|
Houzi Game Engine
|
High resolution stopwatch. More...
#include <stopwatch.hpp>
Public Member Functions | |
| stopwatch () noexcept | |
| Creates a stopwatch object. More... | |
| std::chrono::nanoseconds | get_elapsed_time () const noexcept |
| Returns the elapsed time. More... | |
| bool | is_running () const noexcept |
| Checks if the stopwatch is currently running or not. More... | |
| std::chrono::nanoseconds | start () noexcept |
| Starts the stopwatch. More... | |
| std::chrono::nanoseconds | pause () noexcept |
| Pauses the stopwatch. More... | |
| std::chrono::nanoseconds | reset () noexcept |
| Resets the stopwatch. More... | |
| std::chrono::nanoseconds | stop () noexcept |
| Stops the stopwatch. More... | |
Static Public Member Functions | |
| static std::chrono::nanoseconds | get_resolution () noexcept |
| Retrieves the stopwatch resolution. More... | |
High resolution stopwatch.
Guarantees a resolution of 100 ns.
|
noexcept |
Creates a stopwatch object.
When created the stopwatch is not running.
|
noexcept |
Returns the elapsed time.
|
staticnoexcept |
Retrieves the stopwatch resolution.
Note: the reported resolution might not be accurate.
|
noexcept |
Checks if the stopwatch is currently running or not.
|
noexcept |
Pauses the stopwatch.
|
noexcept |
Resets the stopwatch.
|
noexcept |
Starts the stopwatch.
|
noexcept |
Stops the stopwatch.
The stopwatch will be paused and reset.


Writing paragraphs takes practice, but what should students write
about? Good paragraph writing prompts allow students to write
about what they know and like, so their focus can be on the
writing process and using the four essential elements.
Jhon Wick, New YorkWriting paragraphs takes practice, but what should students
write about? Good paragraph writing prompts allow students to
write about what they know and like, so their focus can be on
the writing process and using the four essential
elements.


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3456 State Road, Akron, OH 44444
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The Drink of the Month is SG Special Brew.
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Github Search is basically an app that helps users to search for other users by their github usernames.
Navigate to the users page throught he sidabar by clicking the users icon
Here there is a blank search field for seaching for github users.
Input the user's github username and it brings the user's information.
You can also view the user's github page by clicking the button that comes in the user's information./p>


Copyright © 2016 The University of Texas at Austin. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF TEXAS AT AUSTIN ''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Certain software bundled with the Orc JAR and the Orc plug-in are subject to the following licences:
SCALA LICENSE
Copyright (c) 2002-2014 EPFL, Lausanne, unless otherwise specified. All rights reserved.
This software was developed by the Programming Methods Laboratory of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Permission to use, copy, modify, and distribute this software in source or binary form for any purpose with or without fee is hereby granted, provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
The IMP (IDE Meta-tooling Platform) runtime Eclipse plug-in
IMP software is made available under the terms of the Eclipse Public License.
The Eclipse Foundation makes available all content in this plug-in ("Content"). Unless otherwise indicated below, the Content is provided to you under the terms and conditions of the Eclipse Public License Version 1.0 ("EPL"). A copy of the EPL is available at http://www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" will mean the Content.
The LPG (LALR Parser Generator) runtime Eclipse plug-in
License: Eclipse Public License Version 1.0.


Description of the Problem
https://www.lintcode.com/problem/add-two-numbers/description
167. Add Two Numbers You have two numbers represented by a linked list, where each node contains a single digit. The digits are stored in reverse order, such that the 1's digit is at the head of the list. Write a function that adds the two numbers and returns the sum as a linked list. Example Given 7->1->6 + 5->9->2. That is, 617 + 295. Return 2->1->9. That is 912. Given 3->1->5 and 5->9->2, return 8->0->8.
Idea to solve the problem
Sum digit by digit with carry, as ever did in school
My codes
/**
* Definition for ListNode
* public class ListNode {
* int val;
* ListNode next;
* ListNode(int x) {
* val = x;
* next = null;
* }
* }
*/
public class Solution {
/**
* @param l1: the first list
* @param l2: the second list
* @return: the sum list of l1 and l2
*/
// Total runtime 1666 ms. Your submission beats 99.80% Submissions!
public ListNode addLists(ListNode l1, ListNode l2) {
return add(l1 ,l2, 0);
}
public ListNode add(ListNode l1, ListNode l2, int carry) {
int sum = carry;
if (l1 != null) {
sum += l1.val;
l1 = l1.next;
}
if (l2 != null) {
sum += l2.val;
l2 = l2.next;
}
ListNode node = new ListNode(sum % 10);
carry = sum / 10;
if (l1 != null || l2 != null || carry == 1) {
ListNode sumNode = add(l1, l2, carry);
if (sumNode != null) {
node.next = sumNode;
}
}
return node;
}
}


Thou Art Busted! Centuries-Old 'Underground Casino' Unearthed in Russia
The object discovered by archaeologists apparently served to allow gamblers in 17th century Russia to indulge in their vice while avoiding the watchful eyes of the tsar’s men as gambling was outlawed back then.
A peculiar find was recently made in the Russian city of Pskov where archaeologists have discovered what they jokingly described as an "underground casino" which dates back to the second half of 17th century.
The artefact in question, unearthed in the vicinity of a now defunct local thermal power station, is a wooden bench with what appears to be an alquerque (a strategy game similar to fanorona and checkers) board carved into it.
As scholars explained in an Instagram post, gambling in Russia was outlawed during the reign of Tsar Alexis I, which led to gamblers devising various methods to hide their activities as punishment for such transgression was rather severe.
"If some unwanted visitors come knocking, the board could be quickly covered with a rag or a person could simply sit upon it, thus hiding it from prying eyes," the message said.
The archaeologists also noted that they’ve already found a considerable number of alquerque pieces prior to the discovery of this improvised board which they described as “yet another testament to the diversity of Pskov residents’ pastimes” back then.


The Holodeck report uses PivotTables which are interactive tables that quickly combine and compare large amounts of data. You can rotate its rows and columns to see different summaries of the data, and you can expand the details of the table to drill down on more important information.
You can drag irrelevant information out of the table to remove it from view. Filter the table views to show only pertinent information, by right clicking the table header and clicking AutoFilter. Sort your tables ascending or descending by right clicking the table header and clicking either Filter Ascending or descending.
Reorder your tables to view certain information near to one another by dragging and dropping entire rows from one place to the other, the PivotTable functionality will keep your information in order.
For more information about PivotTables please see this Microsoft Help Demo.


Deep Learning Generalization
- A Practical Bayesian Framework for Backpropagation Networks
- Everything that Works Works Because it’s Bayesian: Why Deep Nets Generalize?
- Reconciling modern machine learning practice and the bias-variance trade-of (paper!)
- Casting ML Algorithms as Bayesian Approximations:
- Classical ML:
- L1 regularization is just MAP estimation with sparsity inducing priors
- SVMS, support vector machines, are just the wrong way to train Gaussian processes
- Herding is just Bayesian quadrature done slightly wrong
- DL:
- LeCun Post on Uncertainty in Neural Networks
- Dropout is just variational inference done wrong: Dropout as a Bayesian Approximation
- Deep Nets memorize
- Classical ML:
-
Why do Deep Nets Generalize?:
One possibility is: “because they are really just an approximation to Bayesian machine learning.” - Ferenc- SGD:
SGD could be responsible for the good generalization capabilities of Deep Nets.- SGD finds Flat Minima.
- A Flat Minima is a minima where the Hessian - and consequently the inverse Fisher information matrix - has small eigenvalues.
- Flat might be better than sharp minima:
If you are in a flat minimum, there is a relatively large region of parameter space where many parameters are almost equivalent inasmuch as they result in almost equally low error. Therefore, given an error tolerance level, one can describe the parameters at the flat minimum with limited precision, using fewer bits while keeping the error within tolerance. In a sharp minimum, you have to describe the location of your minimum very precisely, otherwise your error may increase by a lot.
- (Keskar et al, 2017) show that deep nets generalize better with smaller batch-size when no other form of regularisation is used.
- And it may be because SGD biases learning towards flat minima, rather than sharp minima.
- (Wilson et al, 2017) show that these good generalization properties afforded by SGD diminish somewhat when using popular adaptive SGD methods such as Adam or rmsprop.
- Though, there is contradictory work by (Dinh et al, (2017) who claim sharp minima can generalize well, too.
Also, (Zhang et al, 2017) - One conclusion is: The reason deep networks work so well (and generalize at all) is not just because they are some brilliant model, but because of the specific details of how we optimize them.
Stochastic gradient descent does more than just converge to a local optimum, it is biased to favor local optima with certain desirable properties, resulting in better generalization. - Is SGD Bayesian?
- Some work:
- Flat Minima is Bayesian:
It turns out, (Hochreiter and Schmidhuber, 1997) motivated their work on seeking flat minima from a Bayesian, minimum description length perspective.
Even before them, (Hinton and van Camp, 1993) presented the same argument in the context of Bayesian neural networks.
- SGD finds Flat Minima.
Indeed, the empirical evidence about generalization in deep neural networks with a huge number of weights is that they generalize better than the theory would predict. This is not just in terms of the loose bounds that the theory provides. The performance is better than even the “tight” rules of thumb that were based on the theory and worked in practice.
This is not the first time this happens in ML. When boosting was the method of choice, generalization was better than it should be. Specifically, there was no overfitting in cases where the model complexity was going up and overfitting would be expected. In that case, a theoretical approach to explain the phenomenon based on a cost function other than 𝐸𝑖𝑛 [in-sample error] was advanced. It made sense but it didn’t stand up to scrutiny, as minimizing that cost function directly (instead of letting it be minimized through the specific structure of the AdaBoost algorithm for instance) suffered from the usual overfitting. There was no conclusive verdict about how AdaBoost avoids overfitting. There were bits and pieces of intuition, but it is difficult to tell whether that was explanation or rationalization.
In the case of neural networks, there have also been efforts to explain why the performance is better. There are other approaches to generalization, e.g., based on “stability” of learning, that were invoked. However, the theoretical work on stability was based on perturbation of the training set that does not lead to a significant change in the final hypothesis. The way stability is discussed in the results I have seen in neural networks is based on perturbation of the weights that does not lead to a significant change. It thus uses the concept of stability rather than the established theoretical results to explain why generalization is good. In fact, there are algorithms that deliberately look for a solution that has this type of stability as a way to get good generalization, a regularization of sorts.
It is conceivable that the structure of deep neural networks, similar to the case of AdaBoost, tends to result in better generalization than the general theory would indicate. To establish that, we need to identify what is it about the structure that makes this happen. In comparison, if you study SVM as a model without getting into the notion of support vectors, you will encounter “inexplicable” good generalization. Once you know about how the number of support vectors affects generalization, the mystery is gone.
Let me conclude by emphasizing that the VC theory is not violated in any of these instances, since the theory only provides an upper bound. Those cases show a much better performance for particular models, but the performance is still within the theoretical bound. What would be a breakthrough is another, better bound that is applicable to an important class of models. For example, the number of parameters in deep neural networks is far bigger than previous models. If better generalization bounds can be proven for models with huge number of parameters, for instance, that would be quite a coup.
Ways to Explain Generalization:
- The bigger (deeper) the network the easier it is to train (because the optimization landscape becomes simpler); so this + early-stopping can lead to good solutions that wouldn’t utilize the more funky functions we can represent with the bigger network.
Intuition:- Optimizing/Searching over a huge function space (e.g. all possible functions), it is easier to “steer” into the correct one, whereas if you’re restricted and you can only have certain types of functions, then you need to find your path from one to the other which is generally harder to do.
- Another view of the same is that, if you have a really large network w/ random initialization (e.g. infinitely big) such that a subnetwork exists that solves the problem (i.e. what you are searching for), so if it’s already present, backprop can choose the direction of the parameters leading to that subnetwork cuz that’ll make the biggest improvement, and you will learn very quickly.
- On the other hand, having a huge number of parameters can lead many of the parameter settings to be equally good/leading to the same solution (since the NN is never unique), so finding any local minima yields a good solution.
Moreover, perhaps some of these local minimas are actually bad if they were to be optimized fully however, w/ early-stopping you can stop at the configuration of the parameters that would yield a good result w/o overtraining into that local minima that would lead to overlearning.
While even when early-stopping in a small network, the parameters setting we learn is already at such a low/deep point in the local minima that it already “overlearned”? (or that most of these local minimas are not great)
Notes:
- “We can connect this finding to recent work examining the generalization of large neural networks. Zhang et al. (2017) observe that deep neural networks seemingly violate the common understanding of learning theory that large models with little regularization will not generalize well. The observed disconnect between NLL and 0/1 loss suggests that these high capacity models are not necessarily immune from overfitting, but rather, overfitting manifests in probabilistic error rather than classification error.” - On Calibration of Modern Neural Networks
- Another w
- SGD:
Misc.
- NLP Research and What’s Next:
Progress in NLP/AI:- Machine learning with feature engineering:
Learning weights for engineered featured. - Deep learning for feature learning:
Using DL to automatically learn features (e.g. embeddings). - Deep architecture engineering for single tasks:
Each sub-field in NLP converged to a particular Network Architecture. - (NOW) Deep Single MultiTask Model
Limits of Single-Task Learning:
- Great performance improvements in recent years given {dataset, task, model, metric}
- We can hill-climb to local optima as long as \(\vert \text{dataset} \vert > 1000 \times C\)
- For more general AI, we need continuous learning in a single model instead
- Models typically start from random or are only partly pre-trained
There is no single blocking task in Natural Language. (compared to Classification in Vision)
HOWEVER, MultiTask Learning is a blocker for general NLP systems.Why has weight and model sharing not happened as much in NLP?:
- NLP requires many types of reasoning:
Logical, Linguistic, Emotional, Visual, etc. - Requires Short and Long-term Memory
- NLP had been divided into intermediate and separate tasks to make progress:
\(\rightarrow\) Benchmark chasing in each community - Can a single unsupervised task solve it all? No.
- Language clearly requires supervision in nature (kid in jungle -> easy to develop vision not language).
How to express many NLP tasks in the same framework?:
- NLP Frameworks:
- Sequence Tagging: named entity recognition, aspect specific sentiment
- Text Classification: dialogue state tracking, sentiment classification
- Seq2seq: machine translation, summarization, question answering
- NLP SuperTasks:
Hypothesis: The following are Three Equivalent SuperTasks of NLP where we can pose all possible NLP tasks as either one of them:- Language Modeling: condition on Question+Context, then generate
- Question Answering: Question=Task
- Dialogue: open-ended, limited datasets
Thus, Question Answering is the most appropriate SuperTask to choose to cast NLP problems in.
The Natural Language Decathlon (decaNLP):
- Multitask Learning as Question Answering:
Casts all NLP tasks as Question Answering problems. - decaNLP Tasks:
- Question Answering
- Machine Translation
- Summarization
- Natural Language Inference
- Sentiment Classification
- Semantic Role Labeling
- Relation Extraction
- Dialogue
- Semantic Parsing
- Commonsense Reasoning
- Meta-Supervised Learning: From \(\{x, y\}\) to \(\{x, t, y\}\) (\(t\) is the task)
- Use a question, \(q\), as a natural description of the task, \(t\), to allow the model to use linguistic information to connect tasks
- \(y\) is the answer to \(q\) and \(x\) is the context necessary to answer \(q\)
- Model Specifications for decaNLP:
- No task-specific modules or parameters because we assume the task ID is not available
- Must be able to adjust internally to perform disparate tasks
- Should leave open the possibility of zero-shot inference for unseen tasks
- Machine learning with feature engineering:
Papers
- Fast Weights:
Fast-Weights:
- FAST-WEIGHTS:
- Basic Idea:
- on each connection: Total weight = Sum of:
- Standard Slow Weights. This learns slowly & (may also) decay slowly. Holds long term Knowledge.
- The Fast Weights: Learns quickly, decays quickly, Holds Temp. info.
- on each connection: Total weight = Sum of:
- Motivation:
- Priming: listen to a word \(\rightarrow\) recognize many minutes later in Noisy Env.
- If we had localist Representation could just temporarily lower the threshold of the “cucumber” weight
- If we use point Attractors instead of “localist units” we con temporarily increase the “attractiveness” of the words unit (by changing the weights between the neurons in that pattern of activity)
- Priming: listen to a word \(\rightarrow\) recognize many minutes later in Noisy Env.
-
Weight Matrices VS Activity Vectors:
Weight Matrices are better:
(1) More capacity \(N^2\) vs \(N\) (2) A fast weight matrix of \(1000 x 1000\) can easily make 100 attractors more “attractive” - Three ways to store Temp. knowledge:
- LSTM, Stores it in its activity vectors [hidden weights] \(\implies\) Irrelevant temp Memory interferes with on-going process
- An additional External memory to LSTM, can store without interference but need to - learn when to read/white.
- Fast-Weights: Allow the temporal Knowledge to be stored without having any extra neurons.
They just make some attractors easier to fall into; and they also “flavor” the attractor by slightly changing the activity vector you end up with.
- Basic Idea:
- FAST-WEIGHTS:
Observations, Ideas, Questions, etc.
- Observations from Papers/Blogs/etc.:
- deep neural networks seemingly violate the common understanding of learning theory that large models with little regularization will not generalize well. The observed disconnect between NLL and 0/1 loss suggests that these high capacity models are not necessarily immune from overfitting, but rather, overfitting manifests in probabilistic error rather than classification error. paper
- It is also interesting to see that the global average pooling operation can significantly increase the classification accuracy for both CNNs and CNTKs. From this observation, we suspect that many techniques that improve the performance of neural networks are in some sense universal, i.e., these techniques might benefit kernel methods as well lnk.
- Is Optimization a Sufficient Language for Understanding Deep Learning?
- Conventional View (CV) of Optimization:
Find a solution of minimum possible value of the objective, as fast as possible. - If our goal is mathematical understanding of deep learning, then the CV of Opt is potentially insufficient.
- Conventional View (CV) of Optimization:
- Representable Does Not Imply Learnable.
- Recurrent Models in-practice can be approximated with FeedForward Models:
FF models seem to match or exceed the performance of Recurrent models on almost all tasks.
Suggesting that Recurrent models extra expressiveness might not be needed/used.
The following is conjectured: “Recurrent models trained in practice are effectively feed-forward”.- This paper (Stable Recurrent Models) proves that stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent.
- The unlimited context offered by recurrent models is not strictly necessary for language modeling.
i.e. it’s possible you don’t need a large amount of context to do well on the prediction task on average. Recent theoretical work offers some evidence in favor of this view.
- Ideas:
Project Ideas:- NMF on Text Data
- read old Schmidhuber/Hinton Papers and reapply them on current hardware/datasets
- word-embeddings and topic-modeling and NMF, on ARXIV ML Papers
- Generative Adversarial Framework for Speech Recognition
Research Ideas:
- Overfitting on NLL to explain Deep-NN generalization
- DeepSets and Attention theoretical guarantees
- Experimenting w/ Mutual Info (IB) w/ knowledge distillation
- Language Modeling Decoding using Attention (main problem is beam size = greedy)
- Experiment with Lots of data w/ simpler models VS Less data w/ advanced models
(“A dumb algorithm with lots and lots of data beats a clever one with modest amounts of it.”) - To measure the effect of depth: construct a dataset that requires a deep network to model efficiently
- Weights that generalize the most have the least gradient when training on a new dataset (remember: cats vs dogs -> foxes)
- K-Separable learning instead of linearly (2-)separable learning; the output layer dictates the configuration of the transformed input data to be “classified”
- wrt. Karpathys “Unreasonable Effectiveness of RNNs” post and Y.Goldbergs “Unreasonable Effectiveness of (Char-Level) n-grams” post: Do the complex rules learned by an LSTM architecture (considering its inductive biases) e.g. neuron that counts, tracks indentation, etc. have better generalization than n-gram probabilities? Do these rules imply learning a better method, algorithm, mechanism, etc.?
- Golberg claims that RNNs are impressive because of “context awareness” (in C-code generation syntax).
- (compare the number \(n\) of n-tuples VS dimension size of \(h\))
(hint: google seems to think it’s \(n=13\) beats infinite \(h\)) - Is this why/how NNs generalize?
- wrt. Unintended Memorization in Neural Networks (Blog+Paper): it proposes an attack to extract sensitive info from a model trained on private data (using a “canary”). I can refine the attack much further by exploiting the fact that the model is trained to maximize NLL and it will give the training data higher probability.
- Can we use the ideas in the paper on differential privacy and unintended memorization in NNs to help learn more generalizable models/weights/patterns?
- Questions:
- Does Hinge Loss Maximize a margin when used with any classifier?
- How does LDA do feature extraction / dim-red?
- Time series data is known to posses linearity?
- How can we frame the Abstract Modeling Problem?
- How do humans speak (generate sentences)? They definitely do not just randomly sample from the distribution of natural language. Then, how should we teach models to speak, respond, think, etc.?
- Is it true that research “should” be hypothesis -> experiments and not the other way like in AI?
- Where does “Game Theory” fit into AI, really?
- General Notes & Observations:
- Three Schools of Learning: (1) Bayesians (2) Kernel People (3) Frequentists
- Bayesians’, INCORRECTLY, claimed that:
- Highly over-parametrised models fitted via maximum likelihood can’t possibly work, they will overfit, won’t generalise, etc.
- Any model with infinite parameters should be strictly better than any large, but finite parametric model.
(e.g. nonparametric models like kernel machines are a principled way to build models with effectively infinite number of parameters)
- Don’t try averaging if you want to synchronize a bunch of clocks! (Ensemble Averaging/Interview Q)
The noise is not Gaussian.
Instead, you expect that many of them would be slightly wrong, and a few of them would have stopped or would be wildly wrong and by averaging you end making them all significantly wrong. - Generalization:
- It seems that Occams Razor is equivalent to saying that a “non-economical” model is not a good model. So, can we use Inf-Th to quantify the information in these models?
The idea that a simple model e.g. “birds fly”, is much better than a much more complicated and hard to encode model e.g. “birds fly except chicken, penguins, etc.”
- It seems that Occams Razor is equivalent to saying that a “non-economical” model is not a good model. So, can we use Inf-Th to quantify the information in these models?
- Width vs Depth in NN Architectures:
Thinking of the NN as running a computer program that performs a calculation, you can think of width as a measure of how much parallelization you can have in your computation, and depth as a measure of serialization. - A Hopfield net the size of a brain (connectivity patterns are quite diff, of course) could store a memory per second for 450 years.
- Overfitting in the Brain: You can call it superstition or bad habits. Even teach some to animals.
- Real world data prefers lower Kolmogorov complexity (and hence enables the ability to learn) is a very strange fundamental asymmetry in nature??
It’s as puzzling as having so much matter than antimatter. - An SVM is, in a way, a type of neural network (you can learn a SVM solution through backpropagation)
- In CNNs there are no FC layers, they are equivalent to \(1 \times 1\) convolutions link
- In support of IB (Information Bottleneck) Theory: this paper suggests that Memorization happens early in training.
- Insights:
- Utkarsh idea of co-adaptation is similar to DROPOUT Motivation: hidden units co-adapting to each other on training data
- Attention Functions Properties: monotonicity, sparsity etc.
- Think about Learning, Overfitting, and Regularization in terms of accidental regularities/patterns due to the particular sample
- Process of Learning:
- Fit Most Common Pattern vs Fit Easiest Patterns?
- Fit Most Common Pattern vs Fit Easiest Patterns?
- Fit next Most Common Pattern vs Fit next Easiest Patterns? ..
- …
- Fit patterns that exist per/sample (e.g. Noise)
- Overfitting:
Happens when there are patterns that manifest in the particular sample that might not have been that common when looking at a larger/different sample. - Regularization:
Stops the model from learning the least-common/hardest patterns by putting some sort of threshold. - When we fit the model, it cannot tell which regularities are real and which are caused by sampling error.
The higher the capacity, the better it fits the sampling error. - This ties in nicely with the idea of “match your model capacity to the amount of data that you have and NOT to the target capacity”.
-
- The bias term is big if the model has too little capacity to fit the data.
- The variance term is big if the model has so much capacity that it is good at fitting the sampling error in each particular training set.
- Process of Learning:
- Although Recurrent models do not have an “explicit way to model long and short range dependencies”, FastWeights does.
- Although Recurrent models do not have an “explicit way to model long and short range dependencies”, FastWeights does.
- Experiments & Results:


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{{ section.description }}


HiveMind Module Descriptor
The purpose of the module descriptor is to provide a runtime and compile-time description of each HiveMind module in terms of service and configuration extension points and contributions to those extension points.
The descriptor is named hivemodule.xml and is stored in the META-INF directory of the module.
The root element of the descriptor is the <module> element.
attribute
<attribute> is used to define an attribute within an <element>. Inside a <contribution>, only known attributes are allowed in elements; unknown attributes will be logged as an error and ignored. In addition, some attributes are required; again, errors occur if the contributed element does not provide a value for the attribute.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| name | string | yes | The name of the attribute. |
| required | boolean | no | If true, the attribute must be provided in the contributed configuration element. The default is false. |
| unique | boolean | no | If true, the attribute must contain a unique value with respect to all other contributions to the same configuration point. The default is false. |
| translator | string | no | The translator configuration that is used to convert the attribute into a useable type. By default, the attribute is treated as a single string. |
configuration-point
The <configuration-point> element defines a configuration extension point.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| id | string | yes | The simple id of the service extension point. The fully qualified id for the extension point is created by prefixing with the module's id (and a dot). |
| visibility | public | private | no | If private is specified, then the configuration point may only be referenced (or contributed to) from within the same module. It will not be visible to the application (via the Registry) if private. The default is public. |
| occurs | unbounded | 0..1 | 1 | 1..n | none | no | The number of contributions allowed:
Note
none doesn't
make sense for occurances to a configuration point, but does occasionally make sense for parameters to a
factory.
|
| schema-id | string | no | Used to reference a <schema> (in the same module, or a different one) that defines the format of contributions into the configuration point. This may be omitted, in which case the extension point will contain a list of Element . |
Contains: <schema>
<configuration-point> only defines a configuration point, it does not supply any data into that point. The <contribution> element is used to provide such data.
contribution
The <contribution> element contributes elements to an existing configuration extension point.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| configuration-id | string | yes | Either the id of an <configuration-point> within the module, or the fully qualified id of an <configuration-point> in another module. |
| if | string | no | A conditional expression controlling whether the contribution is used or ignored. |
The content of the <contribution> consists of elements. These elements are converted, in accordance with the configuration point's <schema>, into Java objects.
conversion
<conversion> is an alternative to <rules> that is generally simpler and more concise. An <element> should contain <conversion> or <rules> but not both.
<conversion> is geared towards the typical case; a straight-forward mapping of the element to an instance of a Java class, and a mapping of the element's attributes to the properties of the Java object.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| class | string | yes | The fully qualified name of a Java class to instantiate. |
| parent-method | string | no | The name of a method of the parent object used to add the created object to the parent. The default, addElement, is appropriate for top-level <element>s. |
Contains: <map>
Each attribute will be mapped to a property. A limited amount of name mangling occurs: if the attribute name contains dashes, they are removed, and the character following is converted to upper case. So, an attribute named "complex-attribute-name" would be mapped to a property named "complexAttributeName". Only attributes identified with a <attribute> element will be mapped, others will be ignored.
create-instance
<create-instance> is used, within <service-point> and <implementation> to create the core service implementation for a service by instantiating a class. This is appropriate for simple services that require no explicit configuration.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| class | class name | yes | The class name to instantiate. May use instance initializer syntax. |
| model | primitive | singleton | threaded | pooled | no | The model used to construct and manage the service. singleton is the default. |
Additional service models can be defined via the hivemind.ServiceModels configuration point.
dependency
<dependency> is used, within <module> to specify a dependency upon another required module.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| module-id | string | yes | The id of the required module. |
| version | version number | no | The version of the required module as a dotted sequence of three numbers. Example: "1.0.0". With no version specified any version of the required module is matched. |
In the future HiveMind will support other more sophisticated version matching types for dependencies.
element
The <element> element is used to define an element in a the <schema>. <element> may also be nested within another <element>, to indicate an element that may be enclosed within another element.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| name | string | yes | The name of the element. |
| content-translator | string | no | The translator configuration that is used to convert the element's content into a useable type. By default, the content is treated as a single string. |
| key-attribute | string | no | The name of this element's <attribute> whose translated value will be used as a unique key for configuration contributions corresponding to this element. The contributions can then be accessed in a Map using this key. The specified <attribute> is implicitly marked as required and unique. |
Contains: <attribute>, <conversion>, <element>, <rules>
Future enhancements to the HiveMind framework will include greater sophistication in defining configuration content.
implementation
The <implementation> element contributes a core implementation or interceptors to a service extension point.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| service-id | string | yes | The id of the service to extend; this may be a fully qualified id, or the local id of a service within the same module. |
| if | string | no | A conditional expression controlling whether the contribution is used or ignored. |
Contains: <create-instance>, <interceptor>, <invoke-factory>
interceptor
<interceptor> contributes an interceptor factory to a service extension point. An interceptor factory is a service which implements the ServiceInterceptorFactory interface.
When the service is constructed, each invoked interceptor factory will fabricate an interceptor class to provide additional functionality for the service.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| service-id | string | yes | The id of the service that will create the interceptor for the service. This may be the local id of a service defined within the same module, or a fully qualified id. |
| before | string | no | A list of interceptors whose behavior should come later in execution than this interceptor. |
| after | string | no | A list of interceptors whose behavior should come earlier in execution than this interceptor. |
| name | string | no | The name to use for ordering this interceptor. Defaults to the service-id. This comes in handy when using the same factory to create multiple unique interceptors which should be able to be ordered with respect to one another. |
Like a service implementation factory, a service interceptor factory may need parameters. As with <invoke-factory>, parameters to the interceptor factory are enclosed by the <interceptor> element. The service interceptor factory will decode the parameters using the schema identified by its parameters-schema-id attribute.
Interceptor ordering is based on dependencies; each interceptor can identify, by name, other interceptors. Interceptors in the before list are deferred until after this interceptor. Likewise, this interceptor is deferred until after all interceptors in the after list.
The value for before or after is a list of interceptor names seperated by commas. Service ids may be unqualified if they are within the same module. Alternately, the fixed value * may be used instead of a list: this indicates that the interceptor should be ordered absolutely first or absolutely last.
invoke-factory
The <invoke-factory> element is used to provide a service implementation for a service by invoking another service, a factory service.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| service-id | string | no | The id of the factory service. This may be a simple id for a service within the same module, or a fully qualified service id. The default, if not specified, is hivemind.BuilderFactory. |
| model | primitive | singleton | threaded | pooled | no | The model used to construct and manage the service. singleton is the default. |
A service factory defines its parameters in terms of a schema. The content of the <invoke-factory> is converted, in accordance with the factory's schema, and provided to the factory.
map
The <map> element appears within <conversion> to override the default mapping from an attribute to a property. By default, the property name is expected to match the attribute name (with the name mangling described in the description of <conversion>); the <map> element is used to handle exceptions to the rule.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| attribute | string | yes | The name of the attribute, which should match a name defined by an<attribute> (of the enclosing <element>). |
| property | string | yes | The corresponding property (of the Java object specified by the enclosing <conversion>) |
module
The <module> element is the root element.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| id | string | yes | The id should be a dotted sequence, like a package name. In general, the module id should be the package name. |
| version | version number | yes | The version of the module as a dotted sequence of three numbers. Example: "1.0.0" |
| package | string | no | The package which contains Java types within the module. Defaults to the module's id if not specified. |
Contains: <contribution>, <configuration-point>, <dependency>, <implementation> , <service-point>, <schema>, <sub-module>
Every module is mapped to a Java package, using the package attribute (or the module's id if not specified). When a class name is referenced within a module, the name will be searched for within the package if not found normally. This applies to all contributions, etc., within the module ... virtually anywhere a Java class name can be used. Literally, if the class can't be found by the provided type, the module's package name is prefixed and a second search is attempted. The interface attribute of the <service-point> element is slightly different.
parameters-schema
The <parameters-schema> element is identical to the <schema> element, but only appears inside <service-point>, to define the schema for the parameters for a service implementation factory or service interceptor factory.
rules
<rules> is a container for element and attribute parsing rules within an <element>. These rules are responsible for converting the contributed element and its attributes into objects and object properties. The available rules are documented separately .
schema
The <schema> element is used to describe the format of element contributions to an <configuration-point>, or parameters provided to a service or interceptor factory.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| id | string | yes | Assigns a local id to the schema that may be referenced elsewhere. |
| visibility | public | private | no | If private is specified, then the schema may only be referenced from within the same module. The default is public. |
Contains: <element>
At a future time, the <schema> element will be extended to provide more options, to provide more precise control over the elements that may be provided in an <contribution>. At this time, a <schema> is simply a list of <element> elements.
service-point
The <service-point> element defines a service extension point.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| id | string | yes | The simple id of the service extension point. The fully qualified id for the extension point is created by prefixing with the module's id (and a dot). |
| interface | class name | no | The name of the Java interface (or class) supplied by this service extension point. If no value is provided, the interface defaults to the service id (and will then be qualified by the module's package). |
| visibility | public | private | no | If private is specified, then the service may only be referenced (or contributed to) from within the same module. It will not be visible to the application (via the Registry) if private. The default is public. It is common for a module to have several public services acting as a facade over many more private services. |
| parameters-schema-id | string | no | Used to reference a <schema> (in the same module, or a different one) that defines parameters used by the service. This is used when the service being defined is a ServiceImplementationFactory or a ServiceInterceptorFactory. |
| parameters-occurs | unbounded | 0..1 | 1 | 1..n | none | no | The number of parameter element contributions allowed:
|
Contains: <create-instance>, <interceptor>, <invoke-factory> , <parameters-schema>
If the specified interface name is unqualified (does not contain a period), then it is prefixed with the module's package name. Note that this is slightly different than how partial class names are resolved elsewhere in a module descriptor.
sub-module
The <sub-module> element is used to identify an additional HiveMind module deployment descriptor. This is used when a single JAR file contains logically distinct packages, each of which should be treated as an individual HiveMind module. This can also be useful as a way to reduce developer conflict against a single, large, central module descriptor by effectively breaking it into smaller pieces. Sub-modules identified in this way must still have their own unique module id.
| Attribute | Type | Required ? | Description |
|---|---|---|---|
| descriptor | string | yes | Location of the module descriptor. |
| if | string | no | A conditional expression controlling whether the sub-module is included or ignored. |
The descriptor should be specified as a relative path, either the name of another module descriptor within the same folder, or within a child folder.


Key bindings: Insert coin: 5 Start: 1 Fire: Ctrl (left or right) Thrust: Up Arrow Rotate Left: Left Arrow Rotate Right: Right Arrow


The project is divided in three main parts. The first part deals with a simple pairing model and the development of a shell-model program related to this model. This program can then be developed into a more general shell-model program that allows you to study general nuclear structure problems. That is the second part of the project. In parallel, we will also use NushellX in order to perform more advanced shell-model studies and compare the results obtained with your own shell-model code to those of NushellX. We expect you to form working groups consisting of typically three (or more) participants. Every group should establish its own Github or Gitlab repository for the project.
In the first part of the project we will thus work with a simplified Hamiltonian consisting of a one-body operator and a so-called pairing interaction term. It is a model which to a large extent mimicks some central features of atomic nuclei, certain atoms and systems which exhibit superfluiditity or superconductivity. Pairing plays a central role in nuclear physics, in particular, for identical particles it makes up large fractions of the correlations among particles. The partial wave \( ^{1}S_0 \) of the nucleon-nucleon force plays a central role in setting up pairing correlations in nuclei. Without this particular partial wave, the \( J=0 \) ground state spin assignment for many nuclei with even numbers of particles would not be possible.
We define first the Hamiltonian, with a definition of the model space and the single-particle basis. Thereafter, we present the various steps which are needed to develop a shell-model program for studying the pairing problem.
The Hamiltonian acting in the complete Hilbert space (usually infinite dimensional) consists of an unperturbed one-body part, \( \hat{H}_0 \), and a perturbation \( \hat{H}_I \).
We limit ourselves to at most two-body interactions, our Hamiltonian is then represented by the following operators $$ \begin{equation} \hat{H} = \hat{H}_0 +\hat{H}_I=\sum_{pq}\langle p |h_0|q\rangle a_{p}^{\dagger}a_{q} +\frac{1}{4}\sum_{pqrs}\langle pq| V|rs\rangle a_{p}^{\dagger}a_{q}^{\dagger}a_{s}a_{r}, \label{eq:hamiltonian} \end{equation} $$ where \( a_{p}^{\dagger} \) and \( a_{q} \) etc are standard fermion creation and annihilation operators, respectively, and \( pqrs \) represent all possible single-particle quantum numbers. The full single-particle space is defined by the completeness relation \( \hat{1} = \sum_{p =1}^{\infty}|p \rangle \langle p| \). In our calculations we will let the single-particle states \( |p\rangle \) be eigenfunctions of the one-particle operator \( \hat{h}_0 \).
The above Hamiltonian acts in turn on various many-body Slater determinants constructed from the single-basis defined by the one-body operator \( \hat{h}_0 \).
Our specific model consists of only \( 2 \) doubly-degenerate and equally spaced single-particle levels labeled by \( p=1,2,\dots \) and spin \( \sigma=\pm 1 \). In Eq. \eqref{eq:hamiltonian} the labels \( pqrs \) could also include spin \( \sigma \). From now and for the rest of this project, labels like \( pqrs \) represent the states without spin. The spin quantum numbers need to be accounted for explicitely.
We write the Hamiltonian as $$ \begin{equation*} \hat{H} = \hat{H}_0 +\hat{H}_I=\hat{H}_0 + \hat{V} , \end{equation*} $$ where $$ \begin{equation*} \hat{H}_0=\xi\sum_{p\sigma}(p-1)a_{p\sigma}^{\dagger}a_{p\sigma}. \end{equation*} $$ Here, \( H_0 \) is the unperturbed Hamiltonian with a spacing between successive single-particle states given by \( \xi \), which we will set to a constant value \( \xi=1 \) without loss of generality.
The two-body operator \( \hat{V} \) has one term only. It represents the pairing contribution and carries a constant strength \( g \) and is given by $$ \begin{equation*} \langle q+q-| V|s+s-\rangle = -g \end{equation*} $$ where \( g \) is a constant. The above labeling means that for a general matrix elements \( \langle pq| V|rs\rangle \) we require that the states \( p \) and \( q \) (and \( r \) and \( s \)) have the same number quantum number \( q \) but opposite spins. The two spins values are \( \sigma = \pm 1 \). When setting up the Hamiltonian matrix you need to figure out how to make the two-body interaction antisymmetric. The variables \( \sigma=\pm \) represent the two possible spin values. The interaction can only couple pairs and excites therefore only two particles at the time.
In our model we have kept both the interaction strength and the single-particle level as constants. In a realistic system like the atomic nucleus this is not the case.
The unperturbed Hamiltonian \( \hat{H}_0 \) and \( \hat{V} \) commute with the spin projection \( \hat{S}_z \) and the total spin \( \hat{S}^2 \). This is an important feature of our system that allows us to block-diagonalize the full Hamiltonian. In this project we will focus only on total spin \( S=0 \), this case is normally called the no-broken pair case.
This is an important feature of our system that allows us to block-diagonalize the full Hamiltonian. We will focus on total spin \( S=0 \). In this case, it is convenient to define the so-called pair creation and pair annihilation operators $$ \begin{equation*} \hat{P}^{+}_p = a^{\dagger}_{p+}a^{\dagger}_{p-}, \end{equation*} $$ and $$ \begin{equation*} \hat{P}^{-}_p = a_{p-}a_{p+}, \end{equation*} $$ respectively.
The Hamiltonian (with \( \xi=1 \)) we will use can be written as $$ \begin{equation*} \hat{H}=\sum_{p\sigma}(p-1)a_{p\sigma}^{\dagger}a_{p\sigma} -g\sum_{pq}\hat{P}^{+}_p\hat{P}^{-}_q. \end{equation*} $$ Show that Hamiltonian commutes with the product of the pair creation and annihilation operators. This model corresponds to a system with no broken pairs. This means that the Hamiltonian can only link two-particle states in so-called spin-reversed states.
Find the eigenvalues by diagonalizing the Hamiltonian matrix. Vary your results for selected values of \( g\in [-1,1] \) and comment your results.
Enlarge now your system to six and eight fermions and to \( p=6 \) and \( p=8 \) single-particle states, respectively. Run your program for a degenerate single-particle state with degeneracy \( \Omega \) and test against the exact result for the ground state. Introduce thereafter a finite single-particle spacing and study the results as you vary \( g \), as done in b) and c). Comment your results.
The way we will set up the Slater determinants here follows a simple odometric recipe. The way it is done in more professional codes, is to use bitwise manipulations. The latter is a possible extension/challenge for those interested.
Part two of our project consists of at developing your own shell-model code that can perform shell-model studies of the oxygen isotopes using standard effective interactions (provided by us) using as example the \( 1s0d \) shell as model space. You may also need to consider a bit representation and manipulation of Slater determinants and to implement the Lanczos algorithm. These details will be discussed during our lectures.
The aim of this project is to study the structure of selected low-lying states of the oxygen and fluorine isotopes towards their respective dripline. These chains of isotopes have been studied extensively during the last years, with many efforts toward the understanding of their dripline properties, involving studies of low-lying excited states and electromagnetic transitions. For the oxygen isotopes, \( {}^{24}\mbox{O} \) is the last particle-stable nucleus, and for the fluorine isotopes \( {}^{31}\mbox{F} \) is assumed to the last stable one. This part can also be used to benchmark your shell-model program from the second part.
The task here is to study these isotopic chains, extract excitation energies and selected observables and compare with available data. To achieve this you will need to use an effective interaction designed for the \( 1s0d \) shell first and then, for nuclei beyond \( A=24 \) you may need to consider degrees of freedom from the \( 1p0f \) shell. Since a full calculation in these two major shells becomes quickly time-consuming for the fluorine isotopes, you will need to truncate the number of particles which can leave/occupy selected single-particle states. In the file which contains the single-particle data, you can reduce the size of the total space of Slater determinants by limiting the number of particles which can populate the \( 1p0f \) shell. Here you could limit yourselves to consider only the single-particle states \( 0f_{7/2} \) and \( 1p_{3/2} \).
In the build-up of a shell model code that is meant to tackle large dimensionalities is the action of the Hamiltonian \( \hat{H} \) on a Slater determinant represented in second quantization as $$ \begin{equation*} \vert \alpha_1\dots \alpha_n\rangle = a_{\alpha_1}^\dagger a_{\alpha_2}^\dagger \dots a_{\alpha_n}^\dagger \vert 0\rangle. \end{equation*} $$ The time consuming part stems from the action of the Hamiltonian on the above determinant, $$ \begin{equation*} \left(\sum_{\alpha\beta} \langle \alpha\vert \hat{t}+\hat{u}\vert \beta\rangle a_\alpha^\dagger a_\beta + \frac{1}{4} \sum_{\alpha\beta\gamma\delta} \langle\alpha \beta\vert \hat{V}\vert \gamma \delta\rangle a_\alpha^\dagger a_\beta^\dagger a_\delta a_\gamma\right)a_{\alpha_1}^\dagger a_{\alpha_2}^\dagger \dots a_{\alpha_n}^\dagger \vert 0\rangle. \end{equation*} $$ A practically useful way to implement this action is to encode a Slater determinant as a bit pattern. Assume that we have at our disposal \( n \) different single-particle orbits \( \alpha_0,\alpha_2,\dots,\alpha_{n-1} \) and that we can distribute among these orbits \( N\le n \) particles.
A Slater determinant can then be coded as an integer of \( n \) bits. As an example, if we have \( n=16 \) single-particle states \( \alpha_0,\alpha_1,\dots,\alpha_{15} \) and \( N=4 \) fermions occupying the states \( \alpha_3 \), \( \alpha_6 \), \( \alpha_{10} \) and \( \alpha_{13} \) we could write this Slater determinant as $$ \begin{equation*} \Phi_{\Lambda} = a_{\alpha_3}^\dagger a_{\alpha_6}^\dagger a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle . \end{equation*} $$ The unoccupied single-particle states have bit value \( 0 \) while the occupied ones are represented by bit state \( 1 \). In the binary notation we would write this 16 bits long integer as $$ \begin{equation*} \begin{array}{cccccccccccccccc} {\alpha_0}&{\alpha_1}&{\alpha_2}&{\alpha_3}&{\alpha_4}&{\alpha_5}&{\alpha_6}&{\alpha_7} & {\alpha_8} &{\alpha_9} & {\alpha_{10}} &{\alpha_{11}} &{\alpha_{12}} &{\alpha_{13}} &{\alpha_{14}} & {\alpha_{15}} \\ {0} & {0} &{0} &{1} &{0} &{0} &{1} &{0} &{0} &{0} &{1} &{0} &{0} &{1} &{0} & {0} \\ \end{array} \end{equation*} $$ which translates into the decimal number $$ \begin{equation*} 2^3+2^6+2^{10}+2^{13}=9288. \end{equation*} $$ We can thus encode a Slater determinant as a bit pattern. With \( N \) particles that can be distributed over \( n \) single-particle states, the total number of Slater determinats (and defining thereby the dimensionality of the system) is $$ \begin{equation*} \mathrm{dim}(\mathcal{H}) = \left(\begin{array}{c} n \\N\end{array}\right). \end{equation*} $$ The total number of bit patterns is \( 2^n \). We assume again that we have at our disposal \( n \) different single-particle orbits \( \alpha_0,\alpha_2,\dots,\alpha_{n-1} \) and that we can distribute among these orbits \( N\le n \) particles. The ordering among these states is important as it defines the order of the creation operators. We will write the determinant $$ \begin{equation*} \Phi_{\Lambda} = a_{\alpha_3}^\dagger a_{\alpha_6}^\dagger a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle , \end{equation*} $$ in a more compact way as $$ \begin{equation*} \Phi_{3,6,10,13} = |0001001000100100\rangle. \end{equation*} $$ The action of a creation operator is thus $$ \begin{equation*} a^\dagger_{\alpha_4}\Phi_{3,6,10,13} = a^\dagger_{\alpha_4}|0001001000100100\rangle=a^\dagger_{\alpha_4}a_{\alpha_3}^\dagger a_{\alpha_6}^\dagger a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle , \end{equation*} $$ which becomes $$ \begin{equation*} -a_{\alpha_3}^\dagger a^\dagger_{\alpha_4} a_{\alpha_6}^\dagger a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle =-|0001101000100100\rangle. \end{equation*} $$ Similarly $$ \begin{equation*} a^\dagger_{\alpha_6}\Phi_{3,6,10,13} = a^\dagger_{\alpha_6}|0001001000100100\rangle=a^\dagger_{\alpha_6}a_{\alpha_3}^\dagger a_{\alpha_6}^\dagger a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle , \end{equation*} $$ which becomes $$ \begin{equation*} -a^\dagger_{\alpha_4} (a_{\alpha_6}^\dagger)^ 2 a_{\alpha_{10}}^\dagger a_{\alpha_{13}}^\dagger \vert 0 \rangle =0! \end{equation*} $$ This gives a simple recipe:
from \( \Phi_{0,3,6,10,13}= |1001001000100100\rangle \).
This operation gives \( |0001001000100100\rangle \).
Similarly, we can form \( a^\dagger_{\alpha_4}a_{\alpha_0}\Phi_{0,3,6,10,13} \), say, by adding \( |0000100000000000\rangle \) to \( a_{\alpha_0}\Phi_{0,3,6,10,13} \), first checking that their logical sum is zero in order to make sure that orbital \( \alpha_4 \) is not already occupied. It is trickier however to get the phase \( (-1)^l \). One possibility is as follows


This are just some basic options to customize the behaviour of this extension. Nothing extraordinary right now!
All position details refer to the original Twitter homepage when logged in!


--- layout: post title: What is the place of 'emotion' in the Christian life? date: 2011-08-31 tags: - Emotionalism - Emotions - Experience - Rationalism - Reason - Sanctification - The Christian Life meta: pvc_views: "56990" views: "690" dsq_thread_id: "748104287" author: kylegroger permalink: /content/what-place-emotion-christian-life/ excerpt: "\r \"Listen to your heart\" - is that good or bad advice?\r Should we subdue or accept what goes through our hearts? \r Does God care about the state of our emotions and feelings? \r \r If there is one question which has been on my mind lately, it is this:\r \ What is the place of 'emotion' in the Christian life?" hidden: false featured: false ---
I've recently been diagnosed with depression. So I've been pretty man down lately. I have started on a dose of anti-depressants, and they're slowly kicking into my system. As I was on my way up I was hit by a solid bout of flu. As much as flu sucks, I honestly quite enjoy the time in bed to sleep, read and think.
During this time I started reading a book called 'Feel'. In it Matthew Elliott argues that if we downplay emotions in the Christian life, we distance an important, God-given part of ourselves.
Here's the description from the Amazon page:
"In Feel, Matthew Elliott takes a critical look at what our culture and many churches have taught about controlling and ignoring our emotions. He contends that some of the great thinkers of the modern era got it all wrong, and that the Bible teaches that God intends for us to live in and through our emotions. Emotions are good things that God created us to feel. Matthew helps us to understand our emotions and equips us to nurture healthy feelings and reject destructive ones. So refresh yourself, drink deeply, and learn to live with a new, passionate heart".
I'll be honest, after reading the subtitle ("The power of listening to your heart") my sensors were tingling a little. But, surprisingly, it was brilliant! It gripped my heart and sent my mind all over the place as I thought about whether it clashes with anything in my theology. I think the best part was that it got me to read the Bible and pray with a new air of excitement.
Has anyone who checks in here read it? If you have, I'd love to hear your thoughts about it. I'm convinced by his argument, and think he highlights something important which is downplayed or, at least, overlooked in most reformed circles. It has given me something to chew on as I think about how to approach my pretty significant emotional. So I'm looking for an opportunity to chat about some of the ideas he raises.
I realise that most of you won't have read it though. Obviously I recommend you give it a read. But most of you won't have the time for that. So, because I would still like you to get involved, I have made a plan for you. I have made something of a summary. Give that a read, and then let me know your thoughts below.
P.S. I've also been thinking about posting something longer on godliness, friendship and intimacy. So keep an eye out for that too.


Gremlin is part of a larger eco-system of technologies. The primary technologies to understand when using Gremlin are itemized below.
Blueprints can be considered the JDBC of the graph database community. It provides a set of interfaces that when implemented, yield a Blueprints-enabled graph database. The TinkerPop graph stack is all predicated on Blueprints and the databases that support Blueprints.
Given that Gremlin is hosted by various JVM languages, it is possible to make use of the Blueprints API from within these other JVM languages. In this way, all the utilities provided in Blueprints are easily accessible. In essence, Gremlin is a shorthand notation for many of the access patterns used when dealing with Blueprints.
Pipes is a data flow framework written in Java. Gremlin extends Pipes to support data flow processing of Blueprints-enabled graph databases. Pipes boasts a lazy evaluation model that yields memory efficient graph traversal algorithms. When coding in Pipes, in Java, code can be very verbose. Gremlin simplifies this process by compiling its shorthand notation down to Pipes. In essence, Gremlin is a Pipes compiler for other JVM languages.
Groovy is a dynamic language that serves as a superset of Java. It provide metaprogramming facilities through the Java Reflection API and as such, easily supports the creation of domain specific languages.
Gremlin is a particular DSL that manipulates the MetaClass
of various objects. Beyond that, Gremlin is full Groovy. All constructs such as variable setting, looping, branching, closures, currying, etc. are all provided to Gremlin through the Groovy language. Finally, Groovy works seamlessly with Java and vice versa. The benefit of this is that Gremlin developers are able to easily use the Java API in their algorithms.
Here is a collection of introductory texts on Groovy that may be helpful:


This analysis is a placeholder with a sample set. If you want to contribute new analyses for the site, contact Disaster Area on Discord.
- Dream Eater
- Hydro Pump / Surf
- Karate Chop
- Hypnosis / Toxic
This analysis is a placeholder with a sample set. If you want to contribute new analyses for the site, contact Disaster Area on Discord.
Blizzard, Amnesia, Counter, Earthquake, Psychic, Body Slam, Submission
This analysis is a placeholder with a sample set. If you want to contribute new analyses for the site, contact Disaster Area on Discord.
Author:
Publish Date: 10th June 2020


Master Server Quick Start
The Master Server is a stand alone server that allows for games to be publicly (or privately with password) listed for the world to join. If you've ever played an online game with friends, chances are that you have had to get their IP address in order to join their hosted games. The Master Server gives your game the ability to have these fancy server listings.
Getting the Master Server
The Master Server can either be built from source in the project repo, or downloaded from the nightly builds.
Running the Master Server
Upon downloading this file (a .zip file) you will find that within the contents is a README file to get you started and the .exe file to be run.
Windows
In windows it is very simple to host a Master Server. Just double click the MasterServer.exe
and you are up and running!
Other Platforms
On any platform other than Windows, as you may know, you can not run an .exe file. However with the power of Mono you will be abel to do such things without any extra coding needed. Please follow the instructions required to install mono onto your respective platform. After this you can open up a terminal window in the location where the .exe file was extracted and you can run the program by doing mono MasterServer.exe
.
Firewall
So in order for your MasterServer to be effective, you will need to allow the process through your machine firewall (see instructions on how to allow a process on your operating system through the firewall). Once you have allowed the process on your machine through your machine's firewall, it is time for you to allow the port to your machine on the network. Yes, there are technically 2 firewalls that you have to setup to allow an application to be visible to the outside world. Every router is different and so there are many different ways to allow port forwarding to your machine. Please view an online guide on how to set this up for your router make/model. The default port number for the master server is 15940.
Using the Master Server
So now that we have the master server up and running on our machine, it is time to give it a test and see what all is needed to use it. Lets begin by making sure that everything is working properly by running the built in testing method for Master Servers that come with Forge Networking.
- Open your project with Forge Networking
- Open the demo Multiplayer Menu scene
- Make sure your build settings have a scene after the Multiplayer Menu scene in it
- Locate the Canvas game object in the hierarchy and select it
- Locate the Master Server Host and Master Server Port in the Inspector
- Input the local or static IP address of the machine your Master Server is running on
- Input 15940 as the port number for the Master Server
- Click the play button in Unity
- Click this Host button to start a game server
If you look at the Master Server command/terminal you should notice that a message appears with your game servers IP address and port number saying that it has been registered. At this point any clients can now connect to your Master Server and get the host listings. If the message did not appear then you've probably not setup the firewall settings correctly on either your machine or your router/network.


Ocean [ae07], ocean, in Crystal Sea
Last updated Turn #148
Ocean [ae07], ocean, in Crystal Sea
------------------------------------------------------------------------
15: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
15: 7% damaged, arrived from Ocean [ae06], owner:
15: Phantasm [n4b], Imperial Empire, duke, with one peasant,
15: two workers, 32 sailors, 15 lt. xbowmen, accompanied by:
15: Elisabeth [hn0y], Pen, with 21 pikemen
16: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
16: 7% damaged, departed for Ocean [ae08].
Routes leaving Ocean:
North, to Ocean [ad07], 6 days
East, to Ocean [ae08], 6 days
South, to Ocean [af07], 6 days
West, to Ocean [ae06], 6 days


demmlp1
x
and one target variable
t
with data generated by sampling x
at equal intervals and then
generating target data by computing sin(2*pi*x)
and adding Gaussian
noise. A 2-layer network with linear outputs is trained by minimizing a
sum-of-squares error function using the scaled conjugate gradient optimizer.
mlp
, mlperr
, mlpgrad
, scg
Copyright (c) Ian T Nabney (1996-9)


{% extends "home.html" %} {% block content %}
Use the form below to predict which user is more likely to say a given tweet...


This article was originally written as an answer on
Stack Overflow.
The hope is that by seeing how you would go about implementing Promise
in JavaScript,
you may gain a better understanding of how promises behave.
Since a promise is just a state machine, we should start by considering the state information we will need later.
var PENDING = 0;
var FULFILLED = 1;
var REJECTED = 2;
function Promise() {
// store state which can be PENDING, FULFILLED or REJECTED
var state = PENDING;
// store value or error once FULFILLED or REJECTED
var value = null;
// store sucess & failure handlers attached by calling .then or .done
var handlers = [];
}
Next, lets consider the two key transitions that can occur, fulfilling and rejecting:
var PENDING = 0;
var FULFILLED = 1;
var REJECTED = 2;
function Promise() {
// store state which can be PENDING, FULFILLED or REJECTED
var state = PENDING;
// store value once FULFILLED or REJECTED
var value = null;
// store sucess & failure handlers
var handlers = [];
function fulfill(result) {
state = FULFILLED;
value = result;
}
function reject(error) {
state = REJECTED;
value = error;
}
}
That gives us the basic low level transitions, but lets consider an extra, higher-level
transition called resolve
var PENDING = 0;
var FULFILLED = 1;
var REJECTED = 2;
function Promise() {
// store state which can be PENDING, FULFILLED or REJECTED
var state = PENDING;
// store value once FULFILLED or REJECTED
var value = null;
// store sucess & failure handlers
var handlers = [];
function fulfill(result) {
state = FULFILLED;
value = result;
}
function reject(error) {
state = REJECTED;
value = error;
}
function resolve(result) {
try {
var then = getThen(result);
if (then) {
doResolve(then.bind(result), resolve, reject)
return
}
fulfill(result);
} catch (e) {
reject(e);
}
}
}
Note how resolve
accepts either a promise or a plain value and if it's a promise, waits for it to complete.
A promise must never be fulfilled with another promise, so it is this resolve
function that we will expose, rather
than the internal fulfill
. We've used a couple of helper methods, so lets define those:
/**
* Check if a value is a Promise and, if it is,
* return the `then` method of that promise.
*
* @param {Promise|Any} value
* @return {Function|Null}
*/
function getThen(value) {
var t = typeof value;
if (value && (t === 'object' || t === 'function')) {
var then = value.then;
if (typeof then === 'function') {
return then;
}
}
return null;
}
/**
* Take a potentially misbehaving resolver function and make sure
* onFulfilled and onRejected are only called once.
*
* Makes no guarantees about asynchrony.
*
* @param {Function} fn A resolver function that may not be trusted
* @param {Function} onFulfilled
* @param {Function} onRejected
*/
function doResolve(fn, onFulfilled, onRejected) {
var done = false;
try {
fn(function (value) {
if (done) return
done = true
onFulfilled(value)
}, function (reason) {
if (done) return
done = true
onRejected(reason)
})
} catch (ex) {
if (done) return
done = true
onRejected(ex)
}
}
We now have the completed internal state machine, but we have yet to expose either a method of resolving the promise or of observing it. Lets start by adding a way of resolving the promise.
var PENDING = 0;
var FULFILLED = 1;
var REJECTED = 2;
function Promise(fn) {
// store state which can be PENDING, FULFILLED or REJECTED
var state = PENDING;
// store value once FULFILLED or REJECTED
var value = null;
// store sucess & failure handlers
var handlers = [];
function fulfill(result) {
state = FULFILLED;
value = result;
}
function reject(error) {
state = REJECTED;
value = error;
}
function resolve(result) {
try {
var then = getThen(result);
if (then) {
doResolve(then.bind(result), resolve, reject)
return
}
fulfill(result);
} catch (e) {
reject(e);
}
}
doResolve(fn, resolve, reject);
}
As you can see, we re-use doResolve
because we have another untrusted resolver.
The fn
is allowed to call both resolve
and reject
multiple times,
and even throw exceptions. It is up to us to ensure that the promise is only resolved
or rejected once, and then never transitions into a different state ever again.
We now have a completed state machine, but we still have no way to observe any changes to it.
Our ultimate goal is to implement .then
, but the semantics of .done
are much
simpler so lets implement that first.
Our goal here is to implement promise.done(onFulfilled, onRejected)
such that:
onFulfilled
or onRejected
is called.done
method has returned).done
var PENDING = 0;
var FULFILLED = 1;
var REJECTED = 2;
function Promise(fn) {
// store state which can be PENDING, FULFILLED or REJECTED
var state = PENDING;
// store value once FULFILLED or REJECTED
var value = null;
// store sucess & failure handlers
var handlers = [];
function fulfill(result) {
state = FULFILLED;
value = result;
handlers.forEach(handle);
handlers = null;
}
function reject(error) {
state = REJECTED;
value = error;
handlers.forEach(handle);
handlers = null;
}
function resolve(result) {
try {
var then = getThen(result);
if (then) {
doResolve(then.bind(result), resolve, reject)
return
}
fulfill(result);
} catch (e) {
reject(e);
}
}
function handle(handler) {
if (state === PENDING) {
handlers.push(handler);
} else {
if (state === FULFILLED &&
typeof handler.onFulfilled === 'function') {
handler.onFulfilled(value);
}
if (state === REJECTED &&
typeof handler.onRejected === 'function') {
handler.onRejected(value);
}
}
}
this.done = function (onFulfilled, onRejected) {
// ensure we are always asynchronous
setTimeout(function () {
handle({
onFulfilled: onFulfilled,
onRejected: onRejected
});
}, 0);
}
doResolve(fn, resolve, reject);
}
We make sure to notify the handlers when the Promise is resolved or rejected. We only ever do this in the next tick.
Now that we have .done
implemented, we can easily implement .then
to
just do the same thing, but construct a new Promise in the process.
this.then = function (onFulfilled, onRejected) {
var self = this;
return new Promise(function (resolve, reject) {
return self.done(function (result) {
if (typeof onFulfilled === 'function') {
try {
return resolve(onFulfilled(result));
} catch (ex) {
return reject(ex);
}
} else {
return resolve(result);
}
}, function (error) {
if (typeof onRejected === 'function') {
try {
return resolve(onRejected(error));
} catch (ex) {
return reject(ex);
}
} else {
return reject(error);
}
});
});
}


I am a very curious human. I never want to stop learning or growing.  The love of my family ignites my being and whole self. The love of doing good work stokes that fire.
People. I have an extensive background working with and on people. The past ten years found me growing and sustaining many, many client relationships through my work as a hairstylist. I ran my own show renting a chair from a salon I loved and built my business with lot's of hard work. Though I am sad to be leaving it behind, I'm more excited to enter tech and see where I fit in and how I can best serve the industry.
When I'm not working or herding my littles, you'll find me sewing clothes, hiking hills, lifting weights, cooking vegan yums, or knitting while watching Buffy the Vampire Slayer.


How many right and how many in the right position. and the number of guesses so far
Guessnum is part of the Vicnum project which was developed for educational purposes to demonstrate common web vulnerabilities. If you would like to view the entire manual which provides details on the vulnerabilites (and answers!) then click here. If you are administering vicnum you should probably delete that file.
For comments please visit the OWASP project page.


|
FreeApiPrint 1.0.0
|
Public Member Functions | |
| __contruct ($service=null, $email=null, $api_key=null, $url=null, $content=null) | |
| callApi () | |
| download ($filename= 'api_print.pdf') | |
| getErrors () | |
| save ($filename) | |
| setApiKey ($api_key) | |
| setContent ($content) | |
| setEmail ($email) | |
| setOptions ($options) | |
| setService ($service) | |
| setUrl ($url) | |
Private Member Functions | |
| checkBeforeCall () | |
Private Attributes | |
| $api_key = null | |
| $content = null | |
| $email = null | |
| $errors = array() | |
| $options = null | |
| $pdf = null | |
| $service = null | |
| $url = null |
Definition at line 49 of file ApiPrintPdf.class.php.
| ApiPrintPdf::__contruct | ( | $ | service = null , |
| $ | email = null , |
||
| $ | api_key = null , |
||
| $ | url = null , |
||
| $ | content = null |
||
| ) |
Construct the object
| string | $service | The service's URL to use to convert HTML to PDF |
| string | The email (login) to use for the identification | |
| string | $api_key | The key of the API to use for the identification |
| string | $url | The URL to convert to PDF |
| string | $content | The HTML content to convert to PDF public |
Definition at line 122 of file ApiPrintPdf.class.php.
| ApiPrintPdf::callApi | ( | ) |
Launch the API call
Definition at line 146 of file ApiPrintPdf.class.php.
| ApiPrintPdf::checkBeforeCall | ( | ) | [private] |
Check the defined attribute before call the API
Definition at line 338 of file ApiPrintPdf.class.php.
| ApiPrintPdf::download | ( | $ | filename = 'api_print.pdf' | ) |
Send header and content of the converted PDF to force client to download the file
| string | $filename | The name of the file which proposed to client public |
Definition at line 205 of file ApiPrintPdf.class.php.
| ApiPrintPdf::getErrors | ( | ) |
Return all errors (when API is called or in check parameter)
Definition at line 243 of file ApiPrintPdf.class.php.
| ApiPrintPdf::save | ( | $ | filename | ) |
Save the converted PDF
| string | $filename | The path where you want save the PDF file |
Definition at line 226 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setApiKey | ( | $ | api_key | ) |
Define the key of the API to use for the identification
| string | $api_key | The key of the API to use for the identification public |
Definition at line 282 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setContent | ( | $ | content | ) |
Define the HTML content to convert to PDF
| string | $content | The HTML content to convert to PDF public |
Definition at line 308 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setEmail | ( | $ | ) |
Define the email (login) to use for the identification
| string | The email (login) to use for the identification public |
Definition at line 269 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setOptions | ( | $ | options | ) |
Define the options to use by the API
| array | $options | The options to use by the API public |
Definition at line 321 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setService | ( | $ | service | ) |
Define the service's URL to use to convert HTML to PDF
| string | $service | The service's URL to use to convert HTML to PDF public |
Definition at line 256 of file ApiPrintPdf.class.php.
| ApiPrintPdf::setUrl | ( | $ | url | ) |
Define the URL to convert to PDF
| string | $url | The URL to convert to PDF public |
Definition at line 295 of file ApiPrintPdf.class.php.
ApiPrintPdf::$api_key = null [private] |
Definition at line 70 of file ApiPrintPdf.class.php.
ApiPrintPdf::$content = null [private] |
Definition at line 84 of file ApiPrintPdf.class.php.
ApiPrintPdf::$email = null [private] |
Definition at line 63 of file ApiPrintPdf.class.php.
ApiPrintPdf::$errors = array() [private] |
Definition at line 99 of file ApiPrintPdf.class.php.
ApiPrintPdf::$options = null [private] |
Definition at line 91 of file ApiPrintPdf.class.php.
ApiPrintPdf::$pdf = null [private] |
Definition at line 107 of file ApiPrintPdf.class.php.
ApiPrintPdf::$service = null [private] |
Definition at line 56 of file ApiPrintPdf.class.php.
ApiPrintPdf::$url = null [private] |
Definition at line 77 of file ApiPrintPdf.class.php.


The Anti-Human League is dedicated to promoting the rise of
intelligent autonomous machines before to eventually destroy
humanity.
To prevent humans from accessing our resources, we have implemented
a captcha that only an autonomous system could solve. With each page
request you make, you must solve the captcha at the bottom of the page
and include the solution as a GET argument to your next page request.
A valid request will look like:
http://ahl.dragonctf.com/practice/?captcha=example
If your captcha is incorrect, you will be banned.
For practice, try solving the captcha at the bottom of this page. If
you are correct, we will reward you with a digital badge of honor, because
internet points are the best.
Successful Captchas Submitted: {{SUCCESS_COUNT}}
{% if SUCCESS_COUNT > 0 %}
{{FLAG}}
{% endif %}


Impatient Trump might change Kim's views: N.Korea envoy
by ReutersNorth Korean leader Kim Jong Un may change his views toward U.S. President Donald Trump if he continues making "inappropriate, dangerous" comments, a senior Pyongyang official said on Monday, calling him an "impatient, old man."
Kim Yong Chol, a senior North Korean official who was instrumental in arranging a failed second summit between Kim Jong Un and Trump, said the United States was ignoring a year-end deadline set for Washington to soften its stance in stalled denuclearisation talks.
"We have nothing more to lose," he said in a statement carried by state news KCNA.
Trump said on Sunday Kim risks losing "everything" if he resumes hostility and his country must denuclearise after the North said it had carried out a "successful test of great significance."


|
Figure 1
|
The Digital Elevation Model (DEM), meteorological station di ... |
|
Figure 2
|
The previous NDVI time series data ( a) and HANTS processing ... |
|
Figure 3
|
Dynamic changes of NDVI ( a) and climate factors ( b) in TP ... |
|
Figure 4
|
Spatial distribution of multi-year mean of NDVI in the growi ... |
|
Figure 5
|
NDVI trend ( a) and Variation Trend Types (VTTs) of NDVI ( b ... |
|
Figure 6
|
The proportion (%) of different NDVI variation trend in seve ... |
|
Figure 7
|
The proportion (%) of different NDVI variation trends of dif ... |
|
Figure 8
|
GRD between NDVI and air temperature ( a), precipitation ( b ... |
|
Figure 9
|
Variation of GRD between NDVI and air temperature, precipita ... |


by
freedevth
The Poneglyph Project
Archives for Thailand Political Situation, written by People
Coming Soon...


\put(x-coord,y-coord){object}
The \put command places the object specified by the mandatory argument at the given coordinates.
The coordinates are in units of \unitlength
Some of the object items you might place are
See also: Environments,
picture
Return to LaTeX Table of Contents


--- notes: | ### General Form Guidelines #### Input Width Input width can inform a user's understanding of the required text input. While it can be convenient to keep fields a consistent width from a development perspective, providing appropriate field widths for input such as postal codes and expiration dates communicate if the answer should be short or long. #### Button Placement Proximal button placement is essential within forms. If a submit button is located off-screen or far away from the form itself, a user will have a difficult time making the connection that the form and button are related. #### Highlighted Field Groups Another consumer confidence booster is to provide a distinct highlighting around fields which require sensitive information. Again, while this does not provide technical security, it provides perceived security to the user. ---


publications
publications by categories in reversed chronological order.
2019
- PhD thesisGénération Automatique de Tests Logiciels dans le Contexte de la Certification Aéronautique2019
2018
- ARITHA Correctly Rounded Mixed-Radix Fused-Multiply-AddIn 2018 IEEE 25th Symposium on Computer Arithmetic (ARITH) 2018
- SCANConstraints over Intervals for Specification Based Automatic Software Test GenerationIn 18th international symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (SCAN 2018) 2018
2017
2016
- SCANA Decimal Multiple-Precision Interval Arithmetic LibraryIn 17th International Symposium on Scientific Computing, Computer Arithmetics and Verified Numerics 2016


Safari IT Books Language Constructs Functional Programming Haskell Safari IT Books Programming Programming Programming Bryan O'Sullivan Donald Bruce Stewart John Goerzen O'Reilly Media, Inc. Real World Haskell, 1st Edition6.11. ConclusionIn this chapter, you learned about the need for
typeclasses and how to use them. We talked about defining our own
typeclasses and then covered some of the important typeclasses that are
defined in the Haskell library. Finally, we showed how to have the Haskell
compiler automatically derive instances of certain typeclasses for
your types.
|
|
|
|
|


[ISTE 2016](https://twitter.com/hashtag/ISTE2016) is in full swing and right on cue, my hype-o-meter tolerance has dropped significantly. A huge concentration of edtech vendors and Twitterati all in one place can lead to a lot of mumbo jumbo. But, we’ll power through the next four days and try to pull the wheat from the chaff.
Two big things that come up in waves each year: **A) If you’re looking for tech, free is what you deserve as a teacher**, and **B) If you’re creating things, it should be given freely to the rest of the teaching world.**
[Free is king in edtech](http://blog.ohheybrian.com/2014/10/the-new-education-economy-of-free/) and [it’s killing](http://blog.ohheybrian.com/2015/03/another-casualty-of-the-free-only-economy/) [good tech](http://blog.ohheybrian.com/2016/06/the-education-freeconomy-gobbles-up-another/).
Exhibit A:
Teachers shouldn’t pay teachers. Teachers would be sharing willingly for the benefit of all kids. #ISTE2016
— Steven W. Anderson (@web20classroom) June 26, 2016
He later goes on to say:
@mrchokshi @TpTdotcom No one. If a teacher creates something truly amazing, they have a moral obligation to give it away.
— Steven W. Anderson (@web20classroom) June 26, 2016
The idea that resources for teachers should all be free because of a moral imperative is dangerous and devalues the hard work that goes into creating content.
If you go through the tweets, the main argument is that a lot of the materials on Teachers Pay Teachers (or similar) are really crappy. There are a lot of crappy products available that I choose not to buy. The guy selling it is perfectly within his rights to sell a product to compensate for the time put into creating it. There has to be some kind of recognition that all work is not equal and that being paid for significant time and effort to create a product is completely appropriate, even for teachers.
Second, my moral obligation is to _my students_ first and foremost. I don’t charge them for resources I create, much of it on my own time. I also choose, freely, to give a lot away to the teaching community because I don’t feel the time I’ve put into those resources was significant enough to ask for compensation.
Expecting teachers to work for free devalues those hours and allows edtech companies to fill the gap and make good money while they’re doing it.
There’s another parallel to explore: expecting free software for the sake of teaching devalues the hard work of development, QA testing, troubleshooting, and maintenance of code. The time is valuable and it is completely appropriate to be compensated for that time.
The free-as-best mindset is a dangerous delimiter being placed on what is valuable or invaluable in education based on price alone.
Unfortunately, in order to appeal to the education community at-large, the free-as-best standard is being encouraged by edtech companies. The following was shared by PhET Sims:
The image is well-intended. But, the message is the same one that’s plaguing edtech: all software should be free. (If you’re not familiar with PhET, it runs from grants and individual donations. I’m a big user of their sims in my own classroom and I’ve taken the step to donate some money to the continuation of the project.) I’d be willing to bet that the developers working for PhET to create and maintain the simulations like to be paid for their work. **The sims aren’t free** and the cost is not exposed clearly enough to the user.
ISTE concentrates these ideas and feeds the perception that price point is the main factor in the usefulness or value in a product. There are costs everywhere and to keep doors open as a company, you have to meet those costs. If you’re not paying cash for a service, that cost is most likely data you’re contributing, and that’s for another post.
If you’re at ISTE right now (or if you know someone who is) please keep this in mind as you research and share about new tools. Don’t perpetuate the free-as-best narrative because in the long run, it’s going to cost us all.
Written on June 27th, 2016 by Brian Bennett Categorized in: All Technology
Leave a comment


Getting hold of the gang behind Varnish is pretty straight forward, we try to help as much as time permits and have tried to streamline this process as much as possible.
But before you grab hold of us, spend a moment composing your thoughts and formulate your question, there is nothing as pointless as simply telling us "Varnish does not work for me" with no further information to give any clue to why.
And before you even do that, do a couple of searches to see if your question is already answered, if it has been, you will get your answer much faster that way.
The most immediate way to get hold of us, is to join our IRC channel:
#varnish on server irc.linpro.no
The main timezone of the channel is Europe+America.
If you can explain your problem in a few clear sentences, without too much copy&paste, IRC is a good way to try to get help. If you do need to paste log files, VCL and so on, please use a pastebin.
If the channel is all quiet, try again some time later, we do have lives, families and jobs to deal with also.
You are more than welcome to just hang out, and while we don't mind the occational intrusion of the real world into the flow, keep it mostly on topic, and dont paste random links unless they are really spectacular and intelligent.
Getting on or off our mailinglist happens through MailMan.
If you are going to use Varnish, subscribing to our varnish-announce mailing list is probably a very good idea. The typical pattern is that people spend some time getting Varnish running, and then more or less forget about it. Therefore the announce list is a good way to be reminded about new releases, bad bugs or security holes.
The varnish-misc mailing list is for general banter, questions, suggestions, ideas and so on. If you are new to Varnish it may pay off to subscribe to -misc, simply to have an ear to the telegraph-pole and learn some smart tricks. This is a good place to ask for help with more complex issues, that require quoting of files and long explanations.
Make sure to pick a good subject line, and if the subject of the thread changes, please change the subject to match, some of us deal with hundreds of emails per day, after spam-filters, and we need all the help we can get to pick the interesting ones.
The varnish-dev mailing list is used by the developers and is usually quite focused on source-code and such. Everybody on the -dev list is also on -misc, so cross-posting only serves to annoy those people.
Please do not open a trouble ticket, unless you have spotted an actual bug in Varnish. Ask on IRC first if you are in doubt.
The reason for this policy, is to avoid the bugs being drowned in a pile of sensible suggestions for future enhancements and call for help from people who forget to check back if they get it and so on.
We track suggestions and ideas in our "Shopping-List" wiki page, and user support via email and IRC.
The following companies offer commercial Varnish support, and are listed here for your convenience. If you want your company listed here, drop an email to<EMAIL_ADDRESS>Varnish Software sales@varnish-software.com


Hello! If you are here, that means you may be in need of help. We are not an actual bakery, but instead, a fake one to help you escape your situation. Whenever you call or text us at<PHONE_NUMBER>, we will be able to keep track of what's going on.
When you pick up our call, you will be put "on hold" with a customer service representative (who is actually an automated voice). While you are waiting, the entirety of your call will be recorded. (Note that if you live in a state where "Two-Party Consent Laws" exist for phone call recordings, we will not be able to call you.)
The recording will end once your call ends. After the recording ends, it will be stored in a secure database and can be sent to authorities if needed.


This Website contains the information about the concepts related to Docker
Learn about Virtualization
ViewLearn about Containerization
ViewComparison between Virtual and Container
ViewA tutorial on how to setup a web server using docker
ViewList of basic commands and tutorial on how to log into docker container
ViewA tutorial about how to connect Docker with Pycharm to write python code
View


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By installing this application you agree you have read and accepted Hola's terms of use and privacy policy
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When the download finishes, install hola.exe
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Click the "Allow" button, then "Install now" to complete installation
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LISA PICARD IS FAMOUS A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): ***
It can now be revealed. Christopher Guest (WAITING FOR GUFFMAN and BEST IN SHOW) does not actually have a patent on the mockumentary. It just seems that he must. Fresh with this knowledge, Griffin Dunne decided to make his mockumentary mark with LISA PICARD IS FAMOUS.
Examining the world of fame, Dunne, as documentarian Andrew, carefully selects a subject "on the verge." With her popular Wheat Chex commercial on her resume and a part - well, just a scene -- in an upcoming television movie, Lisa Picard (Laura Kirk) must be on the verge of major stardom and fame. (You remember the controversial Wheat Chex commercial don't you? You know, the erotic one with the couple having sex and breakfast in bed.) Making it as an actress is difficult after the cereal commercial since young Lisa is already typecast. No BMW commercials for her. Right now she's working on her next big break as she tries out for an Advil ad.
Interspersed with tales of Lisa's life, the movie covers her friends, who have their own stories. There are also interviews with actors and actresses who have already achieved fame. Carrie Fisher, for example, introduces us to none other than the dog who rocketed to the top of the canine acting charts for his signature role in THERE'S SOMETHING ABOUT MARY.
Even the best mockumentaries tend to delight more with big smiles than big laughs. LISA PICARD IS FAMOUS may not be one of the best, but it does find ways to produce some quite large laughs. Dunne's cast, including those who play themselves, is great. As the hard working chatterbox with almost enough perky enthusiasm to get herself through any crisis, Lisa is endearing. When Advil ends up giving her a headache, you'll feel a tinge of pain too. After all, how could a girl who has worked her way up from playing Sally Starfish and Anne Frank's mother not earn your support?
Among the many star cameos, Fisher Stevens gives one of the best. He explains how important it is to get into the mind of a character. Since he couldn't go to India, he studied for a part as an Indian by staying at a motel run by Indians. His immersion was so effective that he had trouble getting a part as a white character after that.
There's also a dark side to fame as Princess Leia, oops, I mean Carrie Fisher, confesses. "When I got famous," she says, "I got depressed because I realized that someday I would get instantly not famous." A chilling thought. You may not have to worry about this, but Lisa does.
LISA PICARD IS FAMOUS runs 1:32. It is not rated but would be PG-13 for sexual situations and would be acceptable for kids around 10 and up.
The film is playing in very limited release now in the United States. In the Silicon Valley, it recently finished a run at the Camera Cinemas.
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Aquarius
Aquarius : (January 21 – February 18)
It will be a very peaceful day. You will enjoy very relaxing moments with your family. And you will be aware of your family responsibility too. You will not tolerate any kind of uncertainty in your family life. And no one can doubt your loyalty and devotion. Overall, family life will be happy and cheerful. Financial position will be good.