road award, they can take that award from the current award holder. Likewise, the first to play 3 soldlier/knight cards can claim the Largest Army award, and if someone plays more soldlier/knight cards than the Largest Army award holder, they can take away the Largest Army award from the holder. Both Longest Road and Largest Army awards are worth 2 points each.
Lastly, 5 of the purchasable development cards are victory point cards, which are worth 1 hidden victory point each.
With the combination of settlements, cities, awards, and victory point cards, the first to 10 victory points becomes Lord/Lady of Catan and wins the game.


Listed below are the license details from the License Key you have provided. Please confirm the following details then proceed with the system activation.
Client Name
PC Number
HD Serial Number
License Key
License Duration
Congratulations! You have successfully activated the system. You may now get started by logging on the system. Please refer below for the new license details.


Deploying a Connector
The Greenplum Database administrator deploys a connector to a Greenplum Database cluster by first registering the connector and its dependencies, and then synchronizing the PXF configuration files across the Greenplum Database cluster. After deploying the connector, the administrator must restart the PXF agent.
Identifying Connector Run-Time Dependencies
You must identify your connector’s run-time dependencies on other JAR files.
You must also identify any dependencies that your connector has on third-party commands or other components on the system. These commands and components must be installed on all Greenplum Database segment hosts. Any programs on which your connector depends must be executable by the gpadmin
operating system user.
Registering Connector Run-Time Dependencies
The PXF agent determines certain runtime dependencies from files found in the $GPHOME/pxf/conf
, $PXF_CONF/conf
, and $PXF_CONF/lib
directories. Only the Greenplum Database administrative user can register PXF dependencies.
The $GPHOME/pxf/conf/pxf-private.classpath
configuration file identifies the runtime dependencies for the PXF agent itself. This file also identifies the runtime dependencies for the PXF built-in HDFS, Hive, and HBase connectors.
The Greenplum Database administrator registers a third-party connector and its runtime dependencies by copying the files to the $PXF_CONF/lib
directory, synchronizing the PXF configuration across the cluster, and then restarting the PXF agent on each segment host.
Deploying to the Greenplum Database Cluster
When a Greenplum Database administrator deploys a new connector, they:
- Copy the connector JAR file and the (JAR) files of any dependencies to the Greenplum Database master host
- Synchronize the PXF configuration to each Greenplum Database segment host
- Restart the PXF agent on each host
Only the Greenplum Database administrative user can restart the PXF agent.
For example, if PXF_CONF=/usr/local/greenplum-pxf
, these commands deploy the connector across the cluster:
gpadmin@gpmaster$ cp my-connector.jar $PXF_CONF/lib/
gpadmin@gpmaster$ cp connector-dependency.jar $PXF_CONF/lib/
gpadmin@gpmaster$ $GPHOME/pxf/bin/pxf cluster sync
Restart PXF on each Greenplum Database segment host as described in Restarting PXF.
The administrator must also install any third-party commands or other components used by the connector on all Greenplum Database segment hosts and ensure that these programs are executable by the gpadmin
operating system user.
Verifying Connector Deployment
To verify that you deployed a connector successfully, you create a Greenplum Database external table and invoke SELECT
and/or INSERT
commands on the table to test read and write operations on the external data source.
The Greenplum Database end user accesses an external data source by invoking a CREATE EXTERNAL TABLE
command specifying the pxf
protocol. (Refer to the CREATE EXTERNAL TABLE reference page for more about this Greenplum Database command.)
The LOCATION
clause of a CREATE EXTERNAL TABLE
command specifying the pxf
protocol is a URI that specifies the external data source and the location, path to, or name of the data. The query portion of the pxf
protocol URI, introduced by a question mark (?), must include a profile name or the fully-qualified plug-in class names of the connector.
Creating an External Table using PXF in the PXF end user documentation details how to create an external table when specifying a profile name. The syntax for a CREATE EXTERNAL TABLE
command that specifies the Java plug-in class names in the LOCATION
clause follows:
CREATE [WRITABLE] EXTERNAL TABLE <table_name>
( <column_name> <data_type> [, ...] | LIKE <other_table> )
LOCATION('pxf://<path-to-data>?[FRAGMENTER=<fragmenter_class>
&]ACCESSOR=<accessor_class>
&RESOLVER=<resolver_class>
[&<custom-option>=<value>[...]]')
FORMAT '[TEXT|CSV|CUSTOM]' (<formatting-properties>);
The LOCATION
clause identifies the Java plug-in classes that PXF will use to split (FRAGMENTER
), read and/or write (ACCESSOR
), and deserialize/serialize (RESOLVER
) the external data.
Example: Deploying the Demo Connector
In this exercise you deploy the Demo connector, its dependencies, and configuration files and verify the deploy operation.
About the Demo Connector
Recall that your copy of the Demo connector resides in the org.greenplum.pxf.example.demo
package.
Plug-in Classes
The Demo connector read operation uses the following plug-in classes:
- Fragmenter -
org.greenplum.pxf.example.demo.DemoFragmenter
- Read Accessor -
org.greenplum.pxf.example.demo.DemoAccessor
- Read Resolver -
org.greenplum.pxf.example.demo.DemoTextResolver
The Demo connector write operation uses the following plug-in classes:
- Write Accessor -
org.greenplum.pxf.example.demo.DemoFileWritableAccessor
- Write Resolver -
org.greenplum.pxf.example.demo.DemoTextResolver
Notice that the Demo connector read and write operations share the same Resolver class but use different Accessor classes.
Run-Time Dependencies
The Demo connector has a run-time dependency on the Apache Commons Logging JAR, commons-logging.jar
. This JAR file is installed with PXF and should already be specified in the pxf-private.classpath
file.
Prerequisites
Before attempting this exercise, ensure that you have:
- Built the Demo connector as described in Example: Building the Demo Connector JAR File.
- Administrative access to a running Greenplum Database cluster.
- Initialized, configured, and started the PXF agent on each Greenplum Database segment host as described in Configuring PXF.
- Enabled the PXF extension in the database, and optionally granted specific Greenplum Database roles access to the
pxf
protocol; Enabling/Disabling PXF and Granting Access to PXF describe these procedures.
Procedure
Perform the following procedure to deploy the Demo connector and to verify that you deployed the connector successfully:
Log in to the Greenplum Database master node as an administrative user:
$ ssh gpadmin@<gpmaster>
Copy the Demo connector JAR file that you previously built to the
$PXF_CONF/lib
directory on the Greenplum Database master host. ReplacePXFDEV_BASE
with the absolute path to your PXF development work area:gpadmin@gpmaster$ scp user@devsystem:/PXFDEV_BASE/demo_example/build/libs/my-demo-connector.jar $PXF_CONF/lib/my-demo-connector.jar
Copy the JAR file to all Greenplum Database segment hosts by synchronizing the PXF configuration. For example:
gpadmin@gpmaster$ $GPHOME/pxf/bin/pxf cluster sync
Restart PXF on each Greenplum Database segment host as described in Restarting PXF.
Verify that you correctly deployed the Demo connector by creating and accessing Greenplum Database readable and writable external tables that specify the Demo connector plug-ins:
Connect to a database in which you created the PXF extension as the
gpadmin
user. For example, to connect to a database namedpxf_exampledb
:gpadmin@gpmaster$ psql -d pxf_exampledb -U gpadmin
Create a readable Greenplum external table to exercise the Demo connector read operation. For example:
pxf_exampledb=# CREATE EXTERNAL TABLE demo_tbl_read (a TEXT, b TEXT, c TEXT) LOCATION ('pxf://default/tmp/dummy1?FRAGMENTER=org.greenplum.pxf.example.demo.DemoFragmenter&ACCESSOR=org.greenplum.pxf.example.demo.DemoAccessor&RESOLVER=org.greenplum.pxf.example.demo.DemoTextResolver') FORMAT 'TEXT' (DELIMITER ','); CREATE EXTERNAL TABLE
(The Demo connector read operation returns static data. You could have specified any file path in the
LOCATION
clause; it will be ignored by the Demo connector.)Query the
demo_tbl_read
table:pxf_exampledb=# SELECT * from demo_tbl_read; a | b | c ----------------+--------+-------- fragment2 row1 | value1 | value2 fragment2 row2 | value1 | value2 fragment1 row1 | value1 | value2 fragment1 row2 | value1 | value2 fragment3 row1 | value1 | value2 fragment3 row2 | value1 | value2 (6 rows)
Create a writable Greenplum external table to exercise the Demo connector write operation. For example:
pxf_exampledb=# CREATE WRITABLE EXTERNAL TABLE demo_tbl_write (a TEXT, b TEXT, c TEXT) LOCATION ('pxf://tmp/demo_write_1?ACCESSOR=org.greenplum.pxf.example.demo.DemoFileWritableAccessor&RESOLVER=org.greenplum.pxf.example.demo.DemoTextResolver') FORMAT 'TEXT' (DELIMITER ','); CREATE EXTERNAL TABLE
Write some text data into the
demo_tbl_write
table. For example:pxf_exampledb=# INSERT INTO demo_tbl_write VALUES ('x', 'y', 'z'); INSERT 0 1 pxf_exampledb=# INSERT INTO demo_tbl_write VALUES ('u', 'v', 'w'); INSERT 0 1 pxf_exampledb=# INSERT INTO demo_tbl_write VALUES ('r', 's', 't'); INSERT 0 1
Each
INSERT
command writes a file to the directory named/tmp/demo_write_1
on the local file system.View the contents of the
/tmp/demo_write_1
directory on the local file system. For example:gpadmin@gpmaster$ cat /tmp/demo_write_1/* x,y,z u,v,w r,s,t
You successfully deployed the Demo connector. If you choose, you may deploy read and write profiles for the Demo connector as a convenience to the end user. See Deploying a Profile for profile definition and deployment instructions.


Discover Mëbla designer furniture. Sofas, chairs, armchairs and home accessories designed by Alice Rodrigez, Feliks Ståck and many others.
 That's not a problem! Give us a call or send us an email and we will get back to you as soon as possible!


You can use 3rd party platforms such as eventbrite and tickettailor to sell your tickets.
Step 1 Select the type of ticket you wish to purchase
You can use 3rd party platforms such as eventbrite and tickettailor to sell your tickets.
Purchase before 1st Oct 2021. Only 50 tickets available. You can also redeem your member tokens to get yourself a ticket
Purchase before 1st July 2020. Only 50 tickets available. You can also redeem your member tokens to get yourself a ticket
Purchase before 1st May 2020. Only 50 tickets available. You can also redeem your member tokens to get yourself a ticket


Source code for warg.imports
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Christian Heider Nielsen"
__doc__ = r"""
Created on 28/06/2020
"""
__all__ = [
"is_module_available",
"import_warning",
"reimported_warning",
"ensure_in_sys_path",
"clean_sys_path",
]
import sys
from importlib.util import find_spec
from warnings import warn
from pathlib import Path
from typing import Optional
[docs]def clean_sys_path() -> None:
"""
Clean the sys.path for dead paths or duplicates
"""
out = []
for path in sys.path:
p = Path(path).resolve()
if p.exists():
if p not in out:
out.append(p)
sys.path[:] = [str(o.absolute()) for o in out]
[docs]def ensure_in_sys_path(
path: Path, position: Optional[int] = None, resolve: bool = False, absolute: bool = True
) -> None:
"""
Ensures that a path is in sys.path, but avoids duplicates.
Can also resolve and absolute paths for duplication.
Does not clean the existing paths in sys.path
:param path:
:type path:
:param position:
:type position:
:param resolve:
:type resolve:
:param absolute:
:type absolute:
:return:
:rtype:
"""
if absolute:
path = path.absolute()
str_path = str(path)
sys_path_snapshot = sys.path
inclusion_test = None
if resolve:
sys_path_snapshot = [Path(p).resolve() for p in sys_path_snapshot]
inclusion_test = path.resolve() in sys_path_snapshot
else:
inclusion_test = str_path in sys_path_snapshot
if not inclusion_test:
if position:
sys.path.insert(position, str_path)
else:
sys.path.append(str_path)
[docs]def is_module_available(module: str) -> bool:
"""**Return True if module is available.**
Parameters
----------
module: str
Name of the module to be checked.
Returns
-------
bool
True if installed.
"""
return find_spec(module) is not None
[docs]def import_warning(module_name: str) -> None:
"""
Inform the user that module has been imported,
useful when repeated imports are heavy in the contexts of multiprocessing.
Lets the user identify which file is reporting heavy loading and restructure code to avoid repeated importing
:param module_name:
:return:
"""
from sys import modules
if module_name in modules:
warn(
f"You already {module_name} had imported, consider restructuring your code to avoid repeated imports"
)
[docs]def reimported_warning(module_name: str) -> None:
"""
Just an idea
:return:
"""
raise NotImplemented
# TODO: touch .lock file to system for module_name for a multiprocess warning if already exists,
# delete it again once process is done?
# context_wrapper maybe useful
if __name__ == "__main__":
def _main() -> None:
"""
:rtype: None
"""
mod = "matplotlib"
import_warning(mod)
from matplotlib import pyplot
import_warning(mod)
pyplot.figure()
def aisjdi():
from copy import deepcopy
s = deepcopy(sys.path)
ensure_in_sys_path(Path(__file__).parent)
s2 = sys.path
print(s == s2, set(s2) - set(s), set(s) - set(s2), s2)
def iajsd():
from copy import deepcopy
s = deepcopy(sys.path)
clean_sys_path()
s2 = sys.path
print(s == s2, set(s2) - set(s), set(s) - set(s2), s2)
# _main()
# aisjdi()
iajsd()


Choose which you would prefer...
10 points for you
0 for the group
6 points for you
6 for the group
You've earned
0
points.
The group has earned
0
points.
That equals an additional
0
points for you.
0
0


{# Copyright (c) 2015 The Pycroft Authors. See the AUTHORS file. This file is part of the Pycroft project and licensed under the terms of the Apache License, Version 2.0. See the LICENSE file for details. #} {% macro item_cols(attribute, title, width=0) %}
| {{ col.title }} | {% endfor %}
|---|
| 0 %} colspan="{{ footcol.colspan }}"{% endif %}>{{ footcol.title }} | {% endfor %}


# tap-stream [![Build Status](https://secure.travis-ci.org/thlorenz/tap-stream.png)](http://travis-ci.org/thlorenz/tap-stream)
Taps a nodejs stream and logs the data that’s coming through.
npm install tap-stream
Given an [object stream](#object-stream) we can print out objects passing through and control the detail via the depth parameter:
`javascript
objectStream().pipe(tap(0));
`
![depth0](https://github.com/thlorenz/tap-stream/raw/master/assets/depth0.png)
`javascript
objectStream().pipe(tap(1));
`
![depth1](https://github.com/thlorenz/tap-stream/raw/master/assets/depth1.png)
`
objectStream().pipe(tap(2));
`
![depth2](https://github.com/thlorenz/tap-stream/raw/master/assets/depth2.png)
For even more control a custom log function may be supplied:
- .pipe(tap(function customLog (data) {
var nest = data.nest; console.log (‘Bird: %s, id: %s, age: %s, layed egg: %s’, nest.name, data.id, nest.age, nest.egg !== undefined);})
);
`text
Bird: yellow rumped warbler, id: 0, age: 1, layed egg: true
Bird: yellow rumped warbler, id: 1, age: 1, layed egg: true
`
## API
### tap( [ depth | log ] )
Intercepts the stream and logs data that is passing through.
- optional parameter is either a Number or a Function
- if no parameter is given, depth defaults to 0 and log to console.log(util.inspect(..))
- depth controls the depth with which [util.inspect](http://nodejs.org/api/util.html#util_util_inspect_object_showhidden_depth_colors) is called
- log replaces the default logging function with a custom one
Example:
```javascript var tap = require(‘tap-stream’);
- myStream
- .pipe(tap(1)) // log intermediate results .pipe(..) // continute manipulating the data
## Object stream
Included in order to give context for above examples.
```javascript function objectStream () {
- var s = new Stream()
- , objects = 0;
- var iv = setInterval(
- function () {
- s.emit(‘data’, {
id: objects, created: new Date() , nest: {
name: ‘yellow rumped warbler’, age: 1 , egg: { name: ‘unknown’ , age: 0 } }
}
, 4 );
- if (++objects === 2) {
- s.emit(‘end’); clearInterval(iv);
}
}
, 200);
return s;


Hi ${newMessage.name},
You are receiving this email because we received a password reset request for your account.
If you did not request a password reset, no further action is required.
If you’re having trouble clicking the "RESET PASSWORD" button, copy and paste the URL below into your web browser:
${newMessage.link}
Regards,
Felipe


Home
About me
My blog
Contact
My Blog
Here you can know more about the disciplines I teach
My disciplines
International Economics
Management
Consulting


| Age of Empires 2
| full, demo
| Windows 10 Steam
|
|
|
| Age of Mythology
| full 2.8, demo
| Windows 10 Steam
| good
| Custom Scenarios
|
| Aidyn Chronicles: The First Mage
| full
| Nintendo 64
| fails to start
|
|
| America's Army
| full 2
| Windows 10
|
|
|
| Anemoiapolis
| full chapter 1
| Windows 10 Steam
|
|
|
| Banjo-Kazooie
| full
| Nintendo 64
| good
|
|
| Banjo-Tooie
| full
| Nintendo 64
| good
|
|
| Battle.net
| free <IP_ADDRESS>08
| Windows 10
| good
|
|
| Bioshock
| demo
| Windows 10
|
|
|
| Bloodridge
| none
| unknown
| lost?
| Guides
|
| Bomberman 64
| full
| Nintendo 64
| good
|
|
| Bomberman Hero
| full
| Nintendo 64
| good
|
|
| Caesar 2
| demo
| Windows 10
|
|
|
| Call of Duty: Burnville
| demo
| Windows 10
|
|
|
| Call of Duty: Dawnville
| demo
| Windows 10
|
|
|
| Call of Duty 2
| demo 5060
| Wine staging 8.5
| good
|
- files
- Guides
- Requires alt-tab or open console to switch to other windows.
|
| Call of Duty 4: Modern Warfare
| demo
| Windows 10
|
|
|
| Command and Conquer
| full
| Nintendo 64
| good
|
|
| Command and Conquer: Remastered
| full
| Windows 10 Steam
| good
|
|
| Command and Conquer: Tiberian Sun
| full, demo
| Windows 10
|
|
|
| Condemned: Criminal Origins
| demo
| Wine staging 8.5
| good
|
|
| Creeper World 3: Arc Eternal
| full
| Ubuntu 22.04 Steam
| good
|
|
| Cruis'n USA
| full
| Nintendo 64
| good
|
|
| Crysis
| demo
| Windows 10
|
|
|
| Crysis Sandbox Editor
| full
| Windows 10
|
|
|
| Deus Ex
| demo
| Windows 10
|
|
|
| Deus Ex: Invisible War
| demo
| Windows 10
|
|
|
| Diablo 2: Lord of Destruction
| full 1.14D, demo
| Wine staging 8.5
| good
|
|
| Diddy Kong Racing
| full
| Nintendo 64
| good
|
|
| Ditty of Carmeana
| demo
| Windows 10
|
|
|
| Dungeon Siege
| full, demo
| Windows 10 Steam
|
|
|
| Dungeon Siege 2
| full, demo
| Windows 10 Steam
| good
|
|
| EVE Online
| full
| Windows 10
| good
|
|
| FEAR
| demo 1.0 build 209
| Windows 10
|
|
|
| FEAR: Extraction Point
| demo
| Windows 10
|
|
|
| FEAR: Perseus Mandate
| demo
| Windows 10
|
|
|
| Full Tilt! Pinball
| demo
| Wine staging 8.5
| good
| Loud default volume.
|
| Gauntlet Legends
| full
| Nintendo 64
| cannot save
|
|
| Garry's Mod
| full
| Ubuntu 22.04 Steam
| good
|
|
| Gex: Enter the Gecko
| full
| Nintendo 64
| cannot save
|
|
| Gothic 3
| demo
| Windows 10
|
| files
|
| Half-Life 2
| full
| Ubuntu 22.04 Steam
| good
|
|
| Half-Life 2: Episode 1
| full
| Ubuntu 22.04 Steam
| good
|
|
| Half-Life 2: Episode 2
| full
| Ubuntu 22.04 Steam
| good
|
|
| Half-Life 2: Lost Coast
| full
| Ubuntu 22.04 Steam
| good
|
|
| Hero Editor
| full 1.04
| Wine staging 8.5
| good
| Installation requires virtual desktop.
|
| Hitman: Blood Money
| full, demo
| Windows 10 Steam
|
|
- files
- Minor mouse issues.
- Requires alt-tab to switch to other windows.
|
| Hitman: Contracts
| full, demo
| Windows 10 Steam
|
|
|
| Hitman 2: Silent Assassin
| full, demo
| Windows 10 Steam
|
|
- Guides
- Config file needs rework.
- Strange window behavior. Requires alt-tab to switch to other windows.
|
| House Party
| full
| Windows 10 Steam
| good
|
|
| Life Is Strange
| chapter 1
| Ubuntu 22.04 Steam
| good
|
|
| LMS
| full
| Windows 10
| lost?
| Lost the source code.
|
| Lords of Magic
| full, demo
| Windows 10 Steam
|
|
|
| Lunar Magic
| full 3.21
| Windows 10
|
|
|
| Mafia
| full, demo
| Windows 10 Steam
| good
|
|
| Marble Blast Gold
| full
| Wine staging 8.5
| good
|
- Guides
- Minor mouse issues.
- Minor resolution issues if largest resolution selected.
- Potential minor crash if focus lost.
|
| Mario Kart 64
| full
| Nintendo 64
| good
|
|
| Mario Party 2
| full
| Nintendo 64
| good
|
|
| Might and Magic - Dark Messiah
| demo
| Windows 10
|
| files
|
| Minecraft
| full 1.20.1, launcher 2.6.16-1.2.30
| Ubuntu 22.04
| good
|
|
| Mortuary Assistant
| full, demo
| Windows 10 Steam
| good
|
|
| Neverending Nightmares
| full 3.3.22468
| Ubuntu 22.04 Steam
| good
|
|
| Nox
| demo
| Windows 10
|
|
|
| Outlast
| full
| Windows 10 Steam
| good
|
|
| Pandemonium
| demo
| Windows 10
|
|
|
| Pandemonium 2
| demo
| Windows 10
|
|
|
| Paper Mario
| full
| Nintendo 64
| good
|
|
| Path of Exile
| full 3.21.2b
| Windows 10
| good
|
|
| Pilotwings 64
| full
| Nintendo 64
| good
|
|
| Planescape: Torment
| full
| Ubuntu 22.04 Steam
| good
|
|
| Plants vs Zombies
| full
| Windows 10 Steam
| good
|
|
| PlugY
| full 14.02
| Wine staging 8.5
| good
|
|
| Portal
| full
| Ubuntu 22.04 Steam
| good
|
|
| Portal 2
| full
| Ubuntu 22.04 Steam
| good
|
|
| Prey
| demo
| Windows 10
|
|
- files
- Guides
- Requires alt-tab to switch to other windows.
|
| Rise of Nations
| demo
| Windows 10
|
|
|
| Robin Hood: Legend of Sherwood
| full, demo 1.00
| Windows 10 Steam
|
|
|
| RollerCoaster Tycoon 2
| full, demo
| Windows 10 Steam
|
|
|
| RollerCoaster Tycoon 3
| full, demo
| Windows 10 Steam
|
|
|
| RuneScape
| free osrs, launcher <IP_ADDRESS>
| Windows 10
| good
| Guides
|
| Sanitarium
| demo
| Windows 10
|
|
|
| Shadow Man
| full, demo
| Nintendo 64
|
|
|
| SimCity 3000
| demo
| Windows 10
|
|
|
| Steam
| full
| Ubuntu 22.04, Windows 10
| good
|
|
| StarCraft
| full 64, demo
| Nintendo 64
| good
|
|
| Stronghold
| full, demo
| Windows 10 Steam
|
| Guides
|
| Super Mario 64
| full
| Nintendo 64
| good
|
|
| Super Smash Bros.
| full
| Nintendo 64
| good
|
|
| Switchball
| demo
| Windows 10
|
|
|
| Team Fortress 2
| full
| Ubuntu 22.04 Steam
| good
|
|
| Terraria
| full
| Ubuntu 22.04 Steam
| good
|
|
| The Elder Scrolls IV: Oblivion
| full
| Windows 10 Steam
| good
|
|
| The Legend of Zelda: Ocarina of Time
| full
| Nintendo 64
| good
|
|
| The Legend of Zelda: Majora's Mask
| full
| Nintendo 64
| good
|
|
| The Stanley Parable
| full
| Ubuntu 22.04 Steam
| good
|
- Config
- files
- Rename "bin/libstdc++.so.6" to allow startup.
|
| The Stanley Parable Demo
| full
| Ubuntu 22.04 Steam
| good
|
- Config
- files
- Rename "bin/libstdc++.so.6" to allow startup.
|
| The Walking Dead
| full
| Windows 10 Steam
| good
|
|
| The Walking Dead: Season 2
| full
| Windows 10 Steam
| good
|
|
| The Wolf Among Us
| full
| Windows 10 Steam
| good
|
|
| Thief Gold
| full, demo
| Windows 10 Steam
|
|
|
| Thief: the Dark Project
| demo
| Windows 10
|
| files
|
| Thief 2: the Metal Age
| full, demo
| Windows 10 Steam
|
| files
|
| Thief 3: Deadly Shadows
| full, demo
| Windows 10 Steam
|
|
|
| Thief 4
| full, demo
| Windows 10 Steam
| good
| files
|
| Titan Quest
| demo
| Windows 10
|
| files
|
| Torque Game Engine
| demo 1.5.2
| Windows 10
|
|
|
| Ubuntu
| full 22.04
| Ubuntu 22.04
| good
|
|
| Unreal Engine 2 Runtime
| demo
| Windows 10
|
|
|
| Unreal Tournament
| demo
| Windows 10
|
|
|
| Unreal Tournament 2004
| demo
| Windows 10
|
|
|
| Uru: Ages Beyond Myst
| demo
| Windows 10
|
|
|
| Warlords
| demo
| Windows 10
|
|
|
| Warzone 2100
| full 4.3.5
| Ubuntu 22.04
| good
|
|
| Windows
| 10 Home
| Windows 10
| good
|
|
| Wine
| full 8.5 staging
| Ubuntu 22.04
| imperfect
| Scripts
|
| World of Warcraft
| free retail <IP_ADDRESS>747
| Windows 10
| good
|
|


Getting started with Mummify is easy. Download and install the Chrome Extension below. Once installed, generate your set of keys below. Anyone can encrypt text using your public key, but only you are able to decrypt using your private key. Simply highlight any text, and it will copied into your clip-board or directly translated.
Setting up Mummify is as easy as hitting Download.
Public Key:
(Your Public Key Will Display Here)
Private Key:
(Your Private Key Will Display Here)
Mummify was created by a team of University students in less than 24 hours at nwHacks, the largest Hackathon in Western Canada. The team learned how to create a Chrome-extension and build much of the application on-the-fly. The application was made with privacy in mind, and providing users with an easy-to-use solution for encryption of virtually anything.


git-merge-tree - Show three-way merge without touching index
git merge-tree <base-tree> <branch1> <branch2>
Reads three tree-ish, and output trivial merge results and conflicting stages to the standard output. This is similar to what three-way git read-tree -m does, but instead of storing the results in the index, the command outputs the entries to the standard output.
This is meant to be used by higher level scripts to compute merge results outside of the index, and stuff the results back into the index. For this reason, the output from the command omits entries that match the <branch1> tree.
Part of the git(1) suite


Usage
Ruby2Go is easy to use.
Write your Ruby code in the editor, then
press the icon to run it. The result
is appended (and highlighted) at the top of the list.
 Here you see the results of all the runs during current session, so when you close the app they are not saved.
Custom input
In Ruby you can use the gets
method to read from the standard
input, but here you need to tell your input in order to be read, like:
with_input('line 1', 'line 2', 2016)
Feel free to supply as many parameters as you need to read, then use the
normal gets
method to read it. Also, make sure you do it
before your program reads the input.
Language support
The library used to run the Ruby code is called Opal, which provides support for Ruby 2 but some features are broken. If you encounter a failure, please report it to its repository so we both can benefit.
License
This project is released under the MIT license but distributed with other libraries. To see full license text, and the 3rd party ones, visit this link .


Boilerplate for AngularJS, Java Spring and MySql.
Includes Gulp Minify, Uglify, Concat, Mocha and Chai Tests


|
|
Create DtDNS Account
|
This page covers creating an account at DtDNS. Using this account allows you to configure and run the servers from a dynamically allocated IP address.
Creating a DtDNS account
Creating an account at DtDNS is extremely easy.
- Go to DtDNS home page.
- To the right of log in click Create an Account
There are two pages to fill in. Enter the following details:
Page 1:
| 1) |
Desired Username |
This name is used for logging into your account |
| 2) |
Your Name |
Your real name (Internal use) |
| 3) |
Desired Password |
A password for logging into your account |
| 4) |
Confirm Password |
As above |
| 5) |
E-mail Address |
A real active e-mail is required. An activation code is sent to this e-mail address |
| 6) |
Confirm E-mail |
As above |
| 7) |
Optional |
Skip the Optional Information section if you don't wish fill it in. |
| 8) |
Two CAPCHA words |
Enter the two CAPCHA words (if these are difficult to read, click the new challenge button) |
Note 1: After step 8) you will be redirected to page 2:
Note 2: A confirmation e-mail is sent to your e-mail account set in step 5)
Page 2:
| 9) |
Username |
Enter your Username as entered in 1). |
| 10) |
Enter code |
Enter code sent to your email. |
Note 3: The email sent to you contains a link returning you to page 2. After step 8) you can close the page and wait for this email.
Creating a Hostname overview
General information
- A DtDNS account is limited to five free hostnames.
- A hostname is concatenated with a domain name.
- I have listed available domains on the right.
- Your full URL, for example
- If you chose books as hostname
- And select from the drop down menu effers.com
Will look like this:
http://books.effers.com
|
|
| Available Free Domain Names |
3d-game.com
4irc.com
b0ne.com
bbsindex.com
chatnook.com
darktech.org
deaftone.com
dtdns.net
effers.com
|
etowns.net
etowns.org
flnet.org
gotgeeks.com
scieron.com
slyip.com
slyip.net
suroot.com
|
|
Wild Cards
After creating a hostname, wild cards are enabled by default. This allows you to use a URL such as: http://www.books.effers.com
Note: The www. is the wild card part. This can be anything you like; for example:
- http://www.books.effers.com
- http://www-internation.books.effers.com
- http://local.books.effers.com
- http://www.home.books.effers.com
The host name books.effers.com resolves to your IP address. The wild card can be resolved on your server using Apache Vhosts to create different sites.
Create Hostname
To create a new hostname at DtDNS
Login
- Go to Login page
- Enter Username: as set in step 1)
- Enter Password: as set in step 3)
- My Services page displayed
- Click on Hostnames. This opens the Hostname Manager
Create hostname
- Enter a desired Hostname
- From the drop down menu select a Domain
- Click Add Hostname
- Enter the two CAPCHA words (if these are difficult to read, click the new challenge button)
- Click Add Hostname button
- This is added to your list of host names
Manage existing hostnames
To manage existing hostnames, simply click the entry in the list; for example:
| Hostname |
Domain |
IP Address |
Type |
| uni23 |
dtdns.net |
xx.119.35.45 |
Active Dynamic |
| books |
effers.com |
xx.129.30.174 |
Active Dynamic |
Click a hostname link to open its corresponding configuration page. Here you can change various settings, including the IP address.
Note 1: After reconnecting to your service provider you need to login to DtDNS and update the IP address accordingly. Alternatively add the new account to The Uniform Server's builtin updater. See Edit DtDNS Accounts.
Note 2: With the updater configured you can enable Cron to perform automatic updates.
Summary
For a home web server the free account at DtDNS is ideal. At least it gives you an opportunity to test their service. Perhaps it will encourage you to purchase a real domain and use some of their paid services.
Where to go next
DtDNS - Introduction
|


UN mission in Afghanistan accused of interfering in presidential elections
by Khaama PressThe Council of Presidential Runners has accused the United Nations Assistance Mission in Afghanistan of interfering in presidential elections.
According to a statement released by Council of Presidential Runners, the UN mission in Afghanistan has adopted a ‘biased’ approach while dealing with the issues of presidential elections.
The statement specifically pointed towards the recent letter of UNAMA in which it complained regarding the behavior of one of the election commissioners, calling it a biased approach.
Meanwhile, some social media users and activists have complained that the UN mission in Afghanistan has not only removed their critical comments regarding the mission’s recent statement and Tweets but has blocked them from accessing its social media pages.
On the other hand, Asadullah Saadati, the running mate of Abdullah Abdullah in presidential election has said that he does not think UNAMA is solely responsible for the issue.
Saadati blamed certain active ‘channels’ within UNAMA for the issue, claiming that the channels are actively operating in other areas as well.
This comes as an audio clip of lawmaker Shinkai Karokhail who is also a member of the electoral team of President Ashraf Ghani recently went viral on social media websites in which she talks about a specific channel in UNAMA that operates in favor of State Builder team led by the President.
Author
-
The Khaama Press News Agency is the leading and largest English news service for Afghanistan with over 3 million hits a month. Independent authors/columnists and experts are welcomed to contribute stories, opinions and editorials. Send stories to news@khaama.com.


6. I/O Tutorial¶
Before getting into machine vision topics we’re going to talk about I/O pin control on your OpenMV Cam. It’s important you know how to toggle I/O pins, transmit and receive serial data, and put your OpenMV Cam to sleep so that you can create a system that’s able to “sense”, “plan”, and “act” in one package.
First, let’s take a look at your OpenMV Cam’s pinout below:
Depending on the model of your OpenMV Cam you have 9-10 general purpose I/O pins available which can be used for low-speed digital input and output. Note that we use STM32 processors which have 5V tolerant I/O pins so you can hookup your OpenMV Cam directly to an Arduino or other 5V device without worry. The I/O pins are quite beefy too and can source or sink up to 25 mA each.
Anyway, different I/O pins have different special functions. P0-P3, for example, are your OpenMV Cam’s SPI bus pins which you can use to control SPI devices. P4-P5 are your OpenMV Cam’s Asynchronous Serial or I2C bus bins to talk serial or I2C. P6 is your OpenMV Cam’s ADC/DAC pin for 0V to 3.3V input and output. And P7-P8 (or P7-P9) are your OpenMV Cam’s Auxiliary I/O pins.
6.1. The PYB Module¶
All microcontroller I/O functionality is available from the pyb
(Python
Board) module. You just need to import pyb
in your script to get access to
it. Once imported you’ll have access to the ADC, CAN, DAC, I2C, Pin, Servo, SPI,
and UART classes along with being able to control the board’s power consumption.
Note
The tutorial is not complete at right now. Please see the quick reference and library for more documentation.


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New Jersey country club claims $30G wine-stained Hermes bag is fake
by New York PostThe New Jersey country club being sued after a waiter allegedly spilled red wine on a $30,000 Hermes handbag thinks the pricey accessory is a phony, according to their lawyer.
“There are serious issues regarding the authenticity of the bag,” said attorney Ken Merber, who represents Alpine Country Club in the lawsuit by purse-owner and member Maryana Beyder.
Merber added that the club had the pink Kelly bag “examined by an expert,” but declined to say more on the matter.
CLICK HERE FOR MORE FROM THE NEW YORK POST
Meanwhile, an attorney for Beyder blasted the fake-bag accusations.
NEW JERSEY COUNTRY CLUB SUES WAITER ACCUSED OF POURING RED WINE ON PATRON'S $30G HERMES PURSE
“She would not be suing for the value of the bag if the bag wasn’t authentic,” Alexandra Errico said. “We have an authentication report, two of them.”
CLICK HERE FOR THE FOX NEWS APP
Meanwhile, the club decided not to try and hold the slippery-fingered waiter liable for the flub — filing new court papers Tuesday that no longer target the server, according to Errico.
This story originally appeared in the New York Post. For more from the Post, click here.


I began taking Lyrica for fibromyalgia, which is the main syndrome it is prescribed for. Over the course about a year, I have tried many different pharmaceuticals to try to treat fibromyalgia which causes wide-spread pain and insomnia (none of them working). I thought Lyrica would be like the others, but I was wrong.
I was prescribed 50mg twice daily. Although, Iím only 110lbs, I do tend to have a high tolerance for many analgesics. Originally, I never took more than the recommended dose. After a couple of days of taking it as recommended, I began to shake uncontrollably every day. I became very paranoid and anxious, and the uncontrollable shaking made the paranoia worse. However, I kept taking it because it supposedly takes about 3 months start working for fibromyalgia pain and other FMS symptoms (similar to other treatment for FMS).
Being unable to pour a glass of water gracefully due to the constant shuddering, it seemed like a good idea to stop taking it; however, stopping it suddenly made the side effects worse and made me have withdrawal-like symptoms (headaches and general feeling-like-shit). I felt a horrible anxiety that I may have a seizure (which may have been the paranoia). Also, I was having heart palpitations on and off.
Since then, Iíve dabbled in using it recreational (which didnít work for me). Iíve never taken more than 75mg at once. But, when I have taken that much I didnít even feel the normal pain-killer high (or any high) from taking it, yet I felt really bad, paranoid and shakyólike I took really shitty and weak speed, but with no positive feeling. Overall, this was a shitty drug to experiment with. I wouldnít recommend it, but then again itís possible I just had a bad experience, and Iíve never experimented much with it. Iíve never snorted it but frankly I'd be scared to.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


/*
This file is part of Ext JS 4
Copyright (c) 2011 Sencha Inc
Contact: http://www.sencha.com/contact
Commercial Usage
Licensees holding valid commercial licenses may use this file in accordance with the Commercial Software License Agreement provided with the Software or, alternatively, in accordance with the terms contained in a written agreement between you and Sencha.
If you are unsure which license is appropriate for your use, please contact the sales department at http://www.sencha.com/contact.
*/
/**
* @class Ext.selection.TreeModel
* @extends Ext.selection.RowModel
*
* Adds custom behavior for left/right keyboard navigation for use with a tree.
* Depends on the view having an expand and collapse method which accepts a
* record.
*
* @private
*/
Ext.define('Ext.selection.TreeModel', {
extend: 'Ext.selection.RowModel',
alias: 'selection.treemodel',
// typically selection models prune records from the selection
// model when they are removed, because the TreeView constantly
// adds/removes records as they are expanded/collapsed
pruneRemoved: false,
onKeyRight: function(e, t) {
var focused = this.getLastFocused(),
view = this.view;
if (focused) {
// tree node is already expanded, go down instead
// this handles both the case where we navigate to firstChild and if
// there are no children to the nextSibling
if (focused.isExpanded()) {
this.onKeyDown(e, t);
// if its not a leaf node, expand it
} else if (!focused.isLeaf()) {
view.expand(focused);
}
}
},
onKeyLeft: function(e, t) {
var focused = this.getLastFocused(),
view = this.view,
viewSm = view.getSelectionModel(),
parentNode, parentRecord;
if (focused) {
parentNode = focused.parentNode;
// if focused node is already expanded, collapse it
if (focused.isExpanded()) {
view.collapse(focused);
// has a parentNode and its not root
// TODO: this needs to cover the case where the root isVisible
} else if (parentNode && !parentNode.isRoot()) {
// Select a range of records when doing multiple selection.
if (e.shiftKey) {
viewSm.selectRange(parentNode, focused, e.ctrlKey, 'up');
viewSm.setLastFocused(parentNode);
// just move focus, not selection
} else if (e.ctrlKey) {
viewSm.setLastFocused(parentNode);
// select it
} else {
viewSm.select(parentNode);
}
}
}
},
onKeyPress: function(e, t) {
var key = e.getKey(),
selected,
checked;
if (key === e.SPACE || key === e.ENTER) {
e.stopEvent();
selected = this.getLastSelected();
if (selected) {
this.view.onCheckChange(selected);
}
} else {
this.callParent(arguments);
}
}
});


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Nicolo’s is a unique Italian eatery belonging to a chain of small restaurants. The food at Nicolo’s is always prepared with the finest ingredients made from scratch. This establishment is perfect for people of all ages who are in the mood to enjoy fine Italian food and an affordable cost. Whether it is pizza, pasta, appetizers, salads, or sandwiches that you prefer, Nicolo’s has something for everyone.
Happy Hour 4-6


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<%- include('../partials/header_top.html') -%>
Redirecting to home page
<%- include('../partials/footer.html') -%>