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The PortfolioTarget class accepts two parameters for its constructor: Symbol and Quantity. This is consumed by the execution model, which seeks to reach this target as efficiently as possible; you should not assume orders are filled immediately.
// Create a new portfolio target for 1200 IBM shares. var target = new PortfolioTarget("IBM", 1200);
# Create a new portfolio target for 1200 IBM shares. target = PortfolioTarget("IBM", 1200)
Margin accounts can also use the $[Percent(algorithm, Symbol, percent),M:QuantConnect.Algorithm.Framework.Portfolio.PortfolioTarget.Percent] helper method. This calculates a quantity equivalent to a percentage of portfolio value.
// Calculate target equivalent to 10% of portfolio value var target = PortfolioTarget.Percent(algorithm, "IBM", 0.1);
# Calculate target equivalent to 10% of portfolio value target = PortfolioTarget.Percent(algorithm, "IBM", 0.1)
Your Portfolio Construction Model should return a targets array from your $[CreateTargets,M:QuantConnect.Algorithm.Framework.Portfolio.IPortfolioConstructionModel.CreateTargets] method:
// Return an array of targets return new PortfolioTarget[] { new PortfolioTarget("IBM", 1200) };
# Return an array of targets return [ PortfolioTarget("IBM", 1200) ]


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@@include('../partials/components/headerMenu.html')
Select up to 2 retail pharmacies. Mail order will be automatically included.
#
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1234 Main St
Cincinnati, OH 45202", "distance": "Distance: ", "distValue": "0.95 Mile(s)" }, { "ctrlNumber": "2", "details": "CVS Pharmacy #12345
1234 Main St
Cincinnati, OH 45202", "distance": "Distance: ", "distValue": "0.95 Mile(s)" } ])


Toronto has a semi-continental climate, with a warm, humid summer and a cold winter.
Toronto’s climate is modified by its location on the shores of Lake Ontario. The water in the lake ensures Toronto is warmer in winter and cooler in summer than it would otherwise be. The Great Lakes location is also the source of Toronto’s summer humidity, which many people find uncomfortable.
Although Toronto is one of Canada’s warmer cities in winter, winters are still severe, with snow on the ground most days between mid-December and mid-March. Snow deeper than 1 cm is seen on 65 days a year on average.
Ottawa, which is farther north than Toronto, and does not sit lakeside, has snow depths of greater than 1 cm on about 120 days each year.
Many factories and industrial plants in both Canada and the USA are located on the Great Lakes and smog has become something of a problem in Southern Ontario. About half of the smog is wind-borne from the USA.
According to Forbes, powerful cities such as Toronto attract investment due to the size of their economy, their projected future prosperity, cost of living and quality of life. Forbes describes Toronto as lying at the economic heart of one of the world’s wealthiest countries, projected to keep humming through 2020.
The other top ten most economically powerful cities identified by Forbes were London, Hong Kong, New York, Tokyo, Chicago, Seoul, Paris, Los Angeles, and Shanghai.
Toronto is Canada’s banking/financial capital and the home of its principle stock exchange, the Toronto Stock Exchange.
Toronto has North America’s third largest concentration of private IT companies, bettered only by San Francisco and New York.
In addition to IT and high finance Ontario’s plentiful natural resources, such as hydroelectricity and raw materials, have helped Toronto and its surrounding municipalities to become major industrial centres, producing more than 50 percent of Canada’s manufactured products.
From the perspective of today’s more troubled economic times, Canada’s banks never extended themselves as unwisely as some of their American and European counterparts and have relatively strong balance sheets.
Toronto currently has a construction boom dominated by condo developments and office buildings. Both the Conference Board of Canada and Oxford Economics predict that Toronto’s economy will grow healthily in 2015 and 2016, by about 3% annually.
Figures published by Statistics Canada show that over 150,000 net full-time jobs were created in the Toronto region in the 12 months to August 2015. As a result of these new jobs, the unemployment rate in the Toronto region is 7.5% at the end of August 2015.
Toronto is one of the very few North American cities in which people can choose not to own a car without suffering major inconveniences. This is largely due to The Toronto Transit Commission (TTC) – North America’s third largest public transit system.
The TTC’s far-reaching and reliable network of buses and streetcars are interconnected by an extensive underground subway system. The buses are newly manufactured hybrid-electrics, introduced to reduce air pollution.
The transportation corridor image on the left shows a choice of transportation methods leading to and from downtown Toronto – road, bike-path, railway, and the Don River.
Toronto also has Zipcar, a membership car-sharing service that is billable by the hour. This service is most frequently used in the downtown.
------


Pradeepa K
Software Engineer
B.E. Computer Science Engineering(Full-time)
M.B.A. Systems Management(Part-time)
Location
Bangalore, Karnataka, India
Specialist in
UI Design & Development ◾JavaScript ◾Full Stack (Node.js) ◾Front End Developer Software Engineer Web Apps Building & Deployment
I Love to
Write Tech Blogs in Weekend
Watch Movies with Friends/Parents
Exploring spiritual places
Languages
English
German(A1)


Immersions
Visualizing and sonifying how an artificial ear hears music
This project lets us interact with and explore an audio processing neural network, or what I call an “artificial ear”. This network was trained in an unsupervised way on various music datasets using a contrastive predictive coding method. There are two modes of showing its inner workings - one is visual, the other is sonic. For the visualization, first, the neurons of the network are laid out in two-dimensional space, then their activation is shown at every moment, depending on the input, in real-time. To make audible how music sound to the artificial ear, an optimization procedure generates sounds that specifically activate certain neurons in the network. In case you’re interested, please see the the paper or the poster, as well as the visualization code (that you could use to visualize your own networks)!
If you want to skip the theory, you can jump straight down to the audio and video demonstrations starting in the visualization section.
Introduction
Although neural networks are often described as black boxes, there exist methods to make us see and hear what is going on inside a neural network. A well-known example of this is the DeepDream technique that generates bizarre psychedelic but strangely familiar pictures. A related approach can be used to make audible the innards of sound processing neural nets. The first thing we need is a model we want to examine. It is trained on musical data using a self-supervised training algorithm. Then the basic idea is the following: The starting point is an arbitrary sound clip (e.g. silence, noise, or a drum loop). This clip will be modified by the optimization procedure in a way that stimulates a certain region in the neural net. The changed clip can then be used as a basis for a new optimization. This way you obtain sounds that are generated by the neural network in a freely associative way. One of the most important properties of neural networks is that they process information on different levels of abstraction. An activation in some region of the net thus may, for example, be associated with short and simple sounds or noises, a different region with a more complex musical phenomenon, such as rhythm, phrase or key. For what exactly a neural networks listens depends on the data it was trained on, as well as on the task it is specialized to accomplish. This means to different nets music will sound differently. All this can be directly experienced with the Immersions project. For this, I developed a setup that allows - not unlike a DJ mixing console - the generation, visualization and control of sound in real-time. The contributions presented here are the following:
- The application and evaluation of Contrastive Predictive Coding for self-supervised representation learning on spectral representations of music. For this, new fully convolutional architectures for both encoder and autoregressive model are used and evaluated.
- A method of finding a clear visual representation of a neural network using the multilevel force graph layout technique and visualizing activations of all neurons in real-time.
- Transfer of the principles of feature visualization to the sound domain, allowing the sonification of features a model relies on.
- Development of a system that allows the real-time control and exploration of the sonic feature space.
Contrastive Predictive Coding
In the centre of this project stands an “artificial ear”, meaning a sound processing neural network. It has been shown that analogies exist between convolutional neural networks (ConvNets) and the human auditory cortex (see, for example, this paper). Of course, the human auditory system is not a classification network, we usually don’t learn with the help of explicit labels. The question of the actual learning mechanisms in the brain is highly contested, but a promising candidate, especially for perceptual learning, might be predictive coding. Here future neural responses to stimuli are predicted and then compared to the actually occurred responses. The learning entails reducing the discrepancy between prediction and reality.
One method of using the idea of predictive coding for self-supervised learning in artificial neural networks is presented in Representation Learning with Contrastive Predictive Coding. For this so-called contrastive predictive coding you need two models:
The encoder model builds a compressed encoding $\mathbf{z}\sb{t}$ of the signal for each time step $t$ and the autoregressive model summarizes multiple sequential encodings into a vector $\mathbf{c}$. The encoding $k$ time steps in the future, $\mathbf{z}_{k}$, is predicted by a different learnt linear transformation $\mathbf{M}_k$ of $\mathbf{c}$ for each time step. The time steps $k$ are counted from the latest point $t$ for which the autoregressive model had access to the encoding $\mathbf{z}_t$. The loss for each mini-batch is calculated as follows:
Both $n$ and $m$ are indices of the mini-batch dimension. The numerator is a scalar score that measures how well the prediction matches the correct encoding. It is divided by the sum of the scores that measure the prediction against all encodings of the current mini-batch. Minimizing $\mathcal{L}\sb{CPC}$ maximizes the mutual information between $\mathbf{c}$ and future encodings $\mathbf{z}_{k}$. This means that there is little incentive to encode high-frequency noise since this information is not shared over longer time scales. Instead, the model can learn to focus on “slow” features that are useful for differentiating those signals.
Using contrastive predictive coding frees us to choose out training data without many constraints. No expensively labelled datasets are required, the model learns to make sense of any inputs it is given.
Model Architecture
The encoder network receives as input a scaleogram representation of an audio clip. A scaleogram is the result of a constant-Q or a wavelet transformation of the audio signal and comes quite close to the representation that the cochlea passes on to the brain. For the purpose of input optimization, which will be described below, the transformation from a raw audio signal to the scaleogram has to be differentiable. Fortunately, it can simply be implemented as a 1D convolutional layer with fixed weights in any standard deep learning framework. For each frequency band, two channels of the kernels are assigned to represent the real and imaginary part of the corresponding finite impulse response filter. The stride parameter is equivalent to the hop length of the transformation. The output of this layer can be reshaped to have the dimensions time, frequency, and real/imaginary. This representation is then converted into polar coordinates. The amplitude is squared and logarithmically scaled. This results in the two-dimensional scaleogram representation, which is the input to the encoder network. Additionally, the phase part is used as the second channel for the input, although I did not find that this improves the performance noticeably.
As encoder serves a 2D-ConvNet, similar to the kind common in computer vision and image processing. It is based on the ResNet architecture, with a few modifications: Since the model is trained on a prediction task, we use only causal convolutions. In practice, this simply means padding only at the start of the time dimension for both convolution and pooling layers. Also, in addition to the usual quadratic kernels, we apply kernels that only span the frequency dimension and are supposed to detect overtones and harmonies.
The output of the encoder, only the dimensions time and channel are left. They represent timestep and features of $\mathbf{z}_{k}$. In the original paper, a recurrent neural network is used as the autoregressive model. We use a causal 1D-ConvNet instead, similarly to the encoder network employing ReLU nonlinearities, residual connections, max pooling and batch normalization.
| Block | Channels | Conv 1 | Conv 2 |
|---|---|---|---|
| 0 | 8 | 3x3 pool2 | 1x25 |
| 1 | 16 | 1x3 | 1x3 |
| 2 | 32 | 3x3 | 1x15 |
| 3 | 64 | 1x3 | 1x3 |
| 4 | 128 | 3x3 pool 2 | 1x15 |
| 5 | 256 | 1x3 | 1x3 |
| 6 | 512 | 3x3 | 1x5 |
| 7 | 512 | 1x3 | 1x3 |
| Block | Channels | Conv 1 |
|---|---|---|
| 0 | 512 | 4 |
| 1 | 512 | 4 |
| 2 | 512 | 1 pool 2 |
| 3 | 512 | 4 |
| 4 | 256 | 4 |
| 5 | 256 | 1 pool 2 |
| 6 | 256 | 4 |
| 7 | 256 | 1 |
| 8 | 256 | 4 |
Experiment
For the experiments, the inputs are audio clips with a length of four seconds at a sample rate of 44.1 kHz. The scaleogram has the hop length 1024 and 216 frequency bins (starting at 40 Hz, 24 bins per octave), resulting in the input size 172 x 216. The encoder model has two pooling layers with stride 2, which leads the time steps of the encodings $\mathbf{z}\sb{t}$ to have a length of ca. 93 ms. The autoregressive model integrates 42 consecutive encodings into one representation $\mathbf{c}$. From $\mathbf{c}$, 16 future encodings $\mathbf{z}_{k}$ are predicted, skipping the first 4 immediate next time steps.
I trained the same model on two different datasets: one consisting of about 40 hours of house music mixes, and the other one of 200 hours of classical piano music. To evaluate the performance of the CPC training, the learned representations were used in a test tasks classification setting. For this, the audio data of the test tasks were fed into the trained models and the resulting features $\mathbf{c}$ were saved. Those features were provided with the relevant labels and split into a training and an evaluation set. A linear classifier was fitted with the training set.
The first task was to assign audio clips to the house track it was extracted from (with a total of 15 possible tracks). For the second task the composer of given the audio had to be classified (the samples were also taken from the MAESTRO dataset). As a baseline, the whole architecture was trained directly in a supervised setting from scratch for both of the tasks.
I also trained the models additionally with a slight modification of the CPC algorithm: Instead of contrasting the prediction scores with the other samples from the mini-batch, they are also contrasted with different time steps (score across time steps, SAT):
| Model | house loss | house accuracy | maestro loss | maestro accuracy |
|---|---|---|---|---|
| CPC house | 0.3914 | 90.44 % | 2.212 | 25.36 % |
| CPC house SAT | 0.3583 | 91.81 % | 2.087 | 30.06 % |
| CPC maestro | 0.5131 | 86.83 % | 0.659 | 77.49 % |
| CPC maestro SAT | 0.8789 | 76.62 % | 0.700 | 75.90 % |
| supervised house | 1.563 | 48.25 % | ||
| supervised maestro | 2.744 | 19.70 % |
The performance of the CPC trained models on the test tasks is encouraging, especially when compared to the supervised models. Maybe not surprisingly, the latter show poor performance as there was not enough training data to learn meaningful features for the tasks. The use of SAT loss improves performance only for the model trained on the house dataset. One possible explanation might be that SAT training makes better use of limited data, but can be harmful if enough data is available. It could also depend on the specific nature of the data. For this, further investigation is needed.
Below, for all visualizations and sonifications the CPC maestro model is used.
Visualization
To illustrate what is going on inside the model at each moment, we visualize the activation of the artificial neurons (i.e. the outputs of the nonlinearities). The place of each neuron in our network can be described by four coordinates: layer, frequency, time, and channel. We treat the time dimension different from the other dimensions and exclude it for now. The neurons can be seen as the vertices of a graph. They are connected in different patterns by edges, depending on the type of layer (e.g. fully connected or convolutional) the neurons belong to. There exist methods to lay out graphs into suitable shapes. For our purposes, the force layout technique is the best fit. Here the graph is modelled as a physical system with different forces acting on the vertices. The system, starting from random initial conditions, can be simulated using numerical methods, e.g. Verlet integration.
In our setting, there are three types of forces acting on each vertex. The first force pulls together vertices that are directly connected. The strength is proportional to the distance between vertices and can be scaled with the weight of the connecting edge. Formally, the attractive force $\mathbf{a}$, caused by vertex $j$ and acting on vertex $i$, with their respective positions $\mathbf{p}$, and connected by an edge with weight $w_{ij}$, is:
If there vertices $i$ and $j$ connected, $w_{ij}$ is $0$. In practice, the attractive forces are calculated only for connected vertices.
The second force works as if each vertex had a certain electric charge, which would make vertices repel each other. The force $\mathbf{r}$ caused by vertex $j$ and acting on vertex $i$, and charges $q$, is:
For a physically accurate simulation of an electrostatic force, the exponent $k$ would have to be 2. But seen as an adjustable parameter, it gives us some control over the appearance of the layout: it controls which of the forces, repulsive and attractive, dominates at a given distance between vertices. For high values of $k$, the repulsive force is strong over short and weak over long distances. This leads to the vertices being more evenly spread out. On the other hand, low values of $k$ result in dense clusters of vertices.
The optional centering force, scaled by a global factor $l$, pushes vertices towards the origin:
With that, the combined force $\mathbf{f}$ acting on vertex $i$, can be defined as:
Since the number of individual forces that have to be calculated scales quadratically with the number of vertices, for large networks it is necessary to approximate the forces $\mathbf{r}_{ij}$ in a computationally efficient way using the Barnes-Hut-tree method.
To avoid local energy minima, it is helpful to start the simulation with a low-resolution graph and increasing the resolution in steps until the full graph reached. For our neural networks, the construction of lower resolution levels can be done by iteratively consolidating vertices that are neighbouring in the channel or the frequency dimension. The expansion is done in the inverse order. Also, it can be helpful to slowly increase the centering force factor with each new resolution level.
There are many possibilities on how to determine the weights of the edges and the charges of the vertices. I chose to use uniform weights for the edges and use the variance of the activation of each neuron across the validation set as its charge. For each point in the time dimension, which we excluded above, we get different charge values for the vertices, which means also results in a slightly different layout. Calculating several of these snapshot layouts allows us to construct an animated version of the network. Once the layout is worked out the current state of the net can be depicted by lighting up strongly activated neurons and letting others stay dark.
The code for the GPU accelerated layout calculation (implemented in PyTorch) and the OpenGL-based visualization is available here.
Input Optimization
Today’s neural networks usually are completely differentiable. This means we can generate inputs for a trained model that maximize the activations of certain neurons in the network using iterative gradient-based optimization. These inputs can then be directly experienced by humans and show which particular stimuli the selected neurons respond to. In our case, this procedure sonifies the features activating the selected neurons.
Neural nets, especially if they did not receive adversarial training, are susceptible to small changes in the input. These can lead to local optima that are not perceptible by humans but still have the required properties. To prevent this, several types of regularization are applied: temporal shifting of the input, small pitch changes, masking of random regions in the scaleogram, noisy gradients and de-noising of the input. All these methods make the input optimization more difficult in certain ways and thus enforce more robust and distinct results.
Audio clips that maximize the activation of the selected layer. The right pictures show the scaleogram representations.
Live Performance
Like the inputs, the generated audio clips have a duration of about four seconds. With that, they lend themselves for a loop-based live performance. One loop then corresponds for example to two 4/4 measures at 120 bpm. The optimization procedure described above constantly generates new audio clips. As soon as one clip has finished playing, the latest newly calculated clip is started. An acoustic morphing arises as a result that is also reflected in the visualization (each pattern of activations yields its own distinct sounds). As origin for the optimization silence and noise can be used as well as a pre-built clip that for example dictates a certain rhythm.
A quick journey through some selected activations, starting from silence.
All aspects of the procedure can be adjusted in real-time. For intuitive control, I developed a GUI and made the most important parameters controllable by a MIDI controller. The setup described here is very flexible, other networks trained on different data or with different architectures can easily be employed.
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Aleo
Aleo Checker
Main:
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Home is Where the Steam Is
Review of Hamam (1997, Italian and Turkish with English subtitles; a/k/a Steam: The Turkish Bath)
"Go like the tide and come back like the tide," says a Turk to a visiting Italian in Istanbul. That's the tone of *Hamam*. Life is slow and easy in this crowded, ancient corner of Istanbul (not Constantinople), and presented as a pleasant alternative for the stressed out and skinny Westerners looking to unwind, get real, and live fuller lives.
Whether this is true or not of Turkey, this is the reality of *Hamam*. Francesco (Alessandro Gassman) inherits a building in the Turkish capital from an aunt he's never met. He leaves his shrill wife Marta (Francesca D'Aloja) back in Italy while he goes to wrap up his aunt's affairs. But he sure does take his time when he realizes three things. First, his property included a steam bath, a hamam. Second, he discovers that Zozo (Zozo Toledo), who is pressuring him to sell, is working for a woman who wants to tear down the whole area and build a conference center and office complex.  He figures this out when the son and daughter of his aunt's servants eye him pruriently, and the son (Mehmet Gunsar) wins. Shades of *Boudu Saved from Drowning*...
An interesting device is the narration of his aunt's letters to his mother, which were returned unopened. "One must be happy in life; one must," she tells her sister. They are estranged, but we never find out why. Like a prescient Auntie Mame, she sees ahead to the adult Francesco as someone with "eyes that see desires and strong arms to accomplish them." And that's what happens. Once in the bosom of his aunt's servants' family life, where he becomes one of them, he decides to restore the hamam to its former glory; it faded as the hamam's went out of vogue, probably due to indoor plumbing and increased Western influence. Francesco's restoration efforts are seen with a voiceover of the aunt's letter that details her own restoration of the building years earlier.
Because Italian moviegoers are treated to Intermission, the film had the "part one/part two" markers. This is interesting, as it delineates two distinct movements in the film: Istanbul's seduction of Francesco, and having to face the wife with his new life when she turns up after Francesco stays there a wee bit too long.
 The music is also very stirring, sweeping you up feverishly in what seems like an otherwise quiet lifestyle.
Pushed to gay audiences with a sexy poster of two towel-clad men facing away from the camera, very little sex, much less male pulchritude, is offered to us. Pushed to straight audiences with a photo of the couple, there is very little of them together until the second half.
Like *When the Cat's Away*, Hamam shows the small unsung joys of living in a metropolitan enclave facing "urban renewal" and changes in a way of life that doesn't need changing. And despite an appalling human rights record, life in *Hamam*'s Turkey looks mighty appealing.
Directed by Ferzan Ozpetek and original music by Aldo De Scalzi and Pivio.
More movie reviews by Seth Bookey, with graphics, can be found at http://www.geocities.com/Athens/2679/kino.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


This example demonstrate, how PassBy[ME] works with the passbyme2fa-client PHP application.
First of all read the Get Started and configuration sections from the project GitHub page. Create your own config.php file under "conf" folder by making a copy of the config.dist.php file and set your own configuration.
If you use PassBy[ME] the first time you need a registered user with an enrolled device to send messages to. Users can be created in two ways:
Log in to your registered account (organization).
Click on the icon under "Administrators" menu.
Add your users PassBy[ME] Id and press the "OK" button.
At the end of this process will you have a registered user in your PassBy[ME] system with the same enrolled device as your administrator.
Log in to your registered account (organization).
Create a new user under "PassBy[ME] Users" menu by clicking on the "New user" button.
Add a new device to your user by clicking on the icon.
Click on "Add device enrollment" then download the enrollment sheet by clicking on the icon.
Follow the instructions written on the enrollment sheet.
At the end of these steps you will have a registered user with an enrolled device.
Fill all the form elements below.


Description: PlayPosit (previously known as EduCanon) is a web-based interactive video and assessment tool. The platform enables teachers to add interactive elements - for example, questions and pauses - to instructional videos from popular hosting platforms such as YouTube and Vimeo. Students may rewind to review any parts of the video and rely on controls to submit answers to questions. Teachers review these answers in the context of an online dashboard.
View TechnologyDescription: Virtual manipulatives are digital "objects" that resemble physical objects. You can manipulate them, often with a mouse, in the same ways you would real objects. Many virtual manipulatives typically used in math education are available for free online. Once conceptual understanding is brought about using concrete manipulatives, the subsequent use of virtual manipulatives seems to facilitate bridging to the abstract.
View TechnologyDescription: Kahoot is a game based learning platform. Create a fun learning game in minutes. You can make a series of multiple choice questions or other formats. The format and number of questions are entirely up to you. Add videos, images and diagrams to your questions to amplify engagement. Best played in a group setting, for example, a classroom. Players answer the questions on their own devices, while games are displayed on a shared screen to unite the lesson. Besides creating your own, you can search among millions of existing games.
View Technology


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Any one of these steps alone is insufficient. After all, the townspeople of Trent, Italy, achieved consensus around the fake news of the murder of Simon by the Jews in their community, and acted on the fake news. They did not withhold judgment until the “analysis” came out.
in-context


--- layout: page weight: 0 title: Ruby navigation: show: true ---
This example shows how to send email plain text and HTML email using Ruby. The gem Mail is required.
{% codeblock lang:ruby %} require 'mail' Mail.defaults do delivery_method :smtp, { :address => "smtp.sendgrid.net", :port => 587, :domain => "yourdomain.com", :user_name =><EMAIL_ADDRESS>:password => "yourPassword", :authentication => 'plain', :enable_starttls_auto => true } end mail = Mail.deliver do to<EMAIL_ADDRESS>from 'Your Name


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Drawing Fun with Inktober 2019
Inktober is an art challenge to produce an ink drawing every day for the month of October. Every day has a word associated with it to draw inspiration from. I had thought about participating last year but couldn’t quite find the time. This year, 9 days into October, I decided it’s never too late to start.
Here are some board game related drawings that I had some fun with:


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Favelle Favco may climb higher, says RHB Retail Research
by theedgemarkets.comKUALA LUMPUR (Nov 15): RHB Retail Research said Favelle Favco Bhd may climb higher after posting another white candle and hitting its new 52-week high.
In a trading stocks note today, the research house said with the 21-day SMA line edging upwards, this implies a bullish outlook.
“A positive bias may emerge above the RM2.85 level, with an exit set below the RM2.78 threshold.
“Towards the upside, the immediate resistance is at the RM3.10 mark. This is followed by the RM3.30 level,” it said.


Basic Details
- 1 Bedroom, 1 Bathroom in a 4-unit condo
- 2 Deeded parking lot spaces
- 809 square feet
- In-unit laundry
- Snow removal
Awesome Features
- Granite countertops
- 8'10" ceilings
- Central air & heat
- Enormous basement storage
- Security & Fire supression systems
Incredible Location
- Minutes to Moody St restaurants
- 1/2 mile to commuter rail
- Easy access to Mass Pike & Rt 128
- Minutes to the Charles River Greenway


Median: Mean: Standard deviation: Range:
Here, you'll be able to find the vocabulary that is used, with some example.
Every example will use this set of data:
92 67 14 34 55 99 51 5 42 38
mean = average
→ It's the exact same!
The mean is the sum of every values divided by the number of values.
(92 + 67 + 14 + 34 + 55 + 99 + 51 + 5 + 42 + 38) / 10 = 49.7
First, you have to sort the data in ascending order (from smallest to largest).
In fact, you can sort it into descending order, it just that the you need your data in ascending for other stuff, like the quartiles for example.
The median is the middle value. So, if there is an odd number of values, it's possible. But when there isn't, you have to take the 2 middle values, and get the average of them.
The median is the same as Q2
.
To get the standard deviation, you first need to find the mean.
Once you have the mean, you need to get the square root of the mean of the squares of the difference between each mean and the number. Didn't understand a thing? Have a look at this:
mean = 49.7 squares' sum = (49.7 - 92)² + (49.7 - 67)² + (49.7 - 14)² + ... squares' mean = squares' sum / 10 standard deviation = sqrt(squares' mean)
The range is easy. It's the largest value minus the smallest one
99 - 5 = 94
Made with ♥ by @math2001


Printed Circuit Design and Construction
For the expansion cards in the Alice 2, we decided to use printed circuits. Our original plan was to design, prototype, construct, and integrate each card with some sort of start-to-finish design philosophy. We ended up with a process that more or less matched our intentions, but with a little freedom built-in for how motivated we felt any given day. We designed each board on paper, built a prototype, created a online copy of the printed circuit and transferred it to copper, etched the copper and tinned the resulting traces, fixed-up, drilled, soldered, and finally tested it in the actual Alice 2 card cage.
The pictures in this page document the creation of the I/O board:
Our initial design phase usually consisted of dinner. Sometimes, over food or wine (our initial months spent on Alice were also spent educating ourselves about wine) we would discuss the features which a given board should have, and that would turn into a circuit on paper. Other times, one of us would sketch out a block diagram of the board and bring it to Brad's apartment (and later Brad's house) for the initial prototype implementation. A detailed initial paper design is very valuable, both for history and for later debugging. Several of our boards did not function correctly when built and we used the initial block diagram as a debugging tool, as described later.
This initial diagram ranged in detail from blocks representing integrated circuits connected by lines indicating signals to detailed circuit diagrams including transistors, resistors, gates, etc. We found it very useful to put down all the information we could gather, including pin numbers and signal abbreviations.
From the design on paper, we would usually create breadboarded prototypes. We built two DB-25 extension cables that connected the boardboard to the system bus.
One board we did not fully prototype was the CPU and interrupt board, and we absolutely regretted that. Our CPU prototype did not include the interrupt controller logic (we had no interrupting peripherals at that point), so we only tested that the CPU would run, then built the entire CPU board including the untested interrupt controller. We spent several months debugging the board, finally building another complete CPU board to fix bugs caused by our interpretation of the interrupt controller documentation. See our description of the CPU board for details about this unfortunate problem.
Once the breadboarded prototype worked to our satisfaction, we created an online master of the printed circuit. We used a program called tgif, which was the bane of our lives for the entire Alice project. We could wax prosaic about how bad tgif is. One example of a problem we had in tgif was that we preferred laying down circuit traces at integer multiples of 45 degrees, to make nice pretty boards. Until version 4, which was released after our last board was designed, tgif had no option to snap lines to 45 degrees. It seems like a very simple and also quite useful feature (most graphic design programs support it), but we spent a lot of time looking at the pixels in the lines on the screen trying to get as close to 45 degrees as possible. To be fair, tgif did the job better than any other program we had access to under Linux, including xfig, so we deeply appreciate its existence.
We transferred this image to a transparency for ironing onto copper. We never seemed to finalize our process for this. At one time, as is detailed in the project notebook, we printed directly onto laser printer transparencies. At another time, we printed onto paper with a laser printer and used a copier to transfer the image to the transparency. A transparency from a laser printer definitely will reproduce the original design more accurately, but it seems that the toner deposited by a copier was more suited to ironing.
Ironing was probably the most iffy part of the process. We sandwiched each transparency between a cleaned copper board (toner part against the copper!) and a piece of construction paper, so that the plastic transparency would not stick to the iron. Because we created the circuit master from the perspective of the parts side of the board (some would call that the "front"), we did not have to reverse the printout before ironing.
Each of us in turn would sit with an iron set to "cotton", slowly passing over the surface of the board. Ironing the transparency causes the toner to melt and adhere to the copper. We settled on a total ironing time of about fifteen minutes, since that seemed to maximize the amount of toner successfully transferred without making it smear too much.
Because the transfer was never perfect, we spent a certain amount of time afterwards fixing each trace with a circuit pen. The heat of the iron would often make the transparency warp, which damaged some traces, especially at the edges of the board.
The copper boards were each larger than the size of the card cage we used for Alice 2. Each circuit was surrounded on the transparency by a rectangle indicating the final size for the circuit board. We built a contraption out of wooden planks which allowed us to drag the circuit board under a rotating blade on a Dremel tool. We cut these boards while wearing masks and safety goggles. We did not want to breathe fumes from burning fiberglass and we did not want fiberglass or copper shards to go into our eyes!
Finally, we submerged each board in copper etching solution to remove all the copper from the board except under the toner, which is not affected by the etchant. We didn't have a set time for bathing each board so we would occasionally pull it out and see if it was done. It usually took about ten minutes, but once, while etching, we forgot the board was submerged for about half an hour, and, when we removed it, the copper under the toner was etched away and all that was left was a wash of long strands of toner!
We subsequently cleaned away the toner with steel wool. The resulting board shined brightly, but, unless protected, the copper traces would usually oxidize after a few months and take on a dull finish.
We protected the copper from corrosion and made the board more receptive to soldering by plating tin on the copper traces. We tried a few different tinning products, but finally settled on a bottle of M.G. Chemicals “Liquid Tin” solution (catalog item 421) that took about ten minutes to tin a board, and the solution could then be poured back into its bottle for use another day.
Once tinned, we needed to check the connectivity of each trace to make sure electricity could actually get from the bus connector to each component. We would print out the master copy, check each path between components or to the bus, and mark the trace as checked on the printout. Often we would have to correct a trace with a conductive pen.
We drilled thousands of holes over the lifetime of this project, since components were placed on one side of the board and soldered on the other. Drilling while standing above the board rapidly proved too painful, so we set up a vertical drilling stand on which we C-clamped the board. Drilling was probably the most time-consuming part of board construction, and it was surprisingly hard for us to find drill bits that were the right size for component leads. We had to use a slightly-too-large bit more than once, and we usually regretted it because the holes would cut completely through a solder pad and we'd have to solder very carefully to make sure the lead had contact with the ends of the trace! Once the holes were drilled, we soldered each component to the board.
Making a printed circuit turned out to be a lot more work than we were expecting.
We usually ended up designing and prototyping the board over a few days. Designing the circuit master with tgif then took up to six hours sometimes spread over weeks. We usually reserved an entire Saturday afternoon and evening to iron, etch, tin, fix, and drill, and we sometimes had to continue the next day.
We certainly learned a lot about the process of manufacturing boards for hobby purposes. These techniques, however, would have to be substantially improved for production computer hardware. We have debated whether we would have been better served by wirewrapped boards. Our card cage has only a certain amount of clearance between cards, but even wirewrap leads probably would have fit. We never tried a wirewrap solution, and it would have helped if we had tried both printed circuits and wirewrapped prototyping before we started.
Editorial by Brad: When I originally built the Alice 1, I tried to follow as detailed a design process as possible. It took me about two weeks, spending much of each night working with a graphic design tool or etching or drilling. I must have been really enjoying myself and not paying attention, because I didn't really understand how much time it took until we started the same process with many larger boards instead of the one small board.