In this post, I am going to talk about the similarities between Reinforcement Learning and Neuroscience. The main area I will be focusing is the actor critic method in RL and whether our brains also functions in the same way.
Actor critic algorithm is kind of a method in which both value functions and policies are being learned. Critic is the element that learns whatever policy is being followed by the actor so that it can criticize the action choices of actor and Actor is the element that learns policies. The critic makes use of reinforcement signal also known as TD error to learn the value function by considering the policy which is being followed by the actor at this moment. This value function learned by the critic allows it to send reinforcement signal to actor. If this reinforcement signal is negative, it means that actor’s action resulted in a state which was not as good as that was expected and in simple words action taken can be classified as bad. On the other hand, if this reinforcement signal is positive then it means action taken was actually better than expected.
In our brain, we also have such components mainly know as dorsal striatum and ventral striatum. To further explain these terms, I must describe that striatum is the kind of input structure for basal ganglia (a collection of neuron groups in the forebrain). Basal ganglia are the part of the brain involved in decision making, planning and learning. As the names suggest, dorsal striatum and ventral striatum are the subdivision of striatum while the former being responsible for influencing action selection and the latter is involved in critical assignment of affective value to sensations. Ventral striatum can be considered as the critic which is being critical for different aspects o reward processing and Dorsal striatum can be stated as an actor.
To further explain these similarities, following figure can be used:
Fig(a) shows a simple actor critic method as a neural network. The actor has a layer of A neurons which are categorized from 1 to k and their output can be considered as a vector of k-dimensions. The features which are labeled as x from 1 to n have a connection to V and to each actor neuron A. These connections can also be considered as weights as in any artificial neural network. All of these weights are used to parametrize policies and value functions. Actor weights are updated to parametrize the policy and critic weights update to parametrize value function.
The critic in the fig(a) has a single V neuron which also connects with features. This neuron’s output are state values and TD which calculates TD error by combining reward signal, output of V and previous state values. The phenomena of previous state values is shown by a loop to itself. TD error calculated is then being used to update weights of both actor and critic.
The figure on the left fig(b) shows how the ANN(artificial neural network) in the right figure maps to the brain. This figure also shows that actor is the dorsal striatum and critic is the ventral striatum of the brain. This hypothesis was put forward by Takahashi et al. (2008).
The TD error is now being replaced by Dopamine which is also considered as the reinforcement signal in the brain. The cerebral cortex is now the input structure which gives the states as stimuli. The ventral striatum sends it information to VTA and SNpc , which then after combining with reward and previous stimulus are being used to create Dopamine. How is this being calculated is still an active research area.
After this Dopamine which also represents the widely branching axons of dopamine neurons whose cell bodies are in the VTA and SNpc. In short to understand VTA and SNpc, we can say that these are the cluster of neurons in the midbrain. These cluster consists of neurons which produce dopamine as a neurotransmitter.
Another important consideration is that the reward in the left part of the figure can not be considered as R we use in reinforcement learning. Many interconnected neural systems generate information related to rewards, with diﬀerent structures being recruited depending on diﬀerent types of rewards. Dopamine neurons receive information from many diﬀerent brain areas, so the input to the SNpc and VTA labeled ‘Reward’ in fig(b) should be thought of as vector of reward-related information arriving to neurons in these nuclei along multiple input channels.
Nevertheless, this comparison can be considered as a generalization of the real mechanisms being performed within the brain. A lot has still to be found about brain and research in neuroscience can further help us to understand our own neural networks. Both reinforcement learning and neuroscience research has helped each field to progress and development in one will surely benefit the other.
This post was inspired from the book “Reinforcement Learning” by Richard S. Sutton and Andrew G. Barto.
References:
1. Image adapted from Frontiers in Neuroscience, vol. 2(1), 2008, Y. Takahashi, G. Schoenbaum, and Y. Niv, Silencing the critics: Understanding the eﬀects of cocaine sensitization on dorsolateral and ventral striatum in the context of an Actor/Critic model.
2. http://incompleteideas.net/book/the-book-2nd.html


git
projectI am assuming that you already have Android Studio and git
installed on your computer. If you are on Windows, installing Babun allows you to run command with the Cygwin environment, and also installs git
by default (see How to install Babun).
git
On the "Welcome to Android Studio" window, go to Configure > Preferences > Version Control > Git.
Set the "Path to Git executable" to the location of git
(which you can find using the which git
command. If you are on Windows, it is probably C:\Users\_YOUR_NAME_\.babun\bin\git
).
Go back to the "Welcome to Android Studio" window, and click "Check out from Version Control", then GitHub.
Type the host (github.com
or github.company.com
if you are using GitHub enterprise) and your credentials.
If you want Android Studio to remember your password with: "Save password", you can set a master password (you will need to type this in order for Android Studio to access your previous password), or just leave it blank.
Choose the GitHub repository you want to clone (copy) to your computer
Choose where to copy it (the parent directory must not have the same directory already in it).
You have now successfully configured Android Studio to link your Android project to GitHub.


A bit smarter Wikimedia API application
This page sends request to Wikimedia API. Only english Wikipedia is supported
Usage: to get a PDF file use an article name Only A4 is supported. To cite the article use full URL of the article. Text will be displayed in the output box. Only citation for Zotero and BibTeX are supported.


Overview
This page provides a quick overview of the pymicro code base.
As of version 0.1 (august 2014), pymicro is organised around 4 packages:
- pymicro.apps
contains a simple application to visualise and interact
with 2D images and 3D volumes;
- pymicro.crystal
contains various modules to handle crystal lattices,
crystallographic grains and to organise them into microstructures;
- pymicro.file
contains many helpers methods to handle typical file
reading and writting found at ESRF, ANKA and Soleil;
- pymicro.view
contains the two principal modules for 3D visualisation:
vtk_utils.py for rendering still 3D images and vtk_anim.py to create
animations.
Installation
You can grab a copy of the latest version on the git repository at Centre des Materiaux, open a
terminal and type:
git clone git://vcs/pymicro
This will create a pymicro folder. Then you must add the folder containing pymicro to the
PYTHONPATH environement variable. To do this you can add this line to your .cshrc file:
setenv PYTHONPATH /path/to/folder/containing/pymicro
Then you will be able to use pymicro (you may have to source the .cshrc or restart the terminal).
For instance after starting a ipython shell:
from pymicro.file.file_utils import HST_read
If you want to use pymicro interactively and import all modules at ipython startup you may apply
the following recipe: Using pymicro
interactively
Dependencies
- Python 2.6+ required. Tested with 2.6 and 2.7, please report any problem.
- numpy - For array, matrix and other numerical manipulations. Used extensively
by all modules.
- scipy 0.10+, mainly used for ndimage filters.
- skimage for additional image analysis (http://scikit-image.org).
- matplotlib 1.1+ for plotting (e.g. pole figures or 3D image slices).
- VTK with Python bindings 5.8+ (http://www.vtk.org/)
for visualization of
3D data using the pymicro.view package.
External
- Crystal lattices can be created using CIF files usig the pymicro.crystal.lattice.from_cif
method. We use PyCifRW to read and parse CIF files.
- reading and writing 3d Tiff files is supported via the TiffFile module.
API documentation
For detailed documentation of all modules and classes, please refer to the
API docs.


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Customer Name: {{ delivery.customer.user.username}} Restaurant Name: {{ delivery.restaurant.user.username}} Cost: {{ delivery.totalCost}} Pickup Time: {{ delivery.pickeduptime}} Delivery Time: {{ delivery.deliverytime}} {% if not delivery.pickedup %} {%else%} {%endif%} Addresses |


https://docs.python.org/3/library/unittest.mock.html
https://realpython.com/python-mock-library/
Understanding the Python Mock Object Library
'unittest.mock' is a library for testing in Python (Py3.3+). It allows you to replace parts of your system under test with mock objects and make assertions about how they have been used.
'Mock' is a flexible mock object intended to replace the use of stubs and test doubles throughout your code.
'MagicMock' is a subclass of 'Mock' with all the magic methods pre-created and ready to use.
Mock can create arbitrary attributes on the fly.
from unittest.mock import Mock mock = Mock() print(mock) print(mock.some_attribute) print(mock.do_something()) # <Mock id='140138960515368'> # <Mock name='mock.some_attribute' id='140138960515592'> # <Mock name='mock.do_something()' id='140138958324288'>


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IIT Kanpur Develops Autonomous Drone ‘Prahari’ That Can Capture Rogue Drones
by IANSStudents of IIT-Kanpur have developed a drone named 'Prahari' which can not only carry a load of about 4 to 5 kilograms but can also patrol sensitive zone for three hours continuously.
The autonomous system (drone) consists of an unmanned helicopter with drone catching net. 'Prahari' is unique in this way that it can chase, outrun and capture the rogue drone.
This system has been developed by Prof Abhishek and Prof Mangal Kothari of the department of aerospace engineering and their team of students.
The drone is also equipped with an advanced autopilot system, and is capable of handling the sudden changes in weight and inertia (of this drone) while capturing the rogue drone.
Specially designed for border surveillance and national security, this could be very handy for the armed forces for maintaining vigilance along the border areas and for capturing the rogue drones which pose a serious threat to national security.
Recently, HRD minister Ramesh Pokhriyal 'Nishank' had shared the video of this drone on Twitter.
Prof Abhishek said, "This drone can be used for a wide range of applications, ranging from surveillance, crowd monitoring, crowd control, delivery of emergency supplies, precision agriculture, hostage situations, chemical and nuclear agent detection. It can also outrun the rogue drone and can capture it in its nets.
(This story has been published from a wire agency feed without modifications to the text. Only the headline has been changed.)


This was my first time with DON and purely a research endeavor, as there was little to no information provided about this compound. The entry in PiHkAL suggested that this be the more stimulating and less psychedelic version of DOB. I started with a dose of 2.5mg (lower than the 3.0mg starter dose suggested in PiHKaL). Here goes:
Dose: 2.5 mg orally dissolved in tea. This was taken on an almost empty stomach.
Mindset: Woke up early in the morning after getting absolutely shittedfaced on some GBL the night before. This may or may not have an effect due to Lactone's reported dopamine-inhibitor action. Felt completely normal though.
T+1:00: Started feeling a very slight jaw tension and a rise in blood pressure. Not much else at this point. Come up is said to be around 3-3.5 hrs with this one. Also noticed some unpleasant 'stomach effects'.
T+1:30: The stomach had settled so no real side effects except for slight shaking of the limbs. Noticed an increase in stimulation and a little bit of added confusion. Colors seem really bright but no visuals. Similar to a come up on a low (1.5mg) dose of DOB except for there is a lot more stimulation and a lot less side effects.
T+2:00-2:30: This is nearing the 'peak' I suppose. Not much of a psychedelic acpect here really, just a nice even at times calming stimulation which goes like this: I do not feel the uncontrollable urge to do stuff like with amphetamines, but no super-calm-as-a-tank euphoria as with meth either. I almost feel some kind of 'inner peace' without any schitzophrenic ideations as I get with DOB. Just a nice mellow high, spoiled only slightly by shaking in my legs. Feels fine after some stretching is done though. Also drank some tea here which is always a nice booster.
This is T+3:00 and I just got out of the shower feeling refreshed and relaxed.
So this is more euphoric than most other phenetylamines I've tried (exceptions would be 2C-I and 2C-B) but doesn't quite turn me out on overstimulation like STP does at the peak. Still get the 'laughs' a little bit as with any phenetylamine (this is just a silly laugh a lot of people get which I think is a result of overstimulation; I get it with 2C-B, DOB and STP).
It is now time for me to make my moves for the day, so we'll hit the road and see how well this fares as a 'workable high'. I dont look too messed up when I'm on this and unlike DOB, this here allows me to think better and clearer, a very amphetamine-like quality.
Anything even closely resembling a psychedelic experience was gone after the come up and the only thing left was the semi-euphoric stimulation that lasted another 15 hrs before subsiding. I felt quite cracked out on this but not anything as bad a DOB where you ask yourself 'Why did I do this to my brain?' ))).
Now it is T+12:00 and the stimulation is gradually subsiding. Visited my local pharmacy for some clonazepam and took half a tablet equaling 1mg. This provided the synergy I described with DOB. Clonazepam was an unusually deep and emotional experience and felt like 'medicine for the soul' - a very nice way to end the 'trip' and get the nice calm 'afterglow'. Then I smoked a lot of Cannabis and took another 1mg Clonazepam then went to a bar with some girls where I had a beer. So anyways here's the conclusion:
DON is a nice substitute for amphetamine, more economical than meth, which I wanna do more and then more and after that wait 2 minutes and do some more again, all at lower doses. I'd imagine the side effects could get a bit rough at 4.5-5.0mg but I had little to no side effects common to STP and DOB. Could be an awesome party drug for sure.
Peace, be safe ;-)
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Lattice Synthesis Timing Report -------------------------------------------------------------------------------- Lattice Synthesis Timing Report, Version Sat Jan 19 00:15:11 2019 Copyright (c) 1991-1994 by NeoCAD Inc. All rights reserved. Copyright (c) 1995 AT&T Corp. All rights reserved. Copyright (c) 1995-2001 Lucent Technologies Inc. All rights reserved. Copyright (c) 2001 Agere Systems All rights reserved. Copyright (c) 2002-2017 Lattice Semiconductor Corporation, All rights reserved. Report Information ------------------ Design: top Constraint file: Report level: verbose report, limited to 3 items per constraint -------------------------------------------------------------------------------- ================================================================================ Constraint: create_clock -period 5.000000 -name clk0 [get_nets out_vga_ck_c] 3723 items scored, 3723 timing errors detected. -------------------------------------------------------------------------------- Error: The following path violates requirements by 6.418ns Logical Details: Cell type Pin type Cell name (clock net +/-) Source: FD1S3AY CK \u_vga_core/u0_vga_timing/fsm_v__i1 (from out_vga_ck_c +) Destination: FD1P3AX D \u_vga_core/u0_vga_timing/cnt_v_i0_i15 (to out_vga_ck_c +) Delay: 11.272ns (41.7% logic, 58.3% route), 15 logic levels. Constraint Details: 11.272ns data_path \u_vga_core/u0_vga_timing/fsm_v__i1 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 violates 5.000ns delay constraint less 0.146ns L_S requirement (totaling 4.854ns) by 6.418ns Path Details: \u_vga_core/u0_vga_timing/fsm_v__i1 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 Name Fanout Delay (ns) Pins Resource(Cell.Net) L_CO --- 0.403 CK to Q \u_vga_core/u0_vga_timing/fsm_v__i1 (from out_vga_ck_c) Route 13 e 1.554 \u_vga_core/u0_vga_timing/fsm_v[1] LUT4 --- 0.448 B to Z \u_vga_core/u0_vga_timing/i1_2_lut_rep_36 Route 2 e 0.954 \u_vga_core/u0_vga_timing/n3536 LUT4 --- 0.448 C to Z \u_vga_core/u0_vga_timing/i2_2_lut_3_lut_4_lut Route 1 e 0.788 \u_vga_core/u0_vga_timing/n6_adj_784 LUT4 --- 0.448 D to Z \u_vga_core/u0_vga_timing/i2083_4_lut Route 1 e 0.020 \u_vga_core/u0_vga_timing/n3335 MUXL5 --- 0.212 ALUT to Z \u_vga_core/u0_vga_timing/i2084 Route 1 e 0.788 \u_vga_core/u0_vga_timing/n3336 LUT4 --- 0.448 D to Z \u_vga_core/u0_vga_timing/i3_3_lut_4_lut Route 16 e 1.516 \u_vga_core/u0_vga_timing/n23 A1_TO_FCO --- 0.752 A[2] to COUT \u_vga_core/u0_vga_timing/add_277_1 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2931 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_3 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2932 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_5 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2933 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_7 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2934 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_9 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2935 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_11 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2936 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_13 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2937 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_15 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2938 FCI_TO_F --- 0.544 CIN to S[2] \u_vga_core/u0_vga_timing/add_277_17 Route 1 e 0.788 \u_vga_core/u0_vga_timing/cnt_v_15__N_746[15] -------- 11.272 (41.7% logic, 58.3% route), 15 logic levels. Error: The following path violates requirements by 6.324ns Logical Details: Cell type Pin type Cell name (clock net +/-) Source: FD1P3AX CK \u_vga_core/u0_vga_timing/cnt_v_i0_i1 (from out_vga_ck_c +) Destination: FD1P3AX D \u_vga_core/u0_vga_timing/cnt_v_i0_i15 (to out_vga_ck_c +) Delay: 11.178ns (42.1% logic, 57.9% route), 15 logic levels. Constraint Details: 11.178ns data_path \u_vga_core/u0_vga_timing/cnt_v_i0_i1 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 violates 5.000ns delay constraint less 0.146ns L_S requirement (totaling 4.854ns) by 6.324ns Path Details: \u_vga_core/u0_vga_timing/cnt_v_i0_i1 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 Name Fanout Delay (ns) Pins Resource(Cell.Net) L_CO --- 0.403 CK to Q \u_vga_core/u0_vga_timing/cnt_v_i0_i1 (from out_vga_ck_c) Route 9 e 1.363 \u_vga_core/u0_vga_timing/cnt_v[1] LUT4 --- 0.448 C to Z \u_vga_core/u0_vga_timing/i1_2_lut_rep_31_3_lut Route 3 e 1.051 \u_vga_core/u0_vga_timing/n3531 LUT4 --- 0.448 C to Z \u_vga_core/u0_vga_timing/i4_4_lut_4_lut Route 1 e 0.788 \u_vga_core/u0_vga_timing/n3131 LUT4 --- 0.448 B to Z \u_vga_core/u0_vga_timing/i2095_3_lut Route 1 e 0.020 \u_vga_core/u0_vga_timing/n3334 MUXL5 --- 0.212 BLUT to Z \u_vga_core/u0_vga_timing/i2084 Route 1 e 0.788 \u_vga_core/u0_vga_timing/n3336 LUT4 --- 0.448 D to Z \u_vga_core/u0_vga_timing/i3_3_lut_4_lut Route 16 e 1.516 \u_vga_core/u0_vga_timing/n23 A1_TO_FCO --- 0.752 A[2] to COUT \u_vga_core/u0_vga_timing/add_277_1 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2931 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_3 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2932 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_5 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2933 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_7 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2934 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_9 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2935 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_11 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2936 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_13 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2937 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_15 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2938 FCI_TO_F --- 0.544 CIN to S[2] \u_vga_core/u0_vga_timing/add_277_17 Route 1 e 0.788 \u_vga_core/u0_vga_timing/cnt_v_15__N_746[15] -------- 11.178 (42.1% logic, 57.9% route), 15 logic levels. Error: The following path violates requirements by 6.306ns Logical Details: Cell type Pin type Cell name (clock net +/-) Source: FD1P3AX CK \u_vga_core/u0_vga_timing/cnt_v_i0_i6 (from out_vga_ck_c +) Destination: FD1P3AX D \u_vga_core/u0_vga_timing/cnt_v_i0_i15 (to out_vga_ck_c +) Delay: 11.160ns (42.2% logic, 57.8% route), 15 logic levels. Constraint Details: 11.160ns data_path \u_vga_core/u0_vga_timing/cnt_v_i0_i6 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 violates 5.000ns delay constraint less 0.146ns L_S requirement (totaling 4.854ns) by 6.306ns Path Details: \u_vga_core/u0_vga_timing/cnt_v_i0_i6 to \u_vga_core/u0_vga_timing/cnt_v_i0_i15 Name Fanout Delay (ns) Pins Resource(Cell.Net) L_CO --- 0.403 CK to Q \u_vga_core/u0_vga_timing/cnt_v_i0_i6 (from out_vga_ck_c) Route 5 e 1.222 \u_vga_core/u0_vga_timing/cnt_v[6] LUT4 --- 0.448 A to Z \u_vga_core/u0_vga_timing/i2064_2_lut_rep_35 Route 5 e 1.174 \u_vga_core/u0_vga_timing/n3535 LUT4 --- 0.448 B to Z \u_vga_core/u0_vga_timing/i2_3_lut_4_lut_adj_18 Route 1 e 0.788 \u_vga_core/u0_vga_timing/n3280 LUT4 --- 0.448 A to Z \u_vga_core/u0_vga_timing/i2095_3_lut Route 1 e 0.020 \u_vga_core/u0_vga_timing/n3334 MUXL5 --- 0.212 BLUT to Z \u_vga_core/u0_vga_timing/i2084 Route 1 e 0.788 \u_vga_core/u0_vga_timing/n3336 LUT4 --- 0.448 D to Z \u_vga_core/u0_vga_timing/i3_3_lut_4_lut Route 16 e 1.516 \u_vga_core/u0_vga_timing/n23 A1_TO_FCO --- 0.752 A[2] to COUT \u_vga_core/u0_vga_timing/add_277_1 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2931 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_3 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2932 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_5 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2933 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_7 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2934 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_9 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2935 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_11 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2936 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_13 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2937 FCI_TO_FCO --- 0.143 CIN to COUT \u_vga_core/u0_vga_timing/add_277_15 Route 1 e 0.020 \u_vga_core/u0_vga_timing/n2938 FCI_TO_F --- 0.544 CIN to S[2] \u_vga_core/u0_vga_timing/add_277_17 Route 1 e 0.788 \u_vga_core/u0_vga_timing/cnt_v_15__N_746[15] -------- 11.160 (42.2% logic, 57.8% route), 15 logic levels. Warning: 11.418 ns is the maximum delay for this constraint. Timing Report Summary -------------- -------------------------------------------------------------------------------- Constraint | Constraint| Actual|Levels -------------------------------------------------------------------------------- | | | create_clock -period 5.000000 -name | | | clk0 [get_nets out_vga_ck_c] | 5.000 ns| 11.418 ns| 15 * | | | -------------------------------------------------------------------------------- 1 constraints not met. -------------------------------------------------------------------------------- Critical Nets | Loads| Errors| % of total -------------------------------------------------------------------------------- \u_vga_core/u0_vga_timing/n23 | 16| 3372| 90.57% | | | \u_vga_core/u0_vga_timing/n3336 | 1| 3372| 90.57% | | | \u_vga_core/u0_vga_timing/n3334 | 1| 1798| 48.29% | | | \u_vga_core/u0_vga_timing/n2934 | 1| 1716| 46.09% | | | \u_vga_core/u0_vga_timing/n2935 | 1| 1716| 46.09% | | | \u_vga_core/u0_vga_timing/n3335 | 1| 1574| 42.28% | | | \u_vga_core/u0_vga_timing/n2933 | 1| 1492| 40.08% | | | \u_vga_core/u0_vga_timing/n2936 | 1| 1492| 40.08% | | | \u_vga_core/u0_vga_timing/n2932 | 1| 1048| 28.15% | | | \u_vga_core/u0_vga_timing/n2937 | 1| 1048| 28.15% | | | \u_vga_core/u0_vga_timing/n3131 | 1| 913| 24.52% | | | \u_vga_core/u0_vga_timing/n3533 | 2| 911| 24.47% | | | \u_vga_core/u0_vga_timing/n3280 | 1| 885| 23.77% | | | \u_vga_core/u0_vga_timing/cnt_v[2] | 12| 572| 15.36% | | | \u_vga_core/u0_vga_timing/n10_adj_786 | 1| 572| 15.36% | | | \u_vga_core/u0_vga_timing/fsm_v[1] | 13| 512| 13.75% | | | \u_vga_core/u0_vga_timing/n6_adj_784 | 1| 493| 13.24% | | | \u_vga_core/u0_vga_timing/cnt_v[6] | 5| 490| 13.16% | | | \u_vga_core/u0_vga_timing/cnt_v[9] | 5| 490| 13.16% | | | \u_vga_core/u0_vga_timing/cnt_v[3] | 9| 456| 12.25% | | | \u_vga_core/u0_vga_timing/n3531 | 3| 429| 11.52% | | | \u_vga_core/u0_vga_timing/n3532 | 1| 429| 11.52% | | | \u_vga_core/u0_vga_timing/cnt_v[0] | 12| 426| 11.44% | | | \u_vga_core/u0_vga_timing/cnt_v[1] | 9| 426| 11.44% | | | \u_vga_core/u0_vga_timing/cnt_v_15__N_74| | | 6[15] | 1| 416| 11.17% | | | \u_vga_core/u0_vga_timing/n2931 | 1| 400| 10.74% | | | \u_vga_core/u0_vga_timing/n2938 | 1| 400| 10.74% | | | \u_vga_core/u0_vga_timing/cnt_v_15__N_74| | | 6[13] | 1| 376| 10.10% | | | \u_vga_core/u0_vga_timing/cnt_v_15__N_74| | | 6[14] | 1| 376| 10.10% | | | -------------------------------------------------------------------------------- Timing summary: --------------- Timing errors: 3723 Score: 17839543 Constraints cover 15252 paths, 999 nets, and 2077 connections (87.9% coverage) Peak memory: 75530240 bytes, TRCE: 7450624 bytes, DLYMAN: 163840 bytes CPU_TIME_REPORT: 0 secs


{%extends 'all/base.html'%} {%load static%} {%load bootstrap4%} {%block content%}
Bio ::: {{profile.bio}}
updated on {{profile.update_on}}
Borrowed books ::: {{borrowed_books.count}}
Books
{%for books in borrowed_books %}


Driving Sardine Inovation and Sardine Based Mobile Experiences!
{ "statement_date": "14/07/2013", "amount_due": "2399.00" }
{ "order_number": "8820023-28" }
{ "orders": [ { "id": "8820023-28", "status": "pending" } ] }


Dundee United agree new deal with key midfielder Calum Butcher
Dundee United have agreed a new deal with key midfielder Calum Butcher.
by Ian RoacheThe 28-year-old Englishman rejoined the Tangerines for a second spell at the end of the last January window, initially signing an 18-month contract.
His second stint has been so successful, though, that the Championship leaders have moved to replace that deal with a new, improved one.
Butcher previously enjoyed two seasons at Tannadice before moving to Burton Albion in the summer of 2015, going on to also have spells at Millwall and Mansfield.
Since returning, he has been one of United’s most important players and provided strength and skill both in the middle of the park and in central defence when required.
Butcher further endeared himself to the Tangerines’ fans with two goals in the 6-2 derby win over city rivals Dundee at Tannadice back in August.


Didier
{% if allPurchaseTimes %}
{% for eachCup in allPurchaseTimes %}
- {{ eachCup.name }}
{% endfor %}
{% else %}
Something happened.
{% endif %}
Here are all of the cups manufactured after 2012
{% if after2012 %}
{% for eachCup in after2012 %}
- {{ eachCup.name }}
{% endfor %}
{% else %}
Something happened.
{% endif %}


Mk-12 RV mockup
The W62 is the warhead used with the Mk 12 reentry vehicle which arms a portion of the Minuteman III ICBM force. Designed for use on MIRV (multiple independently targeted reentry vehicle) bus upper stage.
| Yield | 170 Kilotons |
|---|---|
| Weight | Warhead/RV: 700-800 lb; Warhead: 253 lb |
| Length | RV Body: 72 in; Warhead: 39.3 in |
| Diameter | RV Body: 21 in; Warhead: 19.7 in |
| Number In Service | 580 (330 active, 250 inactive) |
Two stage radiation implosion weapon.
RV CEP (circle error probability) is 900 feet
Known to be vulnerable to "hot" (high energy thermal) X-rays like those produced by thermonuclear ABM warheads.
Contains plutonium as primary fissile material
Fissile core is beryllium reflected
Deuterium-tritium boosted
High explosive is LX-04
Probably contains lithium-6 (95% enrichment) deuteride fusion fuel
and enriched uranium in the secondary stage.
Minuteman III ICBM. Each missile carries up to three W62s, and is based in a hardened underground silo. About 150 missiles have one warhead, and another 50 have three warheads.
Dual key launch control.
Designed and developed by Lawrence Livermore National Laboratory (LLNL).
| June 1964 | Development engineering begun at LLNL |
|---|---|
| March 1967 | Production engineering begun |
| March 1970 | Quantity production begun |
| June 1976 | Quantity production ends |
| April 1980 | Retirement of early mods begun |
| October 2006 | Final retirement of remaining warheads begun |
| 2009 | Complete retirement planned |
Initial manufacture March 1970
Initial deployment 1970 (first squadron of Minuteman III missiles went operational on 29 December). Between December 1979 and February 1983 900 W62s were replaced by W78/Mk-12As. Retirement of early mods began in April 1980, presumably the retirement consisted of W62s that had been replaced by W78s. Some of the remaining W62s were modified in a program called Alt 903.
1725 W62 warheads were manufactured.
This warhead was originally slated for retirement and dismantling under START II. The 1995 Nuclear Policy Review reversed this and the W62 was redesignated for permanent retention. Under the 2002 Nuclear Posture Review this warhead was again set for permanent retirement. It is now scheduled for complete retirement from service in 2009 and will be completely dismantled.
Currently in service: 580 warheads (the rest have been permanently retired). At the start of the final retirement from service in October 2006 some 330 were being kept on active duty at any given time.
Replacement of deployed W62 warheads by W87 warheads from the now-retired Peacekeeper missile began in October 2006 and is currently proceeding at a rate of one missile per week. Completion of replacement is due in 2009.


The Membership provider in asp.net is great. As is the Roles support. You can do some very nifty things with it with a considerable amount of ease. You can get hashed or encrypted passwords and a whole host of features that’s pretty much enterprise grade. And you get it for free. Well, not free, but it is pretty easy to set up.
But what if your website only needed two or three user accounts? What if you didn’t need all that fancy stuff. You just want to use a login system that’ll just block off an admin area or something and you want to do it quick and dirty. Maybe it’s a prototype. Maybe it’s your own little site where you just want simplicity for authentication. I’ve actually went through all the aspnet_regsql.exe trouble for a very simple site that would have just one user. Sounds like overkill? It is.
The login controls provided in asp.net 2.0 are quite handy, but they mostly work with a membership provider. The <asp:Login> control is specially helpful for creating a login screen in no time. But it too works with a Membership provider.
So how can we quickly setup a login system without using a Membership provider? And how can we use the <asp:Login> control to set up the login screen when using such a system? Read on…
Forms authentication actually allows you to store usernames and passwords in the web.config file. A typical web.config file doing this is shown here:
I’ve highlighted the important parts. The authentication mode needs to be Forms. The PasswordFormat is left as “Cleared” here. It can also be hashed or encrypted. I’ll make a post in the future about hashed and encrypted passwords. I’ve added a single <user> to the credentials node. You could easily add more. In the <authorization> node, I’ve allowed all users. In the <location> node for the “admin” folder, I’ve set it to deny all unauthenticated users. So, if a user is logged in, they can access the admin folder. If not, they’re asked to login.
Now, with this in place, if you were to put an <asp:Login> control on a page and expect it to work just like when using a Membership provider, then you’re in for a nasty surprise. Even if you enter the correct username and password, it’ll tell you the login has failed. Why? The reason is pretty simple. We’ve not mentioned a Membership provider to use, so asp.net defaults to the Membership provider in the machine.config, which usually is an asp.net sql membership provider drawing info from a database in App_Data. That database has no info about our user named admin with the password “pa$$w0rd!”. So, logging in fails. How do we overcome this? Simple. Just add a handler for the OnLoogingIn (NOT OnLoggedIn) event of the <asp:Login> control and in that handler, do this:
[If you don’t want the authentication cookie to persist between requests, set the second parameter of RedirectFromLoginPage to false.]
That’s all there is to it.
Now, that was very simple, but the question that pops up is is it secure?
Well, the answer is both a yes and a no. It’s yes in the sense that IIS will never serve anything with a .config extension from an outside request. So, users on your site will never be able to download the web.config and look at the username and passwords. The “no” part of the answer comes from the fact that if someone can look inside the web.config file, they’d be able to see the usernames and passwords. If you run your own server or have a hosted account that only you access, this is not that big a problem. [I add the latter coz no hosting company is ever going to go through the risk of looking inside of clients’ files for passwords.] For extra security though, you can use hashed or encrypted passwords in this system too, which I’ll cover in a future post.
A drawback to this is that the web.config file can get very big for a lot of users. But then again, this isn’t meant to be used for a lot of users. This is meant for very simple sites woth very few users. There are sites out there that are like this and don’t need all the Membership gloss. If your site’s like that, you can easily use this method.
Hope that helps.


The biggest scoreline in the history of the Fifa WORLD Cup qualifiers – and indeed in the history of international football – was recorded on 11 april 2001, when australia beet american Samoa 31–0.
This legendary match also brought global renown for Archie Thompson, whose 13–goal haul set a new world record, which stands to this day, for an individual player in a single international match.
And though the defeat earned American Samoa ignominy, so inspiring has their subsequent recovery been that it is now the subject of an acclaimed documentary, 'Next Goal Wins', showing across the world.


- SCREEN_DEPTH - Static variable in class com.informatics.lehigh.cardboardarlibrary.StereoScreenRenderer
-
- set3dObject(Object3dContainer) - Method in class com.informatics.lehigh.cardboardarlibrary.Cube
-
- setCameraSurfaceTexture(SurfaceTexture) - Method in class com.informatics.lehigh.cardboardarlibrary.CameraTextureRenderer
-
Sets the surface texture to update in order to get the most recent camera frame
while rendering.
- setCameraTexture(int) - Method in class com.informatics.lehigh.cardboardarlibrary.StereoScreenRenderer
-
Sets the texture to be used for the back-facing camera feed when rendering.
- setCaptureParam(CaptureRequest.Key<T>, T) - Method in class com.informatics.lehigh.cardboardarlibrary.GarActivity
-
Specifies a parameter to be used when capturing from the camera.
- setObjectsTexture(int) - Method in class com.informatics.lehigh.cardboardarlibrary.StereoScreenRenderer
-
Sets the texture to be used for the 3D objects scene to render on top
of the camera feed.
- setProcessingReaderBufferSize(int) - Method in class com.informatics.lehigh.cardboardarlibrary.GarActivity
-
Sets the number of images that the ImageReader for image processing
is able to hold.
- setProcessingSurfaceResolution(Size) - Method in class com.informatics.lehigh.cardboardarlibrary.GarActivity
-
Sets the resolution that the ImageReader used for image processing will
receive the feed from the back facing camera.
- setupCaptureSurfaces() - Method in class com.informatics.lehigh.cardboardarlibrary.GarActivity
-
Sets up and returns the Surfaces to be drawn to by the back-facing
camera capture besides the one rendered in front of the viewer.
- StereoScreenRenderer - Class in com.informatics.lehigh.cardboardarlibrary
-
Renderer that draws a screen in front of the viewer textured with the combination of two textures:
the physical device camera view, and the augmented objects.
- StereoScreenRenderer(Activity) - Constructor for class com.informatics.lehigh.cardboardarlibrary.StereoScreenRenderer
-
Creates a new StereoScreenRenderer.


Where to consume foraged apples at The Orchard on Fortnite map
by Jordan HeckThe Fortnite Trick Shot challenges were released today, and the name might have you a little confused. Although they're called Trick Shot challenges, none of the challenges actually involve doing trick shots.
Instead, most of the challenges are pretty straightforward (eliminations with Pistols, Damage with Shotguns) with a few that might need a little extra guidance. One of those is "Consume Foraged apples at The Orchard." While somewhat easy to understand, you may not remember exactly where The Orchard is.
That's where we come in. The Orchard is located near Frenzy Farm, although it's a little more north of there.
MORE: Pipeman, Hayman and Timber Tent locations to dance at for Fortnite challenge
Consume apples at The Orchard in Fortnite
The map above shows you where The Orchard is on the Fortnite Chapter 2 map.
When you land there, you'll notice apple trees in the area with some apples that have landed on the ground. If you've been playing for a while, you've likely consumed apples in the past. But if not, all you need to do is look at the apple and then a button will appear (depending on what system you're playing on) that shows how to consume the apple. Just hold on to that button for about a second and you'll eat the apple.
As in the past, once you've eaten the apple you will gain five health (if you need it).
The hardest part of this challenge will be fighting off others for apples. Most likely, a lot of people will be landing here to complete the challenge on their own, meaning the apples may be gone if you wait too long. Other than that, this challenge is pretty simple and you should accomplish it quickly.
The challenge requires you to consume three apples.


"With end-to-end tests, you have to wait: first for the entire product to be built, then for it to be deployed, and finally for all end-to-end tests to run. When the tests do run, flaky tests tend to be a fact of life. And even if a test finds a bug, that bug could be anywhere in the product."


White to play 26
pips White=150 Black=147
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 13/11* 11/5 eq: +0.903 68.19% (G:32.37% B:1.46%) 31.81% (G: 6.92% B:0.32%) 2. 24/18 13/11* eq: +0.858 (-0.044) 68.53% (G:26.40% B:1.39%) 31.47% (G: 6.66% B:0.27%) 3. 13/7 13/11* eq: +0.797 (-0.105) 67.81% (G:30.38% B:1.57%) 32.19% (G: 7.55% B:0.41%) 4. 13/11* 8/2 eq: +0.523 (-0.380) 60.85% (G:25.76% B:1.04%) 39.15% (G:10.53% B:0.72%) 5. 13/5 eq: +0.377 (-0.526) 58.53% (G:22.58% B:0.57%) 41.47% (G: 9.44% B:0.29%)


Utilizing virtualenv¶
Different projects, applications, or scripts may require different versions of Python (e.g. 2.6 vs 2.7 vs 3.X) and can have different sets of dependencies. Virtual environments are a great way of creating isolated spaces for compartmentalizing specific Python versions and/or sets of package dependencies for separate projects and applications.
This additionally prevents the global Python installs from becoming polluted with a plethora of packages and incompatibilities between applications which may require different versions of the same dependency that may lead to conflicts or difficult-to-debug errors. It also avoids typical permissions errors when managing global packages and makes removing a set of application dependencies as simple as deleting the virtualenv directory.
NOTE: While the prerequisites recommend the latest Python 2 and Python 3 installs, if there is a situation where you need a very specific version of Python, as long as that version is installed and you know the path to the Python executable then you can create a virtual environment for that version.
Prerequisites¶
First things first, make sure that you have the latest version of Python 2 or Python 3 downloaded and installed (or just skip the manual download by installing via the appropriate package manager for your OS)
Next you’ll need to make sure that pip is installed (it comes automatically with Python 2 >= 2.7.9 and Python 3 >= 3.4)
Ensure that pip and setuptools are updated: pip install -U pip setuptools
Setup¶
Next we’ll need to install the virtual environment packages:
pip install -U virtualenvwrapper virtualenv
WINDOWS: if you are doing this on a windows box you will instead need to install virtualenvwrapper-win and follow the directions here (good luck!):
pip install -U virtualenvwrapper-win virtualenv
While virtualenv can be used by itself, it is far easier to use the wrapper package which makes creating, managing, and activating virtual environments a far more pleasant experience.
Configuration¶
WINDOWS: again, if you are doing this on a windows box, you should refer to these directions since virtualenvwrapper, and hence this whole section, is only for Bourne-compatible shells (have fun!)
Now that everything is installed, we need to configure our environment to
actually utilize it. The following needs to be added somewhere in your
~/.bash_profile
or your shell equivalent file:
#######################################################
### virtualenv setup and shortcuts
export PIP_REQUIRE_VIRTUALENV=true
export WORKON_HOME="~/.envs"
if [ -f /usr/local/bin/virtualenvwrapper.sh ]; then
source /usr/local/bin/virtualenvwrapper.sh;
fi
alias mkvenv="mkvirtualenv"
alias mkvenv3="mkvirtualenv -p python3"
alias rmvenv="rmvirtualenv"
alias lsvenv="lsvirtualenv"
Broken down, the export PIP_REQUIRE_VIRTUALENV=true actually prevents Python packages from being installed globally unless a specific flag is passed to pip to force it install anyway. What this does is it reminds you if you forget to activate a virtual env, and prevents packages from being installed globally unless you specifically go out of your way to do so.
The export WORKON_HOME="~/.envs"
tells virtualenvwrapper the location where
you want it to create all of your virtual environments (this should NOT be
within a git repository as the env should NOT be committed with code). Modify
this export as needed for wherever you want it to use as the location.
The if statement is actually the most important part of the additions since it is what actually initializes the wrapper whenever you start a shell session so it can do its job. Without the wrapper script getting sourced, it will not work correctly!
The remainder of the additions are aliases which I like to use, however they are not strictly required if you do not wish to add them.
Managing envs¶
Basic usage for creating a virtual env is:
mkvirtualenv <env-name>
For example:
mkvirtualenv example
Or using the alias:
mkvenv example
You can similarly list or remove environments using the aliases:
lsvenv
rmvenv example
Creating a Python 3 env¶
The alias from above can be used to quickly create a virtual env with the system install version of Python 3:
mkvenv3 example_env
Or using just virtualenvwrapper without the alias:
mkvirtualenv -p python3 example_env
Using specific Python versions¶
To use a specific version of Python in a virtual env, you need to explicitly tell it which one via the -p flag:
mkvirtualenv -p path/to/specific/version/of/python
mkvenv -p path/to/specific/version/of/python
Activating/deactivating an env¶
When creating a virtual env, it will automatically be activated, however, to activate an env that is not currently active, you use the workon command:
workon example_env
Activating a virtual env gives you access to the installed packages in the env and the normal Python executable found on the command line should be the executable for the version of Python specified when the env was created (the default Python install on the system if none was specified). Deactivating an env
Leaving an env is also very simple:
deactivate


+Val
(gui '(+Val +NumField) '((N) (* N N)) 10)
See also +Set, +Fmt, Formatting.
+Var
"Occupation" (gui 'job '(+Var +TextField) '*PrsJob 20)
This example links a text field to the variable *PrsJob. See also +E/R.
+View
+Lock
, view is a prefix class that disables a GUI
component. However, +View
can take a functional argument:
(gui '(+Sgn +View +FixField) '(sum> (: home obj)) 2 12) ) )
This example is a snippet from the demo app included with the PicoLisp
distribution, in ord.l. The code produces a disabled text field that
displays the total order sum of the current order object. See also +Lock, +Upd, +Init.


Notifications
Paul
2 days ago
Hey, do you think we will need a video projector for the next meeting ?
Jorgo
3 minutes ago
Are you up for a coffee-break ?
Paul
2 days ago
Hey, do you think we will need a video projector for the next meeting ?
Jorgo
3 minutes ago
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Polar Bear
<EMAIL_ADDRESS>Current status
Mood
Stories
Bio
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Edit profile


It's time for another installment of React patch releases! We didn't break anything in v0.14.2 but we do have a
couple of other bugs we're fixing. The biggest change in this release is actually an addition of a new built file.
We heard from a number of people that they still need the ability to use React to render to a string on the client.
While the use cases are not common and there are other ways to achieve this, we decided that it's still valuable to
support. So we're now building react-dom-server.js
, which will be shipped to Bower and in the
dist/
directory of the react-dom
package on npm. This file works the same way as
react-dom.js
and therefore requires that the primary React build has already been included on the page.
The release is now available for download:
We've also published version 0.14.3
of the react
, react-dom
, and addons
packages on npm and the react
package on bower.
nonce
attribute for <script>
and <style>
elementsreversed
attribute for <ol>
elementsreact-dom-server.js
to expose renderToString
and renderToStaticMarkup
for usage in the browser


TeachOSM Contributors
Desktop
University
Beginner
1 hour
2 - 4 hours
This module organizes basic OSM contributing resources for teachers to introduce to students to initialize the concept of students making contributions to the OSM project. In this module, students will be fully prepared to contribute to the OSM project. Students will familiarize themselves with these mapping-ready resources and answer open-ended questions on important points of the three resources.


WATCH: Colour-blind boy's heartwarming reaction to seeing colour for the first time 25 Nov 2019, 09:14 by Algoa FM


Verify that a single line of red text below is shown. Click
the button below, and then verify that the line of text is
moved to the interior of the popup. Click the popup, and
verify that the text moves back to its original location.
Content is outside of pop-up window
Content is inside of pop-up window


| Place | Team | Average Score | Average Beacons | Average Resources | Average Deaths | Average Kills | Average Assists | Average Cores |
|---|---|---|---|---|---|---|---|---|
| 1 | Amy's Improved Through AStar Flag Collector | 10.8 | 22.6 | 16870.0 | 15.20 | 0.00 | 0.00 | 5.00 |
| 2 | Aggressive Flag Collector 3 ships | 2.8 | 7.0 | 1724.0 | 27.80 | 8.20 | 5.80 | 0.80 |


cpgf library memory management guideline
The design goal of cpgf library memory management is to ease memory management and avoid memory leak.
There is slight different rule between class and function based API (classes name starts with G) and interface based API (classes name starts with I).
Note here is only discussing the object that allocated on the heap, not any auto object that allocated on the stack.
So the object dicussed here is passing by pointer, not by reference or by value.
Memory management rules in class and function based API
Rule 1, every object has explicit, one, and only one owner.
Rule 2, the owner must free the object when the object is not used any more.
Rule 3, the owner can transfer the ownership to another, then the original owner loses the ownership and won't free the object.
cpgf library uses different function name prefix to distinguish the ownership.
A function name prefixes with "create", or "gain", will transfer the ownership to the caller.
So if you are invoking createScriptObject (in class GScriptObject and IScriptObject), you must free the returned object when it's not used.
A function name prefixes with "get", or any other non-"create" and non-"gain", will hold the ownership.
So if you are calling any getMetaXXX functions in GMetaClass, you must NOT free the returned object.
To be exception safe, a scoped smart pointer, the one in cpgf called GScopedPointer, is your friend.
Memory management rules in interface based API
Rule 1, every object uses reference count to manage the object life cycle.
Rule 2, there is no explicit owner for any object.
Rule 3, for any object returned by any API, after it is not used, releaseReference of the object must be called.
So no matter what's the functions named, releaseReference must be called.
To be exception safe, a scoped smart pointer GScopedInterface is provided.


Education
- Artificial and Natural Intelligence Toulouse Institute (ANITI), France and Brown University, USA
- Topic: Exploring the role of (self-)attention in cognitive and computer vision architectures
- Supervisor: Thomas Serre (Professor, Brown University, USA)
- Year: 2019-2023
Erasmus Joint Masters In Vision and Robotics (VIBOT)
- Joint Degree In Association with Heriot Watt University, UK ,University of Bourgogne, France, University of Girona, Spain
- Thesis: MU-Net: A deep learning model for teeth segmentation from panoramic X-ray images
- Supervisor: Hugues Talbot (Professor, CentraleSupelec, France)
- Year: 2017-2019
Bachelor of Technology (Hons.)
- LNM Institute of Information Technology, India
- Major: Electronics and Communication Engineering
- Thesis: Residue coding technique for video compression
- Supervisor: Anil Kumar Tiwari (Assoc. Professor, Indian Institute of Technology, Jodhpur, India)
- Year: 2009-2013


Heart Health
78 Articles
The story of fish oil and heart health started with the observation that several populations ...
Delia Freeman
21 Feb 2019
If you're like most people, you probably think it's high-cholesterol foods like eggs or shrimp that are the worst for your cholesterol levels. But that's not really the case.
Delia Freeman
21 Feb 2019


Brady and Manning
All sports have great rivalries. Currently in the NFL, two elite quarterbacks and two of the best men to ever play the game are best friends and yet worst enemies. They play each other nearly every year and it wasn’t until many years that Manning’s Colts were able to come out on top over Brady’s Patriots. The rivalry between the two has helped them become better players. Because of this, some wonder if they would be as elite had it not been for one another.
The Beginning
Manning
Peyton Manning’s story starts with his father and then the University of Tennessee. Archie Manning was drafted by the Saints in 1971 and stuck around for ten years. At this time, however, the Saints’ fans wore paper bags on their heads and referred to their team as “the Aints”. Peyton and Cooper would attend the games with their mother until it became too much of a strain for her while pregnant with Eli.
Peyton eventually became heavily interested in the sport. He and older brother Cooper played in the yard with their dad. Soon, the Manning brothers owned their high school. During Cooper’s senior year as a wide receiver, Peyton became starting quarterback. Just as Cooper is getting ready to go to Ole Miss, he’s diagnosed with spinal stenosis and his football career is over. To this day Peyton honors him by wearing his number: 18.
In the 1997 Rose Bowl against the UCLA Bruins it was Manning’s first time going into a huddle as the Tennessee QB. They were down by 21 so he tries to pump up his teammates with a pep talk.
From then on Peyton Manning started every game until eleven years into his NFL career. He was eventually drafted in 1998 by the Indianapolis Colts and played his first professional game against his biggest idol, Dan Marino. The Miami Dolphins came out on top defeating The Colts 24–15 with Marino passing for 135 yards and Karim Abdul-Jabbar rushing for 108.
Brady
Tom Brady struggled at Michigan. Head coach, Lloyd Carr, didn’t make it easy for him to play QB. Game after game went on until he decided to do something about it and confront his coach. He told Carr he may transfer because he doesn’t see himself being able to get on the field and feels it’s unfair.
Since this, Brady walked in to practice every day with his A-game, got up early to train, and did his best to be the best — just like he still does walking into practice on the Patriots. Carr said Brady enjoyed the struggle. It changed him and helped him be the competitive player he is today.
To toy with his players even more, Carr eventually had a plan to decide who would be the starting quarterback. He’d put Brady in one quarter, Henson in the second quarter, and decided from there who gets the play the second half. Tom won some and lost some. Tom’s father was upset at the lack of play-time his son had at Michigan.
Brady got his start in the NFL once the Pats lost both games opening the season to both the Bengles and the Jets. Drew Bledsoe was out due to injury and Brady was in. Tom was a sixth-round pick and already knew what it was like not being on the field from Michigan. With what he learned there, he eventually came out on top and started on the Patriots under Bill Belichick.
Nearly Jets
Ryan Leaf was one of the most impressive quarterbacks coming out of college in time for the 1998 draft. He nearly won the Heisman Trophy coming third behind Peyton and Charles Woodson who won it. Meanwhile, the Colts were deciding between Leaf and Manning for their QB. Both were good — Leaf was probably better. But Manning was a safer choice and more of a franchise quarterback. Bill Polian decides on Manning while Leaf is picked up by the San Diego Chargers. Leaf soon shows bad behavior, he’s not performing well, and after an injury he’s history. Leaf is widely referred to as the biggest draft bust in NFL history.
“It was one of the best decisions I ever made. I got to be a senior in college. It’s not a year you can ever get back. There was no guarantee the Jets were even going to draft me.”
Only a short while before this and before Manning really finished college, the NY Jets showed interest in drafting him while he was still at Tennessee. The NFL doesn’t like these types of deals and discourages it, but nothing about it was against the rules. He did graduate, but with a year of eligibility left to play. Manning had a decision to make, he could’ve played with the Jets for the next fifteen years but he also wanted to play as a senior.
Only a select few players get invited to attend the draft. If you’re guaranteed a spot, you have to be there for pictures and such. Manning was at his draft. Brady was sitting on his couch watching it on TV with his family. Rounds went by and Brady wasn’t mentioned once. Finally in the sixth round, the Patriots selected Brady with the 199th overall pick. This was also the draft where Sebastian Janikowski became the first placekicker since 1966 to be selected in the first round.
The Jets had a better chance picking up a future elite QB in 2000 when Bill Parcells was told Brady would be a wise decision for their sixth round pick. Tony Scott, North Carolina safety, ended up being drafted and played for only 23 games.
Stats and Rings
Playing the numbers game is difficult with these two and some would argue it’s not even fair. The best quarterback can mean different things to different people. In 2004, Manning broke Dan Marino’s record of most touchdown passes in a single season at 49. Come the final game of the 2007 season against the Giants, Brady connected with Randy Moss twice to beat Peyton’s record at 50 while Moss broke Jerry Rice’s record of most receiving touchdowns in a season at 23.
Peyton had called Eli before this game asking, if possible, to keep Brady from passing any TDs so he can hold his record for more than just a few years. His gift to the Giant’s defense would have been a night out at the famous New York City steakhouse, Del Frisco’s. In 2013, now on the Broncos, Manning was able to beat Brady’s record and be sure he kept it with 55 touchdowns in a single season. Recently, Manning also took the lead for most career touchdowns. Keep in mind, he has two years in the NFL more than Brady.
Brady has taken the Patriots to the Super Bowl six times, winning four and being named the MVP three times. This is impressive, especially compared to Manning, who has only been to the Super Bowl three times winning just once.
Surrounding Talent
Had Brady been picked up by the Jets or any other team he would not be the Tom Brady we know today. Belichick plus Brady is simply one of the greatest duos in NFL history. The Patriots have also had great teams each time they went to the Super Bowl, but one thing to keep in mind is this is an elite quarterback. The idea that Brady and Belichick with any other team winning is not far fetched.
Manning is one of three quarterbacks to take two teams to the Super Bowl. An example of his best game and one of the greatest comebacks ever is the 2006 season AFC championship against the Patriots, of course. Down 18 points at 21–3, Manning would have to lead the biggest comeback in conference championship history. They managed to tie it multiple times until finally taking the lead with seconds left on the clock. Indianapolis went on to beat the Bears in the Super Bowl two weeks later.
The Book
Brady vs. Manning was an enjoyable read. Gary Myers does a great job keeping you on the edge of your seat reading about a game that happened nearly a decade ago and that you know the outcome of. There is a lot to be learned about the sport of football and the politics of the NFL. I think it was all laid out in such a way that was coherent enough for even someone who doesn’t know much about the sport to enjoy.
There’re a lot of details to every story. Some things are more relevant than others and the lives of Brady & Manning had a lot of fun facts. For example:
- Brady and Gisele sold their house to Dr. Dre for $40 million in 2014 at 4x their original investment.
- Mark Sanchez was the first QB to beat Manning and Brady back-to-back in the playoffs in 2010. And finally:
- Brady has 11 wins while Manning has 5 against each other. Brady has 32 TDs and 14 INTs against Manning while he has 33 TDs and 22 INTs. They’re tied at two playoff wins each as of January 23 2016.
There is a lot more to the Brady-Manning rivalry. The text gives some deep insights into what makes each player better and worse. One thing that deserves its own post is Spygate and Deflategate. Though the scandals accusing the Patriots of cheating might be more of an internal problem, it changes the outcome of a lot and some of it might surprise you.


HttpMethod Uses EnumTrait
HTTP methods listd in "4.3. Method Definitions" in RFC 7231 (HTTP/1.1).
Tags
Table of Contents
- $CONNECT : HttpMethod
- CONNECT.
- $DELETE : HttpMethod
- DELETE.
- $GET : HttpMethod
- GET.
- $HEAD : HttpMethod
- HEAD.
- $OPTIONS : HttpMethod
- OPTIONS.
- $POST : HttpMethod
- POST.
- $PUT : HttpMethod
- PUT.
- $TRACE : HttpMethod
- TRACE.
- __toString() : string
- Get the name of this instance.
- name() : string
- Get the name of this instance.
- valueOf() : static
- Get an instance of this class that the given argument represents.
- values() : array<string|int, mixed>
- Get the list of public class variables listed in this class.
Properties
$CONNECT
CONNECT.
public
static HttpMethod
$CONNECT
Tags
$DELETE
DELETE.
public
static HttpMethod
$DELETE
Tags
$GET
GET.
public
static HttpMethod
$GET
Tags
$HEAD
HEAD.
public
static HttpMethod
$HEAD
Tags
$OPTIONS
OPTIONS.
public
static HttpMethod
$OPTIONS
Tags
$POST
POST.
public
static HttpMethod
$POST
Tags
$PUT
PUT.
public
static HttpMethod
$PUT
Tags
$TRACE
TRACE.
public
static HttpMethod
$TRACE
Tags
Methods
__toString()
Get the name of this instance.
public
__toString() : string
Return values
string —The name of this instance.
name()
Get the name of this instance.
public
name() : string
Return values
string —The name of this instance.
valueOf()
Get an instance of this class that the given argument represents.
public
static valueOf(mixed $value) : static
If the given argument is an instance of this class, the instance itself is returned.
Otherwise, if the given argument is null
, null
is returned.
Otherwise, if the type of the given argument is not string
,
an InvalidArgumentException
is returned.
Otherwise, a class variable whose name is equal to the given
argument is looked up. If found, the instance is returned.
If not found, an InvalidArgumentException
is thrown.
Parameters
- $value : mixed
-
A string that represents an instance of this class, or an instance of this class, or
null
.
Return values
static —An instance of this class.
values()
Get the list of public class variables listed in this class.
public
static values() : array<string|int, mixed>
Return values
array<string|int, mixed> —Instances of this class which are defined as public class variables.


Here you can fins friends or stalk strangers.
The Doge Social Network does not take any responsibility for the information found


Tier1DB — Tier 1 molecular line database services.¶
This module is used to interact with the Tier 1 database.
-
class
admit.util.Tier1DB.
Tier1DB
[source]¶ Class for interacting with the Tier 1 database. Methods are supplied to query the database and get restults of the query. See the DOCUMENTATION on the Tier 1 database for specifics.
Parameters: None Attributes
conn (sql connection) The main sql database connection. cursor (sql cursor) The cursor used to interact with the database. Methods
add
(string)Method to append to the query string, automatically adding where/and if necessary. close
()Method to cleanly close the database connection get
(num)Method to get many results from the query getall
()Method to get all results from the query getone
()Method to get the next result from the query query
(querystring)Method to execute the requested query against the transitions database searchhfs
(hfsid)Method to search the HFS table for the requested transitions searchtransitions
([freq, eu, el, linestr, ...])Method to construct the query and send it to the database. -
add
(string)[source]¶ Method to append to the query string, automatically adding where/and if necessary.
Parameters: string : str
The string to add to the query
Returns: None
-
get
(num)[source]¶ Method to get many results from the query
Parameters: num : int
The number of results to get
Returns: A tuple containing the requested results as a list
-
getall
()[source]¶ Method to get all results from the query
Parameters: None Returns: List of LineData objects, one for each transition
-
getone
()[source]¶ Method to get the next result from the query
Parameters: None Returns: LineData object containing the transition data
-
query
(querystring)[source]¶ Method to execute the requested query against the transitions database
Parameters: query : str
The query to perform, no error checking is done
Returns: None
-
searchhfs
(hfsid)[source]¶ Method to search the HFS table for the requested transitions
Parameters: hfsid : int
The id of the HFS line to get
Returns: None
-
searchtransitions
(freq=[], eu=[], el=[], linestr=[], species=None)[source]¶ Method to construct the query and send it to the database.
Parameters: freq : list or float
The frequency range to search over. If a list is given it is treated as [min,max]. If a single float is given then it is treated as a maximum allowable frequency. The units are GHz.
eu : list or float
The upper energy range to search over. If a list is given it is treated as [min,max]. If a single float is given then it is treated as a maximum allowable upper state energy.
el : list or float
The lower energy range to search over. If a list is given it is treated as [min,max]. If a single float is given then it is treated as a maximum allowable lower state energy.
linestr : list or float
The line strength range to search over. If a list is given it is treated as [min,max]. If a single float is given then it is treated as a maximum allowable line strength.
species : str
The species to restrict the search to. It is executed as an sql “like” statement, and thus an exact match is not required.
-


instanceof
expression is used for testing the type of a parameter in a catch
block.
Testing the type of catch
parameters is usually better done by having separate
catch
blocks instead of using instanceof
.
Example:
void foo(Runnable runnable) {
try {
runnable.run();
} catch (Throwable throwable) {
if (throwable instanceof NoClassDefFoundError) { // warning: 'instanceof' on 'catch' parameter 'throwable'
System.out.println("Class not found!");
}
}
}


QuSpin 0.3.7 (released on 2023.01.01)¶
Highlights: Lanczos module now here; Constrained Hilbert spaces support available; OpenMP support available!¶
Check out Parallel Computing Support and the example script Parallel Computing in QuSpin.
For a tutorial in QuSpin’s user_basis which allows the user to define custom bases with constraints, check out: A tutorial on QuSpin’s user_basis.
Complete list of the most recent features¶
Added in v. 0.3.7 (2023.01.01)¶
Improved Functionality¶
added support for macbook ARM64 processors.
added python 3.10 support.
added note to warning to notify users that hermiticity/symmetry checks are not exhaustive.
added small fixes to Optical Bloch equations: Lindblad dynamics using the matvec function.
fixed bug with automatic symmetry checks for *_basis_1d.
fixed bug with data types in quantum_operator for systems of N>31 sites.
fixed bug with using Nup argument of *_spin_basis_*.
fixed bug with return_rdm optional argument of tensor_basis.
fixed bug with Floquet tool returning the transposed Floquet evolution operator for complex-valued Floquet Hamiltonians.
updated tests removing deprecation warnings and errors.
New Attributes, Functions, Methods, Classes, and Examples¶
new Example Liouville-von Neumann Equation using the MKL-enhanced Sparse Matrix Product [courtesy of J. Verlage] shows how to solve the Liouville-von Neumann equation using sparse matrices.
new Example Kitaev’s honeycomb Hamiltonian in vortex sectors given by the plaquette operators shows how to define symmetries using the user_basis that cannot be defined using the basis_general classes.
Added in v. 0.3.6 (2021.05.01)¶
Improved Functionality¶
adding python 3.9 support.
fixed a bug with non-contiguous subsystems in basis.partial_trace() and basis.ent_entropy() for the fermionic basis clases *_fermion_basis_*.
fixed a bug with defining mixed particle sectors in *_fermion_basis_general.
fixed some typos in the documentation.
New Attributes, Functions, Methods, Classes, and Examples¶
added optional argument noncommuting_bits to user_basis to specify the bits that represent a fermion degree of freedom.
Added in v. 0.3.5 (2021.02.15)¶
Improved Functionality¶
adding python 3.8 support
adding quspin application to test quspin on hpc clusters.
added 2d array/batch support to tools.evolution.expm_multiply_parallel.
fixed errors caused by new releases of some of the dependencies.
- fixed bugs with:
particle-hole symmetry in *_fermion_basis_general;
memory leakage in tools.lanczos;
the reduce_output argument of basis_general.Op_bra_ket().
New Attributes, Functions, Methods, Classes, and Examples¶
deprecated function tools.misc.csr_matvec.
added Majorana fermion operator strings to the *_fermion_basis_general.
added int_to_state and state_to_int functions to spinful_fermion_basis_* classes.
added properties shape and ndim to classes in the operator module.
- new examples:
Majorana fermion operators, cf. Gell-Mann Operators for Spin-1 systems;
Gell-Mann operators for spin-1 systems, cf. Majorana Fermions: Spinless Fermi-Hubbard Model;
Majorana SYK model, cf. Majorana Fermions: SYK Model.
Calculation of spectral functions using symmetries, cf. Direct Calculation of spectral functions using symmetries.
Tutorial on using QuSpin basis objects, cf. Basics of QuSpin basis objects.
Added in v. 0.3.4 (2020.04.17)¶
Improved Functionality¶
discontinued support for python 2.7. Installing QuSpin for py27 will by defult result in version 0.3.3.
deprecated function basis.get_vec(): use basis.project_from() instead.
revised user_basis tutorial for spinless fermions and introduced function _count_particles_32().
added optional arguments svd_solver, svd_kwargs to basis.ent_entropy(); allows to use some scipy svd solvers, which are typically more stable.
expm_multiply_parallel now supports the option to give the operator an explicit dtype, see example Efficient time evolution using the omp-parallelized expm_multiply_parallel.
- fixed bugs:
computing the entanglement entropy when using the spinful_fermion_basis_general.
constructing operators for higher-spin operators (S>1/2).
New Attributes, Functions, Methods and Classes¶
new *_basis_general functions – basis.project_from() and its inverse basis.project_to() – to transform states between a symmetry-reduced basis and the full basis.
new tools.Lanczos module for Lanczos type calculations, see examples Lanczos module: time-evolution and ground state search, Lanczos module: finite-temperature calculations.
new function method Op_shift_sector of the *basis_general_ classes allows to apply operators, which do not preserve the symmetry sector, to quantum states in the reduced basis. Useful for computing correlation functions. See example Autocorrelation functions using symmetries.
new required support package for QuSpin: numexpr.
Added in v. 0.3.3 (2019.10.15)¶
Improved Functionality¶
introducing improvements to Example Monte Carlo Sampling Expectation Values of Obsevables to perform Monte Carlo sampling in the symmetry-reduced Hilbert space.
- new examples:
Example Fermi-Hubbard model without doubly-occupied sites to showcase double_occupancy option of the spinful_fermion_basis_*.
Examples Quantum scars Hamiltonian: spin-1/2 system with Hilbert space constraint, Spin-1/2 system with sublattice particle consevation, Applying symmetries to reduce user-imported bases using QuSpin demonstrate the usage of user_basis.
Example Optical Bloch equations: Lindblad dynamics using the matvec function shows how to use QuSpin for Lindblad dynamics and demonstrates the use of the omp-parallelzied matvec function for speedup.
Example Hexagonal Lattice: Fermi-Hubbard model [courtesy of A. Buyskikh] shows how to construct Hamiltinians on a hexagonal lattice.
improved functionality of the tools.evolution.evolve() function.
fixed import issue with scipy’s comb function.
fixed a number of small bugs.
New Attributes, Functions, Methods and Classes¶
adding *_basis_general.get_amp() function method which effectively provides a partial get_vec() function but does not require the basis to be constructed ahead of time.
adding optional argument double_occupancy to the spinful_fermion_basis_* classes to control whether doubly occupied sites should be part of the basis or not.
adding the user_basis class which enables the user to build in Hilbert-space constraints, and exposes the inner workings of QuSpin’s core function to give the user almost complete control (see A tutorial on QuSpin’s user_basis).
adding tools.misc.matvec() and tools.misc.get_matvec() functions with omp-parallelized implementation which outperforms scipy and numpy in computing matrix-vector peroducts.
adding optional arguments to the dot() and rdot() functions of the operators module.
Added in v. 0.3.2 (2019.03.11)¶
Improved Functionality¶
improved performance for matrix vector product in _oputils and expm_multiply_parallel. Leads to significant speedup in the hamiltonian and quantum_operator classes (e.g. in the hamiltonian.evolve() function) and the tools.evolution.expm_multiply_parallel() function.
Added in v. 0.3.1 (2019.03.08)¶
Improved Functionality¶
support for python 3.7.
discontinued support for python 3.5 on all platforms and python 2.7 on windows. QuSpin for these versions will remain available to download up to and including QuSpin 0.3.0, but they are no longer being maintained.
matplotlib is no longer a required package to install quspin. It is still required to run the examples, though.
parallelization: New parallel features added or improved + OpenMP support for osx. Requires a different build of QuSpin (see also Parallel Computing Support).
new OpenMP features in operators module (see Parallel Computing Support and example script Parallel Computing in QuSpin).
improved OpenMP features in the *_general_basis classes.
new example scripts: (i) use of some new *_basis_general methods, (ii) use of OpenMP and QuSpin’s parallel features.
faster implementation of spin-1/2 and hard-core bosons in the general basis classes.
more memory efficient versions of matrix-vector/matrix products implemented for both hamiltonian and quantum_operator classes. Allows using OpenMP in the hamiltonian.evolve() function method.
refactored code for *_general_basis classes.
large integer support for *_general_basis classes allows to build lattices with more than 64 sites.
New Attributes, Functions, Methods and Classes¶
new argument make_basis for *_basis_general classes allows to use some of the basis functionality without constructing the basis.
new *_basis_general class methods: Op_bra_ket(), representative(), normalization(), inplace_Op().
support for Quantum Computing definition of “+”, “-“ Pauli matrices: see pauli argument of the spin_basis_* classes.
adding argument p_con to *_basis_general.get_vec() and *_basis_general.get_proj() functions.
adding functions basis.int_to_state() and basis.state_to_int() to convert between spin and integer representation of the states.
new basis.states attribute to show the list of basis states in their integer representation.
new methods of the *_basis_general classes for bitwise operations on basis states stored in integer representation.
both hamiltonian and quantum_operator classes support a new out argument for dot and rdot which allows the user to specify an output array for the result.
both hamiltonian and quantum_operator classes support a new overwrite_out argument which allows the user to toggle between overwriting the data within out or adding the result to out inplace without allocating extra data.


Pizza sauce
- ⏲ Prep time: 10 min
- 🍳 Cook time: 0.5-2 hours
Ingredients
- 400g canned tomatoes, preferebly skinned and whole
- 150g tomato puré
- 1 medium sized onion
- olive oil
- garlic
- oregano
- chili pepper (optional)
Directions
- Dice onion and fry in olive oil on medium heat.
- Add chopped garlic and chili pepper (optional) for some minutes.
- Mix in tomato puré, salt, pepper, and oregano. Cook until puré gets oily.
- Add canned tomatoes, and crush with spatula.
- Let it sit on low heat. The longer the better.
Contribution
- brox - xmr:
8AaUvC4JPfTVgy66FLd8mVfsPwT7RXJuPK2xySoKnFEhDZPNafpgn6rh8VPBVauyNP17EQQ1HGhMCZdtUJNoD7xnVt1cSLo
Recipe tags:
basic
pizza
sauce


Step 1.
Go toDashboard » Page » Add New
.
Dashboard » Page » Add New
.
Uix Page Builder Attributes
settings in a meta box in your WordPress backend when you create a new page or when you are editing an existing one. Click the "Use Visual Builder" button.
Dashboard » Settings » Reading » Front page displays
. Select A Static Page option.
Home Slideshow Full Header
.
Dashboard » Appearance » Customize
in your Dashboard. If the "Homepage Logo" exists, this Logo specified on your current homepage only.


NCI invites users to test drive new supercomputer
In preparation for January launch.
by Matt JohnstonThe National Computational Infrastructure’s (NCI) newest supercomputer, Gadi, is now available for users to test out ahead of its official launch early next year.
Gadi has been under construction since August at the Australian National University after it was revealed Fujitsu had won the $70 million contract to build the machine.
The NCI said it is excited for users to test out the new machine and find out what it can do for them as part of an initial transition phase, during which time its existing supercomputer, Raijin, will still be available.
While the NCI initially said Gadi would become Australia’s fastest supercomputer when built with around 10 times the processing capability as Raijin, the organisation has since stated that “Gadi will offer a 5-10 times increase in computational capacity from the original Raijin install in 2012”.
On Monday, NCI began copying Raijin home directories to Gadi.
From then, users will have access to 18 racks of Cascade Lade CPUs from Intel and 96 Nvidia V100 GPUs, covering all of the existing capacity on Raijin.
However, more than half of the CPU nodes and “the large majority” of the GPU nodes are still to come, with installation to be completed in the coming months.
Meanwhile, 50 percent of Raijin’s Intel Sandy Bridge nodes are set to be decommissioned on 27 November to allow power work for Gadi to be completed.
Along with advice on how users can prepare for the transition from Raijin, NCI said that Gadi will fully launch on 6 January, with its predecessor being fully decommissioned on 20 January.
When the Raijin rebuild was announced in late 2017, it was expected that the new capacity would place it among the world's top 25 most powerful computers. Raijin was last ranked at 173rd place.


prtClassRvmFigueiredo Relevance vector machine classifier using a Jefferey's prior
CLASSIFIER = prtClassRvmFigueiredo returns a relevance vector
machine classifier.
CLASSIFIER = prtClassRvmFigueiredo(PROPERTY1, VALUE1, ...) constructs a
prtClassRvm object CLASSIFIER with properties as specified by
PROPERTY/VALUE pairs.
A prtClassRvmFigueiredo object inherits all properties from the
abstract class prtClass. In addition is has the following
properties:
kernels - A cell array of prtKernel objects specifying
the kernels to use
verbosePlot - Flag indicating whether or not to plot during
training
verboseText - Flag indicating whether or not to output
verbose updates during training
learningMaxIterations - The maximum number of iterations
A prtClassRvmFigeiredo also has the following read-only properties:
learningConverged - Flag indicating if the training converged
beta - The regression weights, estimated during training
sparseBeta - The sparse regression weights, estimated during
training
sparseKernels - The sparse regression kernels, estimated during
training
For more informatoin on the Figueiredo algorithm, please refer to
the following reference:
M. Figueiredo, Adaptive sparseness for supervised learning,
IEEE PAMI, vol. 25, no. 9 pp.1150-1159, September 2003.
Training using the Figueiredo algorithm can provide faster
and more robust convergence under some circumstances.
A prtClassRvm object inherits the TRAIN, RUN, CROSSVALIDATE and
KFOLDS methods from prtAction. It also inherits the PLOT method
from prtClass.
% Example
TestDataSet = prtDataGenUnimodal; % Create some test and
TrainingDataSet = prtDataGenUnimodal; % training data
% % Create a classifier with verbose plotting
classifier = prtClassRvmFigueiredo('verbosePlot',true);
classifier = classifier.train(TrainingDataSet); % Train
classified = run(classifier, TestDataSet); % Test
% Plot the results
subplot(2,1,1);
classifier.plot;
subplot(2,1,2);
% figure
[pf,pd] = prtScoreRoc(classified,TestDataSet);
h = plot(pf,pd,'linewidth',3);
title('ROC'); xlabel('Pf'); ylabel('Pd');


TaoStats
0000
Last Block
0000
Current Epoch
00.00%
Valdiator ROI
0
Nodes Reporting
Last Blocks
Block Hash
Size (in TX)
Currently Reporting Nodes
Name
Node Type
Country
Peers
Latency
TX Pending
Height


After creating a mechanic for meerkats to dig tunnels in an earlier release, the team wanted to expand on the concept of animals going underground. This was realised in the form of burrows, a place for certain animals to rest, give birth, and hide from adverse weather. Here's some of the details of my implementation.
Animals can move around in the burrow and perform many behaviours, leaving once they need something above ground, or get restless. Newborns will wait for the mother to leave the burrow, before all following her out. Burrow entrances can be placed anywhere in the habitat with enough space, with the underground chamber automatically positioning itself in a valid location if possible.
All Animal AI states have modified behaviour when inside a burrow.
I avoided the need for new animations, making the mechanic easily extendable
Navigation was handled by preexisting, tested solutions, thanks to my code and discussions with our
modelling team about including a preset navigation mesh in the burrow interior. By also discussing the
entrance geometry in-depth, we also eliminated the need for any specific burrow animations, teleporting
the animal from the entrance to the burrow interior, disabling any problematic physics and IK along the way.
I also worked on our camera code to allow players to see into their burrows via an in-universe webcam,
which they could cast onto billboards they placed throughout their park. The camera came with the stretch
goal of a small investigation behaviour, for our more curious species. This goal was met, alongside all
the other stretch goals for this feature.
Considering how the final product turned out, I see a lot of potential in a system that was more generalised. I planned out an "underground area animals can go to sleep/shelter/give birth" when I could've planned a "____ area animals can go to ____", something more extendable for the future. We haven't needed a similar system since, but a more generic system always makes it easier for designers to suggest ways to use it.
The investigation behaviour in action, from the view of the camera.


{% extends "base.html" %}
{% block content %}
New Feature Request Form
Title
*
Description
Client
*
Client Priority
*
Target Date
Product Area
*
Submit
{% endblock %}


The repetition/auto/for_aux.h
header defines a predicated repetition algorithm.
#include <chaos/preprocessor/repetition/auto/for_aux.h>
The CHAOS_PP_AUTO_FOR_AUX
macro is a predicated repetition construct.
CHAOS_PP_AUTO_FOR_AUX
(pred, op, macro, data, ...)
The predicate that determines whether the algorithm should continue. This predicate must be either a λ expression or a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
pred(s, data†, state‡)
† If the auxiliary data is parenthetic, the data pseudo-parameter must be the number of parameters that corresponds to the number of sub-elements in the auxiliary data. Otherwise, it must be a single parameter.
‡
The state pseudo-parameter must be either ...
or the number of parameters that corresponds to the number of arguments in the initial state, __VA_ARGS__
.
The operation used to iterate the state. This operation must be either a λ expression or a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
op(s, data†, state‡)
† If the auxiliary data is parenthetic, the data pseudo-parameter must be the number of parameters that corresponds to the number of sub-elements in the auxiliary data. Otherwise, it must be a single parameter.
‡
The state pseudo-parameter must be either ...
or the number of parameters that corresponds to the number of arguments in the initial state, __VA_ARGS__
.
The target to be repeated. This target must be either a λ expression or a macro name that refers to a macro with a signature compatible with the following pseudo-signature:
macro(s, data†, state‡)
† If the auxiliary data is parenthetic, the data pseudo-parameter must be the number of parameters that corresponds to the number of sub-elements in the auxiliary data. Otherwise, it must be a single parameter.
‡
The state pseudo-parameter must be either ...
or the number of parameters that corresponds to the number of arguments in the initial state, __VA_ARGS__
.
Auxiliary data to be passed through the algorithm to the predicate, operation, and target. This argument may be either a parenthetic expression of any arity or a non-parenthetic expression.
The initial state.
The CHAOS_PP_AUTO_FOR_AUX
macro is a higher-order thunk that deduces the current higher-order entry point and invokes CHAOS_PP_FOR_AUX
.
This macro may not be used if bypass semantics are in effect.
CHAOS_PP_AUTO_FOR
CHAOS_PP_AUTO_REPEAT
CHAOS_PP_AUTO_REPEAT_FROM_TO
CHAOS_PP_AUTO_REPEAT_SHIFTED
CHAOS_PP_FOR
CHAOS_PP_FOR_AUX
CHAOS_PP_FOR_AUX_PARAMETRIC
CHAOS_PP_FOR_AUX_X
CHAOS_PP_FOR_PARAMETRIC
CHAOS_PP_FOR_X
CHAOS_PP_REPEAT
CHAOS_PP_REPEAT_FROM_TO
CHAOS_PP_REPEAT_FROM_TO_PARAMETRIC
CHAOS_PP_REPEAT_FROM_TO_X
CHAOS_PP_REPEAT_PARAMETRIC
CHAOS_PP_REPEAT_SHIFTED
CHAOS_PP_REPEAT_SHIFTED_PARAMETRIC
CHAOS_PP_REPEAT_SHIFTED_X
CHAOS_PP_REPEAT_X
The CHAOS_PP_AUTO_FOR_AUX_ID
macro indirectly expands to CHAOS_PP_AUTO_FOR_AUX
.
CHAOS_PP_AUTO_FOR_AUX_ID
()
This macro effectively allows CHAOS_PP_AUTO_FOR_AUX
to be passed around without expansion.
The CHAOS_PP_AUTO_FOR_AUX_
macro expands to a λ expression representing CHAOS_PP_AUTO_FOR_AUX
.
CHAOS_PP_AUTO_FOR_AUX_
This macro is a shorthand form of the following λ binding:


My Dear, Greetings and l hope this message finds you well, and pardon me for contacting you this way , it is because of the urgent nature of my request.My name is Mrs. Mary Abavana and I am living with AIDS. Ever since my husband, Hon. Lawrence R. Abavana, former Ghanaian Agriculture minister died of AIDS and I was tested positive of the AIDS virus, the mass media wouldn't just let me and my family be. My children have stopped going to school because they could no longer stand the humiliations from fellow students. The embarrassment are getting on my nerves but what can I do? I would have loved to travel out of the country to a place where I will have peace and spend the rest of my life with my children but I couldn't make it for I am weak and severely sick. Moreover, no Embassy would grant me Visa to her country with my hairs withering and boils all over my body. View my family on the website: www.ghanacastle.gov.gh/President/castle_newsp_details.cfm?EmpID=476 >From all indications, I know that my days are numbered. In the light of the above, I have contacted you to assist me to accommodate the SUM OF $15 Million USD and the 250kg of Gold which my husband acquired while alive and serving as minister and which he deposited as with a Finance and Gold company here in ghana for the up-keep of my children. I want you to safe-keep it for my children as they will be coming over to your country in due course to live and school.  For your effort, I am prepared to give you 25% of the total funds. I am looking forward to hear from you while thanking you for your anticipated cooperation in this regard.Please l will need your telephone number also. Kind Regards, Mrs. Mary Abavana you can also write to me with<EMAIL_ADDRESS>_________________________________________________________________ Try the next generation of search with Windows Live Search today! http://imagine-windowslive.com/minisites/searchlaunch/?locale=en-us&source=hmtagline


BodyComponentBuilder(jointDefSize: Int = 2, fixtureDefSize: Int = 1)
Builder for BodyComponents
Allows serialization of the component, due to storing definitions (e.g BodyDefs)


Faculty Feedback doesn't make sense, do you believe the teachers cannot see our ratings?
And how do we judge the influence of every student?
Also, what's stopping me from giving dishonest feedback,
Maybe I give everyone 100%
We need a baseline, and in this competive world; the best way to do that is to rate teachers relative to each other;
Everyone gets 12 coins for the semester, which they may use to give their teachers, this is the infinity coin;
But each student may get only 12.
They must distribute this among their faculty;
Giving all to their favorite;
Or distributing it to their 'friends' to vote on their behalf
Every faculty starts out with 0 in their wallet, but may end the semester with multiple;
This is where the market comes in: teachers may sell their coins, reducing their balance;
Which brings us to the question, why?
-> 'Bonus' Passive Income for excellent teachers, an 'incentive' to teach well
-> A way of transferring income from the other teachers to the better ones.
-> A way for the college to measure performance
-> Instills a sense of capitalistic demcracy amongst students, as the students with a high balance are going to be very influential;
-> Commitees with a high balance will automatically be seen as better
-> This is because one can see how many coins were sent out ( not to whom ) in the end of the semester if the student wishes to make this data public


Job Search Test Page
Search for a City to recieve some jobs listings.
Search By Location
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Class: Aws::DatabaseMigrationService::Types::ReplicationPendingModifiedValues
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb |
Overview
Provides information about the values of pending modifications to a
replication instance. This data type is an object of the
ReplicationInstance
user-defined data type.
Constant Summary
Instance Attribute Summary
-
#allocated_storage ⇒ Integer
rw
The amount of storage (in gigabytes) that is allocated for the replication instance.
-
#engine_version ⇒ String
rw
The engine version number of the replication instance.
-
#multi_az ⇒ Boolean
rw
Specifies whether the replication instance is a Multi-AZ deployment.
-
#network_type ⇒ String
rw
The type of IP address protocol used by a replication instance, such as IPv4 only or Dual-stack that supports both IPv4 and IPv6 addressing.
-
#replication_instance_class ⇒ String
rw
The compute and memory capacity of the replication instance as defined for the specified replication instance class.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#allocated_storage ⇒ Integer
(rw)
The amount of storage (in gigabytes) that is allocated for the replication instance.
# File 'gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb', line 10514
class ReplicationPendingModifiedValues < Struct.new( :replication_instance_class, :allocated_storage, :multi_az, :engine_version, :network_type) SENSITIVE = [] include Aws::Structure end
#engine_version ⇒ String
(rw)
The engine version number of the replication instance.
# File 'gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb', line 10514
class ReplicationPendingModifiedValues < Struct.new( :replication_instance_class, :allocated_storage, :multi_az, :engine_version, :network_type) SENSITIVE = [] include Aws::Structure end
#multi_az ⇒ Boolean
(rw)
Specifies whether the replication instance is a Multi-AZ deployment.
You can't set the AvailabilityZone
parameter if the Multi-AZ
parameter is set to true
.
# File 'gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb', line 10514
class ReplicationPendingModifiedValues < Struct.new( :replication_instance_class, :allocated_storage, :multi_az, :engine_version, :network_type) SENSITIVE = [] include Aws::Structure end
#network_type ⇒ String
(rw)
The type of IP address protocol used by a replication instance, such as IPv4 only or Dual-stack that supports both IPv4 and IPv6 addressing. IPv6 only is not yet supported.
# File 'gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb', line 10514
class ReplicationPendingModifiedValues < Struct.new( :replication_instance_class, :allocated_storage, :multi_az, :engine_version, :network_type) SENSITIVE = [] include Aws::Structure end
#replication_instance_class ⇒ String
(rw)
The compute and memory capacity of the replication instance as defined for the specified replication instance class.
For more information on the settings and capacities for the available replication instance classes, see Selecting the right DMS replication instance for your migration.
# File 'gems/aws-sdk-databasemigrationservice/lib/aws-sdk-databasemigrationservice/types.rb', line 10514
class ReplicationPendingModifiedValues < Struct.new( :replication_instance_class, :allocated_storage, :multi_az, :engine_version, :network_type) SENSITIVE = [] include Aws::Structure end


Before Change
def _set_plugin_batchsize(plugin, available_vram):
Set the batch size for the given plugin based on given available vram
plugin.batchsize = int(max(1, available_vram // plugin.vram_per_batch))
logger.verbose("Reset batchsize for %s to %s", plugin.name, plugin.batchsize)
def _join_threads(self):
Join threads for current pass
for plugin in self._active_plugins:
After Change
Do not update plugins which have a vram_per_batch of 0 (CPU plugins) due to
zero division error.
if plugin.vram_per_batch != 0:
plugin.batchsize = int(max(1, available_vram // plugin.vram_per_batch))
logger.verbose("Reset batchsize for %s to %s", plugin.name, plugin.batchsize)
def _join_threads(self):
Join threads for current pass
for plugin in self._active_plugins:
plugin.join()