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Institute of Technology, Gopeshwar is one of the Four constituent colleges of
Uttarakhand Technical University that was established in October 2013 with the approval of
Uttarakhand Government.
IT Gopeshwar is approved by AICTE,Ministry of HRD, New Delhi.
IT Gopeshwar,is an Institute which aims to promote technological advancement in the state of uttarakhand.
It provides such a conductive environment to his students in which they become intellectually capable,
innovative and entrepreneurial professionals. IT Gopeshwar also provides an educational
training that promotes and emphasizes social awareness as well as development of professional skills among the students.
Here the knowledge of the students is broadened, deepened and enriched as the institute provide such
solutions that benefit humanity and the natural environment. IT Gopeshwar offers B.Tech.
Degree programs in the in the field of following streams:
The Uttarakhand Technical University, Dehradun was established on 27th January 2005 by Govt. of Uttarakhand through the Uttarakhand Technical University Act 2005. The University is the only affiliating University of the state running various private and Govt. institution. There are 4 Constituent and 118 affiliated colleges with 50,000 students in various courses. ITG offers four B. Tech programme in five disciplines viz. CSE, ECE, ME, CE, and EE. We at ITG strive to provide congenial academic environment by combining education with overall personality development. We have planned to introduce a number of add-on courses to develop an engineer in totality with his/her mind and body as perfect tools for specific career absorptions. Academic excellence is our priority. We have plans to tie up with world class organizations to bring our institution level. The University and ITGopeshwar aim at facilitating and promoting studies, research, technology incubation, product innovation and extension work in Science and Technology for achieving excellence in higher technical education. We provide a congenial environment for the holistic growth and all round development of the students through suitably designed teaching learning processes. The University is serving the technical education through its progress monitored each semester, part time Ph.D programme especially designed for the teachers and scientists by giving them opportunity to explore the untouched area of the research. The University in its own campus running M.Tech and M.Pharma Programmes in various disciplines. Apart from this University is having 4 Constituent Colleges namely:
My dear students and esteemed colleagues, At the very onset, I am prompted to use a John Dewey’s quote: “Education is not preparation for life; education is life itself”. My hearty welcome to you to join Institute of Technology (IT) Gopeshwar, a splendid Temple of Learning which is a constituent college of Uttarakhand Technical University (UTU). Choosing an Institution has been one of the most important decisions of your life. You are entering into the arena of higher education where the future is full of opportunities. I would like to congratulate you on choosing IT, Gopeshwar as an Institution in developing your career and realising dreams. Education is about much more than employability. It includes evolving with a different view, developing different perspectives and opening your mind to the enormous possibilities, and ultimately emerging as world class human resource. The University and IT offer you an intellectually challenging and rewarding atmosphere. It gives you the ability, self-confidence and the desire to go on learning for the rest of your life and to cope with new emerging and challenging situations. We at the University and IT are firmly committed to excel in technical education and research, and are fully aware of the challenges of the future. Apart from strengthening the teaching-learning process, research and extension programmes, special focus on extra-curricular, sports and other similar activities to foster global competencies among the students is our priority. The University and ITG aim at facilitating and promoting studies, research, technology incubation, product innovation and extension work in Science and Technology for achieving excellence in higher technical education. We provide a congenial environment for the holistic growth and all round development of the students through suitably designed teaching learning processes. Society is changing rapidly in many ways which will have a profound impact on the roles of Universities. In this changing world, knowledge will remain a key resource around the globe. The dynamics of the fast-changing world urges the academicians to give the right impetus for making life-long learners out of students. The horizons of professional activities are expanding, and hence today there is a greater scope for the students to present their talents and achievements. Since technical education in our country has become quite dynamic and competitive, we have to be ready and equipped with the required abilities and capacities to acquire the latest knowledge through newer technology. I welcome all the students who chose to be a part of this premier Institute (IT), I am sure that you will feel proud on being associated with us and make us equally proud with your academic excellence and accomplishments.
With best wishes, Prof Narendra S. Chaudhari Vice Chancellor, UTU, Dehradun
My dear students, I take this opportunity to introduce Institute of Technology, Gopeshwar (ITG) to the student’s community. It is time to realize that the finest society can be developed by using creative minds in the advanced areas of Science and Engineering. In that spirit we have come up with this engineering college where girls and boys are being groomed to reach the pinnacle of success. We are proud of your association with the college which will help in spreading the technical education in this region. ITG is focused to create an environment of the total development of students at different levels viz. intellectual, attitudinal, emotional & social. The college is poised to grow into an institute with a difference having strategic planning, enabling students to face the competitive world with confidence. My hearty welcome to you to join Institute of Technology (IT) Gopeshwar, a splendid Temple of Learning which is a constituent college of Uttarakhand Technical University (UTU). Choosing an Institution has been one of the most important decisions of your life. You are entering into the arena of higher education where the future is full of opportunities. I would like to congratulate you on choosing IT, Gopeshwar as an Institution in developing your career and realising dreams. ITG offers four B. Tech programme in five disciplines viz. CSE, ECE, ME, CE, and EE. We at ITG strive to provide congenial academic environment by combining education with overall personality development. We have planned to introduce a number of add-on courses to develop an engineer in totality with his/her mind and body as perfect tools for specific career absorptions. Academic excellence is our priority. We have plans to tie up with world class organizations to bring our institution level. The University and IT aim at facilitating and promoting studies, research, technology incubation, product innovation and extension work in Science and Technology for achieving excellence in higher technical education. We provide a congenial environment for the holistic growth and all round development of the students through suitably designed teaching learning processes. We are determined to have a close tie up and linkages with major Industrial Houses. We involve our student’s right from the beginning on short and long term basis for exhaustive training and industrial project work. It facilitates hands on experience in real technological aspects and skill development. ‘’Human minds perform their best when given a sharper adage”.
With best wishes, Dr. K.K.S.Mer Director, IT Gopeshwar
Institute of Technology, Gopeshwar is one of the Four constituent colleges of Uttarakhand Technical University that was established in October 2013 with the approval of Uttarakhand Government.
Institute of Technology, Gopeshwar is one of the Four constituent colleges of Uttarakhand Technical University that was established in October 2013 with the approval of Uttarakhand Government.


Cash or credit/debit over the phone accepted<PHONE_NUMBER> Buyer pays actual shipping cost Will Ship Lower
48 except: California, Colorado, Connecticut, Hawaii, Maryland, Massachusetts, New Jersey, New York,
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743_wangogogo.py
class UndergroundSystem:
def __init__(self):
self.people = {}
self.station = {}
def checkIn(self, id: int, stationName: str, t: int) -> None:
self.people[id] = [t,stationName]
def checkOut(self, id: int, stationName: str, t: int) -> None:
starttime,startstation = self.people[id]
if (startstation,stationName) not in self.station:
self.station[(startstation,stationName)] = (t-starttime,1)
else:
time,count = self.station[(startstation,stationName)]
self.station[(startstation,stationName)] = (time+t-starttime,count+1)
del self.people[id]
def getAverageTime(self, startStation: str, endStation: str) -> float:
time,count = self.station[(startStation,endStation)]
return time/count
# Your UndergroundSystem object will be instantiated and called as such:
# obj = UndergroundSystem()
# obj.checkIn(id,stationName,t)
# obj.checkOut(id,stationName,t)
# param_3 = obj.getAverageTime(startStation,endStation)


| 027:062 Khan | : |
Is not He (better than your gods) Who responds to the distressed one, when he calls Him, and Who removes the evil, and makes you inheritors of the earth, generations after generations. Is there any ilah (god) with Allah? Little is that you remember! |
| 027:062 Maulana | : |
Or, Who answers the distressed one when he calls upon Him and removes the evil, and will make you successors in the earth? Is there a god with Allah? Little is it that you mind! |
| 027:062 Pickthal | : |
Is not He (best) Who answereth the wronged one when he crieth unto Him and removeth the evil, and hath made you viceroys of the earth? Is there any Allah beside Allah?  Is it another god with GOD? Rarely do you take heed. |
| 027:062 Sarwar | : |
"(Are the idols worthier or) the One who answers the prayers of the distressed ones, removes their hardship, and makes you the successors in the land? Is there any lord besides God? In fact, you take very little heed. |
| 027:062 Shakir | : |
Or, Who answers the distressed one when he calls upon Him and removes the evil, and He will make you successors in the earth. Is there a god with Allah? Little is it that you mind! |
| 027:062 Sherali | : |
Or, Who answers the cry of the distressed person when he calls upon HIM, and removes the evil, and makes you successors in the earth? Is there a god with ALLAH? Little is that you reflect. |
| 027:062 Yusufali | : |
Or, Who listens to the (soul) distressed when it calls on Him, and Who relieves its suffering, and makes you (mankind) inheritors of the earth? (Can there be another) god besides Allah? Little it is that ye heed! |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > تقريع من لا يقر بوحدانيته سبحانه Pillars of Islam> Islamic> Oneness of Allah>Scolding who does not Acknowledge his Glory Oneness أركان الإسلام > التوحيد > توحيد الله تعالي > وحدانيته Pillars of Islam> Islamic> Oneness of Allah> Singularity and Being Unique أركان الإسلام > الصلاة > الدعاء > الحث على الدعاء Pillars of Islam> The Prayers>The Invocation> Incitement for Invocation أركان الإسلام > الصلاة > الدعاء > المأثور من الدعاء Pillars of Islam> The Prayers>The Invocation> What is said Concerning Invocation الإنسان والعلاقات الاجتماعية > الإنسان > شرفه ودنوه Man and The Social Relations>Man>When Honourable and When Unhonourable الإنسان والعلاقات الاجتماعية > الرجل والمرأة Man and The Social Relations>Man and Woman الإنسان والعلاقات الاجتماعية > المجتمعات > جعلهم خلائف Man and The Social Relations>The Societies>Making them Successors العلاقات القضائية > أحكام قانونية > العفو > الاضطرار Judicial Relationships>Legal Rules>Forgiveness>To Be Obliged العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > البحر Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>The Sea
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Notes 8.3.2
Reliable Data Transfer Protocol - No bits are corrupted or lost, and all are delivered in the same order.
Unidirectional Data Transfer - Data dransfer from sender --> reciever.
Bidirectional (Full Duplex) Data transfer - data from sender <--> reciever.
Finite State Machine - Abstract machine that can be in a finite number of states.
Positive/Negative Acknowledgmens - "OK"/"Repeat".
Automatic Repeat reQuest (ARQ) Protocols - Reliable data transfer protocols based on retransmission.
Alternating Bit Protocol - Instead of incrementing the packet ID, it alternates between 0 and 1 (only using 1 bit).
Go-Back-N - Transmit a maximum of N unacknowledged packets until timeout, then go back N packets and retransmit.
Selective Repeat - Only retransmit packets that were recieved in error or not recieved.
Connection-Oriented - Before data transfer can occur, the two processes must "handshake".
Maximum Segment Size (MSS) - Maximum size a segment can have.
Maximum Transmission Unit (MTU) - Largest link-layer frame that can be sent by the sender.
TCP Segmemt Structure:


Detailed Description
Functions, macros and enums related to endpoint management when in USB Device mode. This module contains the endpoint management macros, as well as endpoint interrupt and data send/receive functions for various data types.
Define Documentation
| #define ENDPOINT_BANK_DOUBLE (1 << EPBK0) |
Mask for the bank mode selection for the Endpoint_ConfigureEndpoint() macro. This indicates that the endpoint should have two banks, which requires more USB FIFO memory but results in faster transfers as one USB device (the AVR or the host) can access one bank while the other accesses the second bank.
| #define ENDPOINT_BANK_SINGLE (0 << EPBK0) |
Mask for the bank mode selection for the Endpoint_ConfigureEndpoint() macro. This indicates that the endpoint should have one single bank, which requires less USB FIFO memory but results in slower transfers as only one USB device (the AVR or the host) can access the endpoint's bank at the one time.
| #define ENDPOINT_BANKS_SUPPORTED |
( |
|
EPIndex ) |
_ENDPOINT_GET_BANKS(EPIndex) |
Indicates the total number of banks supported by the given endpoint.
- Parameters:
-
| [in] | EPIndex | Endpoint number, a value between 0 and (ENDPOINT_TOTAL_ENDPOINTS - 1) |
| #define ENDPOINT_CONTROLEP 0 |
Endpoint address for the default control endpoint, which always resides in address 0. This is defined for convenience to give more readable code when used with the endpoint macros.
| #define ENDPOINT_CONTROLEP_DEFAULT_SIZE 8 |
Default size of the default control endpoint's bank, until altered by the control endpoint bank size value in the device descriptor. Not available if the FIXED_CONTROL_ENDPOINT_SIZE token is defined.
| #define ENDPOINT_DIR_IN (1 << EPDIR) |
Endpoint data direction mask for Endpoint_ConfigureEndpoint(). This indicates that the endpoint should be initialized in the IN direction - i.e. data flows from device to host.
| #define ENDPOINT_DIR_OUT (0 << EPDIR) |
Endpoint data direction mask for Endpoint_ConfigureEndpoint(). This indicates that the endpoint should be initialized in the OUT direction - i.e. data flows from host to device.
| #define ENDPOINT_EPDIR_MASK 0x80 |
Endpoint direction mask, for masking against endpoint addresses to retrieve the endpoint's direction for comparing with the ENDPOINT_DESCRIPTOR_DIR_* masks.
| #define ENDPOINT_EPNUM_MASK 0x07 |
Endpoint number mask, for masking against endpoint addresses to retrieve the endpoint's numerical address in the device.
| #define ENDPOINT_EPSIZE_MASK 0x7F |
Endpoint bank size mask, for masking against endpoint addresses to retrieve the endpoint's bank size in the device.
| #define ENDPOINT_MAX_SIZE |
( |
|
EPIndex ) |
_ENDPOINT_GET_MAXSIZE(EPIndex) |
Maximum size in bytes of a given endpoint.
- Parameters:
-
| [in] | EPIndex | Endpoint number, a value between 0 and (ENDPOINT_TOTAL_ENDPOINTS - 1) |
| #define ENDPOINT_TOTAL_ENDPOINTS ENDPOINT_DETAILS_MAXEP |
Total number of endpoints (including the default control endpoint at address 0) which may be used in the device. Different USB AVR models support different amounts of endpoints, this value reflects the maximum number of endpoints for the currently selected AVR model.
Function Documentation
| void Endpoint_ClearStatusStage |
( |
void |
) |
|
Completes the status stage of a control transfer on a CONTROL type endpoint automatically, with respect to the data direction. This is a convenience function which can be used to simplify user control request handling.
| static bool Endpoint_ConfigureEndpoint |
( |
const uint8_t |
Number, |
|
|
const uint8_t |
Type, |
|
|
const uint8_t |
Direction, |
|
|
const uint16_t |
Size, |
|
|
const uint8_t |
Banks |
|
) |
| [inline, static] |
Configures the specified endpoint number with the given endpoint type, direction, bank size and banking mode. Once configured, the endpoint may be read from or written to, depending on its direction.
- Parameters:
-
| [in] | Number | Endpoint number to configure. This must be more than 0 and less than ENDPOINT_TOTAL_ENDPOINTS. |
| [in] | Type | Type of endpoint to configure, a EP_TYPE_* mask. Not all endpoint types are available on Low Speed USB devices - refer to the USB 2.0 specification. |
| [in] | Direction | Endpoint data direction, either ENDPOINT_DIR_OUT or ENDPOINT_DIR_IN. All endpoints (except Control type) are unidirectional - data may only be read from or written to the endpoint bank based on its direction, not both. |
| [in] | Size | Size of the endpoint's bank, where packets are stored before they are transmitted to the USB host, or after they have been received from the USB host (depending on the endpoint's data direction). The bank size must indicate the maximum packet size that the endpoint can handle. |
| [in] | Banks | Number of banks to use for the endpoint being configured, an ENDPOINT_BANK_* mask. More banks uses more USB DPRAM, but offers better performance. Isochronous type endpoints must have at least two banks. |
- Note:
- Endpoints must be configured in ascending order, or bank corruption will occur.
-
Certain models of USB AVR's endpoints may have different maximum packet sizes based on the endpoint's index - refer to the chosen USB AVR's datasheet to determine the maximum bank size for each endpoint.
-
The default control endpoint should not be manually configured by the user application, as it is automatically configured by the library internally.
-
This routine will automatically select the specified endpoint upon success. Upon failure, the endpoint which failed to reconfigure correctly will be selected.
- Returns:
- Boolean true if the configuration succeeded, false otherwise.
| static void Endpoint_DisableEndpoint |
( |
void |
) |
[inline, static] |
Disables the currently selected endpoint so that data cannot be sent and received through it to and from a host.
| static void Endpoint_EnableEndpoint |
( |
void |
) |
[inline, static] |
Enables the currently selected endpoint so that data can be sent and received through it to and from a host.
- Note:
- Endpoints must first be configured properly via Endpoint_ConfigureEndpoint().
| static uint8_t Endpoint_GetCurrentEndpoint |
( |
void |
) |
[inline, static] |
Get the endpoint address of the currently selected endpoint. This is typically used to save the currently selected endpoint number so that it can be restored after another endpoint has been manipulated.
- Returns:
- Index of the currently selected endpoint.
| static uint8_t Endpoint_GetEndpointDirection |
( |
void |
) |
[inline, static] |
Determines the currently selected endpoint's direction.
- Returns:
- The currently selected endpoint's direction, as a ENDPOINT_DIR_* mask.
| static uint8_t Endpoint_GetEndpointInterrupts |
( |
void |
) |
[inline, static] |
Returns a mask indicating which INTERRUPT type endpoints have interrupted - i.e. their interrupt duration has elapsed. Which endpoints have interrupted can be determined by masking the return value against (1 << {Endpoint Number}).
- Returns:
- Mask whose bits indicate which endpoints have interrupted.
| static bool Endpoint_HasEndpointInterrupted |
( |
const uint8_t |
EndpointNumber ) |
[inline, static] |
Determines if the specified endpoint number has interrupted (valid only for INTERRUPT type endpoints).
- Parameters:
-
| [in] | EndpointNumber | Index of the endpoint whose interrupt flag should be tested. |
- Returns:
- Boolean true if the specified endpoint has interrupted, false otherwise.
| static bool Endpoint_IsConfigured |
( |
void |
) |
[inline, static] |
Determines if the currently selected endpoint is configured.
- Returns:
- Boolean true if the currently selected endpoint has been configured, false otherwise.
| static bool Endpoint_IsEnabled |
( |
void |
) |
[inline, static] |
Determines if the currently selected endpoint is enabled, but not necessarily configured.
- Returns:
- Boolean True if the currently selected endpoint is enabled, false otherwise.
| static void Endpoint_ResetDataToggle |
( |
void |
) |
[inline, static] |
Resets the data toggle of the currently selected endpoint.
| static void Endpoint_ResetFIFO |
( |
const uint8_t |
EndpointNumber ) |
[inline, static] |
Resets the endpoint bank FIFO. This clears all the endpoint banks and resets the USB controller's In and Out pointers to the bank's contents.
- Parameters:
-
| [in] | EndpointNumber | Endpoint number whose FIFO buffers are to be reset. |
| static void Endpoint_SelectEndpoint |
( |
const uint8_t |
EndpointNumber ) |
[inline, static] |
Selects the given endpoint number. If the address from the device descriptors is used, the value should be masked with the ENDPOINT_EPNUM_MASK constant to extract only the endpoint number (and discarding the endpoint direction bit).
Any endpoint operations which do not require the endpoint number to be indicated will operate on the currently selected endpoint.
- Parameters:
-
| [in] | EndpointNumber | Endpoint number to select. |
| static void Endpoint_SetEndpointDirection |
( |
const uint8_t |
DirectionMask ) |
[inline, static] |
Sets the direction of the currently selected endpoint.
- Parameters:
-
| [in] | DirectionMask | New endpoint direction, as a ENDPOINT_DIR_* mask. |
Variable Documentation
Global indicating the maximum packet size of the default control endpoint located at address 0 in the device. This value is set to the value indicated in the device descriptor in the user project once the USB interface is initialized into device mode.
If space is an issue, it is possible to fix this to a static value by defining the control endpoint size in the FIXED_CONTROL_ENDPOINT_SIZE token passed to the compiler in the makefile via the -D switch. When a fixed control endpoint size is used, the size is no longer dynamically read from the descriptors at runtime and instead fixed to the given value. When used, it is important that the descriptor control endpoint size value matches the size given as the FIXED_CONTROL_ENDPOINT_SIZE token - it is recommended that the FIXED_CONTROL_ENDPOINT_SIZE token be used in the descriptors to ensure this.
- Note:
- This variable should be treated as read-only in the user application, and never manually changed in value.


Apigee Entity Sample App
This app show the basics of working with assets using the Apigee JavaScript SDK. Click each button to execute an API call and see its response. For this app to work properly you must run the create step first.
To view the code and more detailed comments on how this app operates, open js/index.js.


Introduction
This essay is based on a talk given on June 19, 2019 at the "Probabilistic & Differentiable Programming Summit". Original slides are available here. All the code for producing the animations is available on github, mostly leaning on a bespoke library for researching MCMC written with Jax and autograd.
I am an open source contributor on a number of libraries, notably PyMC3, which is a library for probabilistic programming in Python. One of the benefits of PyMC3 is the friendly, simple API that abstracts away the details of the inference, allowing domain experts to fit models without needing to be experts at inference:
import pymc3 as pm with my_model: # defined earlier trace = pm.sample() # ✨✨magic✨✨
This essay has two goals: Many thanks to the PyMC3 contributors for building the library, and the conference organizers for inviting me. Thanks also to Robert P. Goldman for thoughtful proofreading, and to my colleagues at , particularly Max Livingston, Paul Kernfeld, and Sam Zimmerman, for patient comments and suggestions.
- Provide some intuition into the geometry of some of the algorithms and diagnostics that we use in probabilistic programming to give some appreciation to what happens in the ✨✨magic✨✨ step, and
- Do a code tour to describe how PyMC3 tunes sampler parameters.
This talk is partly in reaction to a question I was recently asked: Good general advice is to just check the Stan manual for details, but I will do some compare and contrasting on PyMC3's approach.
"What does PyMC3 do that I cannot find in the Stan manual?"
While I love PyMC3's ease of use, extensibility, and community, we have a well-earned reputation for borrowing good ideas from other projects, particularly the wonderful Stan library.
"There's an old saying... 'Python is the second-best language for everything'" - @callahad #PyCon2018
— Jake VanderPlas (@jakevdp) May 11, 2018
What is tuning?
"Tuning" broadly describes what happens before sampling. In Bayesian inference, our goal is to calculate expectations over probability distributions. Recall that an expectation with respect to a distribution \(\pi\) is $$ \mathbb{E}[f] = \int f~d\pi. $$ MCMC works by observing that if you have samples from your distribution, \(x_j \sim \pi\), then $$ \mathbb{E}[f] \approx \frac{1}{n}\sum_n f(x_j). $$ MCMC achieves that by producing samples that are asymptotically guaranteed to come from the distribution of interest: the "stationary distribution".
When we tune, we throw away these asymptotic guarantees, searching for parameters for our sampler that will speed up the calculation of these expectations. In the particular case of PyMC3, we default to having 500 tuning samples, after which we fix all the parameters so that the asymptotic guarantees are again in place, and draw 1,000 new samples. By default, the first 500 tuning samples are discarded, as it would be theoretically shaky ground to suggest they came from the stationary distribution at all.
Tuning in Metropolis-Hastings
To warm up, consider a Metropolis-Hastings sampler. Recall that Metropolis-Hastings works by making a proposal, and accepting or rejecting based on the ratio of the density of the stationary distribution at the proposed point and at the current point: Always accept if the proposal has higher probability, and sometimes reject if the proposal has lower probability: If we are being careful (which, we're not), Hastings extended the algorithm to non-symmetric proposal distributions, so this is just a Metropolis acceptance, but all of our proposals will be symmetric anyways $$A(x_t) = \min\left\{1, \frac{\pi(x_t)}{\pi(x_{t - 1})}\right\}$$
If we consider a proposal distribution that is a normal centered at the previous point with diagonal covariance, there is one parameter to tune, which is the step size. $$x_{t + 1} \sim \mathcal{N}(x_t, sI)$$
What are our tradeoffs when choosing step size? Here is a very small step size, being used to sample from an isotropic Gaussian. Notice that nearly every proposal is being accepted, because the steps are small enough that the underlying stationary distribution does not change very much. On the right-hand side is an empirical estimate of the mean and 1 standard deviation ellipse, compared with the true mean and standard deviation.
Going in the other direction, these are very large steps for the proposal - 10,000 times as large as the previous figure - and notice that most proposals are rejected. You can see the empirical mean and standard deviation keep updating as the current position is resampled. There is some evidence that says an acceptance probability of 23.4% is optimal in terms of these expectations converging Roberts, Gelman, and Gilks. "Weak convergence and optimal scaling of random walk Metropolis algorithms.". In practice, PyMC3 tries to get the acceptance probability between 20% and 50% through tuning.
Here we have a tuned diagonal proposal distribution. There is a mix of acceptances and rejections, and it looks like the mean and covariance are converging to the true values more quickly. Remember: all three of these examples have theoretical convergence guarantees, but we are interested in fast convergence of these expectations.
In any rate, this tuning is implemented in a windowed fashion in PyMC3, which will also be the strategy for tuning in our gradient-based samplers.
During each of these windows, we make an estimate of a quantity - here the acceptance rate - and then update parameters based on that estimate. This is actual source code, and by default, every 100 samples, this switch statement runs.
def tune(scale, acc_rate): """Tunes the scaling parameter for the proposal distribution according to the acceptance rate over the last tune_interval: Rate Variance adaptation ---- ------------------- <0.001 x 0.1 <0.05 x 0.5 <0.2 x 0.9 >0.5 x 1.1 >0.75 x 2 >0.95 x 10 """ if acc_rate < 0.001: # reduce by 90 percent scale *=0.1 elif acc_rate < 0.05: # reduce by 50 percent ... elif acc_rate > 0.95: # increase by one thousand percent scale *= 10 return scale
Aside: Effective Sample Size
It is worth noting here that the animations above are asking you to judge convergence heuristically. In reality, effective sample size is the diagnostic that we are trying to intuit. The number comes from the observation that for independent, identically distributed random variables, the variance of the empirical mean, \(\hat{\mu}\), is the true variance divided by the number of samples: $$\hat{\mu} = \frac{1}{n}\sum_{j=1}^n x_j$$ $$\operatorname{Var}(\hat{\mu}) = \frac{\sigma^2}{n},$$ where \(\sigma\) is the true standard deviation of the underlying distribution.
Then the effective sample size is defined as the denominator that makes this relationship still be true: $$\operatorname{Var}(\hat{\mu}) = \frac{\sigma^2}{n_{\text{eff}}}.$$
This quantity is difficult to compute: you might use multiple chains to estimate \(\operatorname{Var}(\hat{\mu})\), and estimating \(\sigma^2\). There is a great recent paper Vehtari, Aki, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. "Rank-Normalization, Folding, and Localization: An Improved \(\widehat{R}\) for Assessing Convergence of MCMC." giving better up to date details.
We might visualize this relationship by running our previous experiment many times, to estimate the variance of the empirical mean over all our samples.
Tuning in Hamiltonian Monte Carlo
Next we talk about the real interesting bit here: adaptation in gradient-based samplers, specifically Hamiltonian Monte Carlo. As a warning, the story is importantly different for the No-U-Turn sampler Hoffman and Gelman. "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.", mainly because of the dynamic tree building, compared to a fixed path length in HMC.
Recall that in Hamiltonian Monte Carlo, there is a system of differential equations we solve, integrating a position, \(\mathbf{q}\), and momentum, \(\mathbf{p}\), for some fixed amount of time I, for one, blame the physicists for \(\mathbf{p}\text{omentum}\) and \(\mathbf{q}\text{osition.}\). $$ \frac{d \mathbf{q}}{dt} = \mathbf{p}, ~~ \frac{d \mathbf{p}}{dt} = - \frac{\partial V}{\partial \mathbf{q}} $$ Details note: \(V\) is the negative log probability, \(-\log \pi(\mathbf{q})\), and is the potential energy in this system. This term is also the reason this is a gradient-based method, and calculating it for arbitrary distributions is why most modern probabilistic programming languages use an autodiff implementation.
This is a 2D mixture of 3 Gaussians, and I am just simulating Hamiltonian dynamics here, not taking any samples. The Hamiltonian equations conserve energy, so in this physical simulation, a trajectory far away from the probability mass will move slowly: it has traded velocity for potential energy. Conversely, when trajectories pass near regions of high probability, they will move fast, but have very little potential energy.
To look deeper into tuning parameters, let's drop down a dimension, and consider this one dimensional mixture of three Gaussians.
To perform HMC, we double the number of dimensions by adding a momentum parameterThis is why we dropped down to 1 dimension, because a 2-dimensional distribution would require 4 dimensions to visualize the position-momentum phase space.. We evolve the system for a fixed path length, and then the new position becomes our new sample, we discard the momentum, and resample a new one. A nice intuition to pick up here is that you can get between modes only when sampling an extreme momentum, which gives the trajectory high energy. So the marginal x-axis samples are from our stationary distribution, and the marginal y-axis samples are from the momentum distribution, which we pick. In this case, I chose a normal distribution. One parameter we will work on tuning is the covariance of this momentum distribution. There is research that suggests a covariance that matches the stationary distribution is optimal. Betancourt, Michael. "A Conceptual Introduction to Hamiltonian Monte Carlo."
We also care about step size. Each of these simulations are sampling 100 points from our stationary distribution, and is displayed with the same frame rate, so this is how the computer feels running your sampling. There is a tradeoff between large steps and integration error, and so you can not just take arbitrarily large steps. We actually have to do a Metropolis acceptance step at the end of each trajectory. By default, PyMC3 tries to get an acceptance rate of 65% for HMC, and 85% for NUTS.
Tuning HMC
Step size adaptation is fairly quick. These are stochastic optimization problems. You can use a variant of what we used for Metropolis-Hastings earlier, but PyMC3 uses the dual averaging algorithm from the NUTS paper:
def dual_averaging_adapt(self, p_accept): self.error_sum += self.target_accept - p_accept log_step = (self.mu - t ** -0.5 * self.error_sum / self.gamma) eta = self.t ** -self.kappa self.log_averaged_step *= (1 - eta) self.log_averaged_step += eta * log_step self.t += 1 return np.exp(log_step), np.exp(self.log_averaged_step)
It is a pretty dense algorithm, and the goal of putting it here is just to show that we can write it
down in a few lines. The parts to call out are that there is a log_step
that does some
noisy exploration, and a stateful log_averaged_step
that smooths out the exploratory steps.
While tuning, we use the exploring step size, and after tuning we switch to the averaged step size.
Here is what the exploring step size, smoothed step size, and simple algorithm look like on a toy problem. You can see convergence is somewhat faster for the dual averaging algorithm. I initialized the simple step size adaptation (which we used in the Metropolis tuning earlier) at a pretty unfair spot. I do not think it is as bad as that.
Covariance estimation is also done in an online fashion:
class _WeightedVariance: def add_sample(self, x): self.w_sum += 1 old_diff = x - self.mean self.mean[:] += old_diff / self.w_sum new_diff = x - self.mean self.raw_var[:] += old_diff * new_diff
There is some math going on here you can check, but it runs during tuning, on two different estimator objects, every step:
class QuadPotentialDiagAdapt: def update(self, sample): self._foreground_var.add_sample(sample) self._background_var.add_sample(sample) self._update_from_weightvar(self._foreground_var) n_samples = self._n_samples if n_samples > 0 and n_samples % self.window == 0: self._foreground_var = self._background_var self._background_var = _WeightedVariance() self._n_samples += 1
The foreground and background covariance estimators swap out every window
steps, which is
100 by default.
So what does this look like?
PyMC3 has two covariance matrices updating constantly, and the exploratory step size is running the whole time as well. Compare this to this sketch of the Stan warmup routine:
The fast tuning of step size is done first, then expanding windows to estimate the covariance matrix, before a final step size adaptation.
Takeaways
- Automate what you can: a simple API that sets sensible defaults has worked well for us.
- Consider effective sample size: samples per second is not a good benchmark to optimize This advice might seem like it is throwing shade at PyMC3's ubiquitous progress bar - which reports samples per second - but reporting progress is different from a benchmark. See, e.g., Don Norman's "The Design of Everyday Things", which suggests providing "full and continuous information about the results of actions and the current state of the product or service." .
- Windowed adaptation: it provides a few orders of magnitude of improvement to effective samples per second.


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Quick Start
Download and install
go get github.com/astaxie/beego
Create file hello.go
package main
import "github.com/astaxie/beego"
func main() {
beego.Run()
}
Build and run
go build hello.go
./hello
View effects
Open your browser and visit http://localhost:8080
Congratulations! You just built your first beego app.
Please see Documentation for going further.
Features
EASY TO USE
With RESTful support, MVC model, and use bee tool to build your apps quickly with features including code hot compile, automated testing, and automated packing and deploying.
INTELLIGENT
With intelligent routing and monitoring, it's able to monitor your QPS, memory and CPU usages, and goroutine status. It provides you full control of your online apps.
MODULAR
With powerful built-in modules including session control, caching, logging, configuration parsing, performance supervising, context handling, ORM supporting, and requests simulating. You get the powerful foundation for any type of applications.
HIGH PERFORMANCE
With native Go http package to handle the requests and the efficient concurrence of goroutine. Your beego applications can handle massive trafic as beego are doing in many productions.


IN DREAMS A movie review by Joe Barlow (c) Copyright 1999
STARRING: Annette Bening, Aidan Quinn, Katie Sagona DIRECTOR: Neil Jordan WRITERS: Neil Jordan and Bruce Robinson (based on the book "Doll's Eyes" by Barry Wood) RATED: R RELEASED: 1999
RATING: ** 1/2 (out of a possible ****)
"In Dreams" is such a strikingly original film that one is somewhat tempted to forgive its excesses. A psychological thriller comprised of equal parts intrigue and phantasmagoria, the story effectively drenches the audience in shadowy insanity, even if the characters themselves never really involve us. It's a textbook example of style over substance.
The plot involves Claire Cooper (Annette Bening), a New England housewife who finds herself mentally linked to a serial killer, sensing his every thought. (The name Claire was chosen, I suspect, as a pun on the word "clairvoyant.") Slowly going insane from the experience, Claire attempts to find and confront the killer, with whom she has a personal score to settle.
It sounds great on paper, but the film is ultimately not as satisfying as one might expect. Despite its promising start, "In Dreams" loses sight of its surprisingly effective macabre tone just past its halfway point, eventually collapsing into a tangled mess of contradictions and confusion.
Much of the story is standard issue: Claire's husband Paul (a thankless performance by Aidan Quinn) tries to convince the police that his wife's psychic abilities are genuine and can assist them in the search for the killer; they scoff, but eventually come around. Claire, meanwhile, is locked up in a mental institution, but manages to escape with a little help from that old movie standby, the ventilation ducts. Soon she's on the trail of the killer, a deeply disturbed fellow by the name of Vivian (actor's identity withheld; see below), and things are wrapped up by not one but *two* tried-and-true staples of American cinema: the chase scene and the gunfight. Yawn. Did someone forget that the film was supposed to be a psychological thriller? Did they think they were making "Die Hard part 19?"
Since the movie goes to great pains to hide the identity of the actor playing Vivian until the last possible moment, I won't reveal the surprise, except to mention that it's a satisfactory performance, though perhaps a bit too over-the-top to be truly "great." On the other hand, Annette Bening's work here gets steadily better as the film progresses; surprisingly, she's far more convincing as a lunatic than the smiling housewife she portrays in the story's opening scenes. Aidan Quinn's performance as Paul, however, is given too little screen time to really become anything more than a fleeting face in the shadows.
As is so often the case, there's a bit too much story contained in the movie. Particularly puzzling is a subplot involving an affair Paul may or may not have been having with an Australian woman-- after a brief mention in the story's opening scenes, the whole issue is completely abandoned. I question its inclusion in the film's final cut; it adds nothing but confusion to the story. Since the movie clearly thinks Paul is a good guy, why question it?
Nonetheless, "In Dreams" has flashes of remarkable originality, marred primarily by its reliance on a conventional resolution for a plot that is *anything* but ordinary; director Neil Jordan deserves credit for even attempting a project of this nature. Boldly photographed and directed, the movie comes frustratingly close to greatness, though in the end its efforts are undermined by a generic finale and logic gaps that could probably have been fixed if another draft of the screenplay had been written. (Question: if Claire and Vivian are psychically connected, why can't he find her when she's hiding in the woods? And why did the hospital put Claire in the *exact same room* that Vivian had occupied 25 years before? That blows beyond mere "suspension of disbelief" and enters into the boring realm of "plot contrivance.") And Neil Jordan's bizzare symbolism fills the frame: what's the significance of the apples? Are we supposed to be this perplexed while watching? Sometimes the director is so determined to prove that he's clever that coherency and plot are sacrified in favor of technique.
But there's still the potential for entertainment here, especially if you like film noir, a genre that doesn't get much attention these days. The movie definitely has some great moments; the "Titanic"-like underwater scenes at the local reservoir are haunting, as are many of Claire's hallucinations. Through them, we can feel (and share) her madness.
I screened "In Dreams" with my brother, himself a fine amateur critic, and his summary of the film says it all: it's like "Nightmare on Elm Street" meets "What Dreams May Come." That should tell you all you need to know. Thanks, Adam; I couldn't have said it better myself.
E-Mail<EMAIL_ADDRESS>Joe Barlow on Film: http://www.ipass.net/~jbarlow/film.htm
If you'd like to receive new film reviews by e-mail, please write to the above address and ask to be put on my mailing list. Or, you can subscribe directly from my web page. Thanks.
-----------== Posted via Deja News, The Discussion Network ==---------- http://www.dejanews.com/ Search, Read, Discuss, or Start Your Own
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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WITNESS LIST Revenue & Public Education Funding, Select Committee March 14, 1997-9:00A School Finance On: Anderson, David (Texas Education Agency) Wisnoski, Joe (Texas Education Agency)


New Zealand v England first Test: Black Caps secure victory
by Sporting LifeEngland have lost the first Test against New Zealand by an innings and 65 runs in Mount Maunganui to fall 1-0 down in the two-match series. Check out our report and reaction here.
Scorecard:
England: 353 all out: Stokes 91; Southee 4-88 197 Wagner 5-44
New Zealand 394-6: Watling 205; Santer 126
New Zealand won by an innings and 65 runs
Report
England were unable to negotiate batting out the final day against New Zealand as a series of injudicious strokes saw the tourists hammered by an innings and 65 runs at Mount Maunganui.
Neil Wagner's five for 44 included two wickets in as many balls to bring an abrupt conclusion to proceedings as the Black Caps took a 1-0 lead in the two-Test series, but a number of England batsmen were culpable in their own downfall.
With seven wickets in hand and needing to bat out the day to guarantee the series stayed level heading to Hamilton later this week, England captain Joe Root and deputy Ben Stokes were among those out to puzzling shot selections.
England's heavy loss on a sluggish pitch - albeit one that offered turn and bounce out of the footholes for Mitch Santner, who took three for 53 - was all the more galling given Trent Boult was only able to send down one over on Monday because of a rib injury that leaves him doubtful for the second Test.
But the hosts found their opponents in charitable mood as the only batsman to last more than 100 balls was Joe Denly, who received scant support elsewhere in his 35 from 142 deliveries as England were all out for 197.
A draw had seemed distinctly remote once they had lurched from 121 for four to 138 for eight before tea and there were still 21.4 overs remaining when Stuart Broad was the last man out for a golden duck in the final session.
Reaction
England captain Joe Root accepts that a shift in strategy will take time to implement after they were humbled by an innings and 65 runs in the first Test against New Zealand.
He said: "We want to play in a certain way which has been very different to how we've had to play in our conditions over the last couple of years and it's going to take time, it's not going to happen overnight.
"Clearly some of the shots were not good. Everyone's very aware of that. I hold my hand up as well.
"I've had a bit of a rough time of it in terms of scores, I'm working extremely hard on it and sometimes it doesn't always come that easy to you.
"You have to get through difficult periods and go through a little bit of a dip sometimes. The fact that I'm captain at the same time, it's an easy excuse to use as a player.
"I keep putting the work in and I'm going to have to keep trusting my game because I've got a lot in the bank, I know I can do it and I don't feel like I'm far away."


Tone.PolySynth
handles voice creation and allocation for any monophonic
instruments passed in as the second parameter. PolySynth is
not a synthesizer by itself, it merely manages voices of
one of the other types of synths, allowing any of the
monophonic synthesizers to be polyphonic.


user
org
room
users
NEW_USER
{{ o.name }}
ID
UserName
Password
Level
Org
{{ org.name }}
Name
Email
modify
add
delete
orgs
NEW_ORG
{{ o.name }}
ID
Name
modify
add
delete
rooms
NEW_ROOM
{{ o.name }}
ID
Name
modify
add
delete


This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Types
Items
A basic item list.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.
Variations
Stackable
An item view can have its items take the full width when below a browser resolution threshold to allow for content to display properly at small sizes
Connected
An item list can be automatically sized to be even height.
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.
Column count
An item list can list how many columns should exist in a row
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.
A connected item list with a specified column count
This dog has some things going for it. Its pretty cute and looks like it'd be fun to cuddle up with.
Sometimes its more important to have a dog you know you can trust. But not every dog is trustworthy, you can tell by looking at its smile.
Silly dogs can be quite fun to have as companions.


![cheetah](/images/cheetah.baby.jpg)
### Spider
- Pushing CRM segments to vendors
- Applications:
* (re)targeting
* outside the website
### Not real-time
- Updates once a while
- Heavy data flows
- Slow
### Use case: personalisation
A customer comes on the website
- What's her loyalty status?
- What's her beauty tribe?
- Etc.
### First assemption
#### Let's push this data from the backend!
- Takes a lot of resources
* Calculated segments
- What about non-digital customers?
### Second assemption
#### Let's create a new target on Spider
- Spider moves huge datasets from a source to a target
- Not optimized for querying a single customer
- No public / private endpoint
## 4 landing customers types
1. Logged in
2. From an email, digital customer
3. From an email, retail only customer
4. The other ones!
*Let's forget the 4. for a while!!*
## Idea
When a customer lands
- Send the card number to Cheetah
- Cheetah sends the CRM data
- Store the data into a cookie
## Cookies
![cookie](/images/cookie.png)
### Personalisation of the banners
- DY
- Our own popups
### Cart / Session abandonment
- Ematic


controls
Mark
|<
-
+
>|
View Mode:
Rel to Cam
Rel to Motion
Absolute
Target
Tour Points:
TourPoints:
Tour Info:


Look no further. Chris's high degree of professionalism makes him a superb standout in the field of magic and illusion. His performance skills and versatility have kept him in great demand for corporate events throughout Ontario. For a strictly client-oriented occasion - let Chris bring his broad experience, humour and sophistication to:
Whether your event includes family, friends and neighbours, kids only, or a community group, Chris Pathsworth's magic is guaranteed to captivate. Chris' shows combine a unique and varied repertoire with a warm, humorous style that quickly creates a rapport with his audience. And his performances can be tailored for the individual occasion, particular age group or audience interest. Well-known and respected throughout Ontario, Chris is called back year after year to put on shows at:
Enter stage right: Chris Pathsworth Productions. Catch the magic! Then let the magic capture your audiences. Chris' crowd-pleasing presentations combine suspense with surprise, warmth with humour, and drama with eye-catching visuals. The magic and illusions - many of which are Chris Pathsworth originals - are performed expertly and intelligently. And the special rapport that he builds with his audience creates many magical moments. Ardently professional, Chris' exacting standards result in strong performances. Catch The Magic is a 60-minute magic extravaganza that is ideal for "soft seat" theatres seating from 300 to 700. The show will captivate the imagination of audiences age 4 to 84. This is a unique presentation that blends magic and illusion with comedy and music. Highlights: a cactus that magically appears inside a clear balloon! Catch The Magic has been presented at: Centre for the Arts, St. Catherines, Ontario Meadowvale Theatre, Mississauga, Ontario Sudbury Theatre, Sudbury, Ontario Showplace Peterborough, Peterborough, Ontario L.B. Pearson Theatre, Brampton, Ontario
Hire Chris now!


To add Trading Economics data to your algorithm use the AddData()
system to request the data. As with all datasets, you should
save a reference to your symbol for easy use later in your algorithm. For detailed documentation on using custom data
see Importing Custom Data.
// U.S. Federal Reserve interest rate var interestRate = AddData<TradingEconomicsCalendar>(TradingEconomics.Calendar.UnitedStates.InterestRate).Symbol;
# U.S. Federal Reserve interest rate interestRate = self.AddData(TradingEconomicsCalendar, TradingEconomics.Calendar.UnitedStates.InterestRate).Symbol
Data can be accessed via Slice events. Slice delivers unique events to your algorithm as they happen. We recommend saving the symbol object when you add the data for easy access to slice later. Data is available in daily resolution. You can see an example of the slice accessor in the code below.
using QuantConnect.Data.Custom.TradingEconomics; namespace QuantConnect.Algorithm.CSharp { public class TradingEconomicsAlgorithm : QCAlgorithm { public override void Initialize() { SetStartDate(2019, 1, 1); // U.S. Federal Reserve interest rate AddData<TradingEconomicsCalendar>(TradingEconomics.Calendar.UnitedStates.InterestRate); } public override void OnData(Slice data) { // Accessing via Slice event var interestRate = data.Get<TradingEconomicsCalendar>(); foreach (var rate in interestRate.Values) { Log($"Unique calendar ID used by Trading Economics: {rate.CalendarId}, Release time and date in UTC: {rate.EndTime}, Country name: {rate.Country}, Indicator category name: {rate.Category}, Specific event name in the calendar: {rate.Event}"); } } } }
from QuantConnect.Data.Custom.TradingEconomics import * class TradingEconomicsAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2019, 1, 1) # U.S. Federal Reserve interest rate self.AddData(TradingEconomicsCalendar, TradingEconomics.Calendar.UnitedStates.InterestRate) def OnData(self, data): # Accessing via Slice event interestRate = data.Get(TradingEconomicsCalendar) for rate in interestRate.Values: self.Log(f"Unique calendar ID used by Trading Economics: {rate.CalendarId}, Release time and date in UTC: {rate.EndTime}, Country name: {rate.Country}, Indicator category name: {rate.Category}, Specific event name in the calendar: {rate.Event}")
All custom data has the properties Time
, Symbol
, and Value
.


Welcome
Angular & Bootstrap Demo
Status
First Name
Last Name
Email
Add user!
first name is required!
form = {{user}}
response = {{msg}}


The written content for this lesson has not been uploaded yet.
The whole lesson is free to download. Use the link below to start the download (6MB).
DOWNLOAD


DOUBLE JEOPARDY RATING: 6 / 10 --> Barely recommendable
For more reviews and movie screensavers, visit http://www.joblo.com/
Okay, so you've all read my plot point by plot point "prediction" before I went to see this movie (If you didn't, see the bottom of this review), which I basically compiled to bring attention to the fact that the marketing on this film, was one of the worst that I have ever seen! A trailer is supposed to entice a viewer to want to see a movie, it is not supposed to give away every single meaningful plot point! Having said that, I still went to see this film, and confirmed the fact that I would have enjoyed it much more, were I not to have seen their crummy giveaway trailer.
PLOT: Woman gets convicted of killing her husband and spends six years in jail. While in prison, this woman finds out that her husband is still alive and well, and living under an assumed name with her son. Once out of prison, this woman makes it her mission in life to find her "dead" husband and get her son back. Double jeopardy refers to the fact that one cannot be tried for the same murder twice, so in her case, she could now kill her husband and get away with it.
CRITIQUE: A "video movie" is a film which doesn't have enough oomph to recommend as a "going out of the house and to a theater night", but can suffice as a lazy evening of lounging around in the apartment. This film is exactly that type of film. It offers very little thrills for a thriller, close to zero suspense, considering that everything is already given away in the stupid-ass trailer, and a couple of bogus action scenes. It did however supply us with an Ashley Judd in her acting best, Tommy Lee Jones in his acting the same, and an extremely interesting premise, which had many women foaming at the mouth (Count Mrs. JoBlo as a guily party). Another good thing about this film was the pacing which was pretty fast. Considering that a lot of stuff actually does transpire, we are thankfully led through many of the basic scenes at a rapid pace, in order to get to the meat of the story, which basically has Judd chasing her "dead" husband.
The biggest problem with this film is that it offers very little in excitement and more importantly, very little in surprises or thrills. There were also a few plot holes here and there, which I can generally forgive in cheezy thrillers, and certain implausibilities, but all in all, they didn't really gnaw at me as much as the fact that I definitely would have enjoyed this film so much more, had I not seen Paramount's inane movie trailer. I am still recommending this film because on that level, and the level I speak of concerns cheese and lazy Sunday afternoons and videotapes, it can be deemed as an enjoyable trip through known territory. For others, you may want to see this flick because Ashley Judd is in it, and she is damn cute and a fine little actress in her own right.
Little Known Facts about this film and its stars: In reality, the legal theory of "double jeopardy" DOES NOT exist. According to Harvard Law School professor, and infamous member of O.J. Simpson legal "dream team", Alan Dershowitz, "The crime is killing a person at a certain time and place, and if she killed him at a different time or place, it would be a different crime" (Entertainment Weekly, Sept. 24, 1999).  As many of you may already know, Ashley Judd's sister and mom are the country singers known as The Judds. Ashley has also made quite a reputation of dating celebrities including actors Robert DeNiro and Matthew McConaughey, and singing has-been, Michael Bolton. Here's another fact which I think most people know by now, but if you didn't, then know full well that actor Tommy Lee Jones once roomed with Vice-President Al Gore during his College days at Harvard, and has never taken an acting class. In 1994, he co-starred with Ashley Judd in another picture called NATURAL BORN KILLERS (8/10), but unfortunately for the Judd, her court room scenes were left on the cutting room floor. Much of this film was shot in British Columbia, Canada, which may explain the presence of Canadian actor Bruce Greenwood in one of the main roles. Bruce is born in the province of Quebec, Canada. Director Bruce Beresford is born in Australia and once directed an Oscar winning picture called DRIVING MISS DAISY in 1989.
Here is my plot point by plot point prediction which I made BEFORE seeing this film, just by checking out the film's trailer.
1. Husband, Wife, Kid = happy family 2. Boating trip, husband appears to have been killed by wife 3. Wife gets nailed on murder, goes to jail 4. While in jail, finds out that husband is still alive, feels set-up 5. Gets out of jail, wanting her son back, and realizing that she can now kill her husband, because she can't be convicted twice for the same murder 6. Tommy Lee Jones, snoop extraordinaire, finds out about this and goes to stop her 7. He catches her at one point, but she floors the gas on his car on a ferry and escapes 8. After much research and woop-dee-do, Judd finds her husband's whereabouts 9. She finally explains the whole story to Jones, who now sympathizes with her plight 10. They both find the husband, and she confronts him with a gun and threatens his life, with Jones watching on. She wants her son back.
Review Date: September 25, 1999 Director: Bruce Beresford Writers: David Weisberg and Douglas Cook Producer: Leonard Goldberg Actors: Ashley Judd as Elizabeth Parsons Tommy Lee Jones as Parole Officer Bruce Greenwood as Nick Genre: Thriller Year of Release: 1999 --------------------------------------- JoBlo's Movie Emporium http://www.joblo.com --------------------------------------- (c) 1999 Berge Garabedian
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Next: Rule of Float Underflow and Overflow, Previous: Examples of Rule of Float and Rational Contagion, Up: Floating-point Computations
Computations with floats are only approximate, although they are described as if the results were mathematically accurate. Two mathematically identical expressions may be computationally different because of errors inherent in the floating-point approximation process. The precision of a float is not necessarily correlated with the accuracy of that number. For instance, 3.142857142857142857 is a more precise approximation to \pi than 3.14159, but the latter is more accurate. The precision refers to the number of bits retained in the representation. When an operation combines a short float with a long float, the result will be a long float. Common Lisp functions assume that the accuracy of arguments to them does not exceed their precision. Therefore when two small floats are combined, the result is a small float. Common Lisp functions never convert automatically from a larger size to a smaller one.


homura.callbacks package¶
Submodules¶
homura.callbacks.base module¶
-
class
homura.callbacks.base.
CallbackList
(*callbacks)[source]¶ Bases:
homura.callbacks.base.Callback
Combine some callbacks
- Parameters
callbacks – callbacks
homura.callbacks.metrics module¶
-
class
homura.callbacks.metrics.
AccuracyCallback
(k: int = 1)[source]¶ Bases:
homura.callbacks.metrics.MetricCallback
Callback for accuracy
- Parameters
k – report top-k accuracy
-
class
homura.callbacks.metrics.
IOUCallback
[source]¶ Bases:
homura.callbacks.metrics.MetricCallback
Callback for IOU (classwise and mean IOU)
-
class
homura.callbacks.metrics.
LossCallback
[source]¶ Bases:
homura.callbacks.metrics.MetricCallback
Callback for loss function
-
class
homura.callbacks.metrics.
MetricCallback
(metric: Callable[[collections.abc.Mapping], Any], name: str, logger=None, reduction='average', no_reduce: bool = False)[source]¶ Bases:
homura.callbacks.base.Callback
Base class of MetricCallback class such as AccuracyCallback
- Parameters
metric – metric function: (data) -> float
name – name of the metric
logger –
no_reduce – skip reducing when distributed
reduction – reduction method after every epoch
-
property
history
¶ History of metric.
- Returns
dict of histories in {mode: [history]}
Using this property, history of metrics can be used after a training loop
>>> metric = ... >>> # training loop >>> final_result = metric.history["val"][-1]
homura.callbacks.saver module¶
-
class
homura.callbacks.saver.
WeightSave
(save_path: str, save_freq: int = 1)[source]¶ Bases:
homura.callbacks.base.Callback
Save weights after every epoch
- Parameters
save_path – path to be saved
save_freq – frequency of saving in epoch. If -1, saved by after_all.


JavaScript Basics I
Kay Ashaolu
JavaScript!
- A fully featured programming language
- Used to be only for the browser, used along with HTML and CSS
- Now used on web servers and for Application Program Interfaces (APIs)
- Great example of composability!
JavaScript: powering your pages
- JavaScript brings a website to life
- No need for a server to execute by the browser
- Pop-ups, drop down navigation
- Form validation, animation
- Application development
JavaScript: powering your servers
- JavaScript is used to power the servers that launch websites! (NodeJS)
- No longer considered a "play" language
JavaScript: powering your apis
-
API: Application Program Interface
- We will not cover this in this class
- JavaScript is not just reserved for building web pages
- JavaScript can power pure back end functionality
We will be using JavaScript
So let's learn some JavaScript
- We are going to use our browsers to run our JavaScript
What this is not
- This is not an exhaustive review of the JavaScript programming language
- One goal is to get you familiar with the features of the language
- Another goal is to give you the opportunity to ask questions
- The better you know JavaScript, the more you'll be able to build rich web applications
How to run JavaScript
- To link a JavaScript file to an HTML web page use the <script> tag
<script src="../js/script.js"></script>
Variables? Data types?
- A variable is an empty space holder for a future value
- There are a few types of values that JavaScript uses
- Numbers and Booleans (true or false)
- Strings or a sequence of characters
- Functions
- Arrays (lists of variables) and Objects
Variables
- Declare with let to scope correctly
- Weak, dynamic typing
let a = 3;
let b = 5;
let c = a + b;
Strings
let a = "hello";
let b = "world!";
let c = a + b;
Arrays (Lists)
let list = [2,3, "KAY"];
alert(list[2]);
Objects
let titles = {
info253: 'Web Arch',
info256: 'Applied NLP’
};
alert(titles.info253);
let schools = {
berkeley: {info253: 'Web Arch'},
stanford: {cs101: 'Intro CS’}
};
alert(schools.stanford.cs101);
But note
- We will go over all of these types through writing functions
- It is extremely important to be comfortable with writing and using functions
So what is a function?
- Think of a function as a black box, where you put things in (inputs) and out comes a result (output)
- It's a black box because you don't necessarily need to know how it's converting the inputs
- The parameters are the inputs, the return statement defines the output
What is a function?
Function Syntax
let add = function(a, b) {return a + b;}
let c = add(2, 7)
/* c is now 9 */
let arithmetic = {add: add, subtract: function(a, b) {return a - b;}};
let d = arithmetic.subtract(11, 4);
let e = arithmetic.add(0, 3);
/* d is now 7; e is now 3; */
Our first function
function helloWorld(first_name, last_name) {
let message = "Hello World " + first_name + " " + last_name;
return message;
}
let first = prompt("Enter your first name");
let last = prompt("Enter your last name");
let output = helloWorld(first, last);
alert(output);
What does this function do?
What did that function do?
- Declared a function that we put in two values or inputs: (first name and last name)
- The function declared a variable (message) that contains the string "Hello World [your first name] [your last name]"
- After it's done, it will produce one value or output: (the message) and return it
- We ask the user for their first and last name, and print out the result from the function
More to come
- Conditionals
- For and While loops
- Arrays
- Objects
For the non programmers here stay with me, learn this and it will empower you throughout this course and beyond


This table contains entries for ubiquitin for a few representative organisms in the UniProt database, as well as for a few of the enzymes involved in ubiquitinylation.† Links are provided to view the original UniProt protein entries, and their display in InterPro.
|
|
SPTR
|
Protein name |
Organism |
InterPro Domain Architecture |
InterPro Graphical Match |
Function |
|
Ubiquitin |
Ubiquitin |
Homo sapiens |
|
Involved in protein degradation, chromosome maintenance, gene expression
regulation, stress response, ribosome biogenesis |
||
|
|
Ubiquitin |
Caenorhabditis elegans
Nematode worm |
|
Involved
in protein degradation, chromosome maintenance, gene expression regulation,
stress response, ribosome biogenesis |
||
|
|
Ubiquitin |
Glycine max Soybean |
|
Involved
in protein degradation, chromosome maintenance, gene expression regulation,
stress response, ribosome biogenesis |
||
|
|
Ubiquitin |
Acetabularia cliftonii Green algae |
|
Involved
in protein degradation, chromosome maintenance, gene expression regulation,
stress response, ribosome biogenesis |
||
|
|
Ubiquitin |
Saccharomyces cerevisiae Bakerís yeast |
|
Involved
in protein degradation, chromosome maintenance, gene expression regulation,
stress response, ribosome biogenesis |
||
|
|
Ubiquitin |
Trypanosoma cruzi Trypanosome |
|
Involved in
protein degradation, chromosome maintenance, gene expression regulation,
stress response, ribosome biogenesis |
||
|
Ubiquitin‑activating
enzymes (E1) |
Ubiquitin‑activating
enzyme E1 |
Homo sapiens |
|
Activates
ubiquitin |
||
|
Ubiquitin‑activating enzyme E1 |
Triticum aestivum
Wheat |
|
Activates
ubiquitin |
|||
|
|
Ubiquitin‑activating
enzyme E1 |
Saccharomyces cerevisiae Bakerís yeast |
|
Activates
ubiquitin |
||
|
Ubiquitin‑conjugating enzymes (E2) |
Ubiquitin‑conjugating enzyme E2 A (EC <IP_ADDRESS>) |
Homo sapiens |
Catalyses attachment
of ubiquitin to proteins |
|||
|
|
Ubiquitin‑conjugating enzyme E2 C (EC <IP_ADDRESS>) |
Homo sapiens
|
Catalyses attachment
of ubiquitin to proteins |
|||
|
|
Ubiquitin‑conjugating enzyme E2 S (EC <IP_ADDRESS>) |
Homo sapiens |
|
Catalyses attachment
of ubiquitin to proteins |
||
|
|
Ubiquitin‑conjugating enzyme E2-25 kDa (EC <IP_ADDRESS>) |
Homo sapiens |
|
Catalyses attachment
of ubiquitin to proteins |
||
|
Ubiquitin‑ligase (E3) |
(RING-type) |
CBL E3
ubiquitin protein ligase (EC 6.3.2.-) |
Homo sapiens
|
|
Accepts ubiquitin from specific E2 proteins. Participates in signal transduction in hematopoietic cells |
|
|
|
(RING-type) |
Ubiquitin-protein
ligase E3 Mdm2 |
Homo
sapiens |
|
Accepts
ubiquitin from specific E2 proteins.†
Acts on p53 tumour-suppressor. |
|
|
|
(HECT-type) |
Ubiquitin‑conjugating
enzyme E3A (EC 6.3.2.-) |
Homo sapiens |
|
Accepts ubiquitin from specific E2 proteins. Targets p53 tumour-suppressor protein for
ubiquitin-mediated proteolysis |
|
|
|
(HECT-type) |
HECT domain and RCC1-like domain protein 3 |
Homo sapiens |
|
Accepts
ubiquitin from specific E2 proteins. |
|
|
|
(U-box-type) |
RING finger
protein 37 |
Homo
sapiens |
|
Accepts ubiquitin from specific E2 proteins. Interacts with UBE2L3 |
|
|
|
(PHD-type) |
F-box/LRR-repeat protein 11 |
Homo
sapiens |
|
Promotes ubiquitination and degradation of some phosphorylated
proteins |
|
|
Ubiquitin‑elongation enzymes (E4) |
Ubiquitin
conjugation factor E4 A |
Homo sapiens |
|
Binds ubiquitin moieties and catalyses ubiquitin
chain assembly |
||
|
|
Ubiquitin
conjugation factor E4 B |
Homo sapiens |
|
Binds ubiquitin moieties and catalyses ubiquitin
chain assembly |


Squinty, the Comical Pig
by Richard Barnum
Chapter 4: “Squinty Gets Home”
Additional Information
- Year Published: 1915
- Language: English
- Country of Origin: United States of America
- Source: Barnum, R. (1915). Squinty, the comical pig. New York: Barse and Hopkins.
-
Readability:
- Flesch–Kincaid Level: 3.5
- Word Count: 2,731
- Genre: Fantasy
- Keywords: children's stories
- ✎ Cite This
Downloads
The rows of corn, in the field where Squinty the comical pig was lost, were like the streets of a city. They were very straight and even, just like the street where your house is, and, if you liked, you could pretend that each hill of corn was a house.
Perhaps Squinty pretended this, if pigs ever do pretend. At any rate the little lost pig wandered up and down in the rows of corn, peering this way and that, to see which way to go so he could get home again. He began to think that running away was not so much fun as he had at first thought.
“Oh dear!” Squinty grunted, in his funny, squealing voice. “I wonder if I’ll ever see my mamma and papa again?”
Squinty ran this way and that up and down the rows of corn, and you can easily imagine what happened. He soon became very tired. “I think I will take a rest,” thought Squinty, talking to himself, because there was no one else to whom he could speak. I think the little pig would have been very glad, just then, to speak even to Don, the dog. But Don was not there.
Squinty, wondering what happened to little pigs when they were lost, and if they ever got home again, stretched out on the dirt between two rows of corn. It was shady there, but over-head the hot sun was shining.
But the earth was rather cool, and Squinty liked it. He would much rather have been down by the cool brook, but he knew he could not have a swim in it until he found it. And, just now, he seemed a good way off from it.
Poor Squinty!  And, more than this, there was nothing to eat.
Squinty had tried to nibble at some of the green corn stalks, but he did not like the taste of them. Perhaps he had not yet learned to like them, for I have seen older pigs eat corn stalks. And pigs are very fond of the yellow corn itself. They love to gnaw it off the cob, and chew it, just as you chew popcorn.
But the corn was not yet ripe, and Squinty was too little to have eaten it, if it had been ripe. Later on he would learn to do this. Just now he cared more about finding his way home, and also finding something that he could eat.
For some time the little lost pig rested on the cool earth, in the shade of the rows of corn. Then he got up with a grunt and a squeal, and began rooting in the ground.
“Perhaps I may find some potatoes, or some pig weed, here,” thought Squinty. “Who knows?”
But all he could root up, with his queer, rubbery nose, was some round stones. Some of these were brown, and looked so much like the little potatoes, that Squinty tried to chew one. But when he felt the hard stone on his little white teeth he cried out in pain.
“Ouch!” squealed Squinty. “That hurt! Those are funny potatoes! I never knew they could be so hard.”
Later on he learned that what he supposed were potatoes were only stones. You see it takes a little pig some time to learn all the things he needs to know.
Squinty let the stone roll out of his mouth, and he looked at it with such an odd look on his face, peering at it with his squinty eye, and with one ear cocked up sort of sideways, that, if you had seen him, you could not have helped laughing. No one could, if they had seen Squinty then, but there was no one in the field to watch him.
“Well,” thought Squinty, after a bit, “this will never do. I can’t stay here. I must try to find my way back home. Let me see; what had I better do? I guess the first thing is to find that field of real potatoes, and not the make-believe ones like this,” and he pushed the stone away with his nose.
“When I find the potato field,” he went on, still talking to himself, “I am sure I can find the brook where I had a swim. And when I find the brook I will know my way home, for there is a straight path from there to our pen.”
So Squinty started off once more to walk through the rows of corn. As he walked along on his little short legs he grunted, and rooted in the earth with his nose. Sometimes he stumbled over a big stone, or a clod of dirt, and fell down.
“Oh dear!” exclaimed poor Squinty, when he got up after falling down about six times, “Oh dear! This is no fun. I wish I had stayed in the pen with my brothers and sisters. I wonder what they are doing now?”
Just then Squinty felt more hungry than ever, and he thought it must be feeding-time back in the pen.
“Oh, they must be having some nice sour milk just now!” thought Squinty. “How I wish I were back with them!”
And then, as he fancied he could smell the nice sour milk, which the farmer or his wife was pouring into the eating trough of the pen, Squinty just howled and squealed with hunger.
Then this gave him an idea.
“Ha!” he exclaimed to himself, in a way pigs have, “why didn’t I think of that before? I must squeal for help. My mamma, or papa, may hear me and come for me.”
Then Squinty happened to think that the hole, by which he had gotten out of the pen, was not large enough for his fat papa or mamma to crawl through.
“No, they can’t get out to come for me,” Squinty thought. “They’ll have to send Wuff-Wuff, or Squealer. And maybe they’ll get lost, the same as I did. Oh dear, I guess I won’t squeal any more. It’s bad enough for me to be lost, without any of my brothers or sisters getting lost, too.”
So Squinty stopped squealing, and walked on and on between the rows of corn, trying to find his way home to the pen all by himself. Squinty was really quite a brave pig, wasn’t he?
By this time, as you can well believe, Mr. and Mrs. Pig, in the pen, had awakened from their afternoon sleep. And all the little pigs had awakened too, for they were beginning to feel hungry again.
“Isn’t it about time the farmer came with some sour milk for us?” asked Mr. Pig of Mrs. Pig.
“I think it is,” she said, looking up at the sun, for the sun is the only clock that pigs, and other animals, have. When they see the sun in the east, low down, they know it is morning. When it shines directly over their heads, high in the sky, they know it is noon. And when the sun sinks down in the west the pigs know it is getting toward night, and supper time.
The sun was low down in the west now, and Mr. and Mrs. Pig knew it must be nearly time for their evening meal.
“Come, Wuff-Wuff. Come, Squealer. Come, Squinty, and all the rest of you!” called Mrs. Pig in her grunting voice. “Come, get ready for supper. I think I hear the farmer coming with the nice sour milk!”
“Squee! Squee! Squee!” squealed all the little pigs, for they were very hungry indeed. “Squee! Squee! Squee!”
They all made a rush to see who would get to the eating trough first. Some of them even put their feet in, they were so anxious. Pigs are always that way. They know no better, so we must excuse them. If they had been taught not to do that, and then did it, we would not excuse them.
“Here comes the farmer with the sour milk,” grunted Mr. Pig. “Oh, how good it smells!”
Just then Squealer cried:
“Why, where’s Squinty?”
His brothers and sisters looked around.
Squinty, the comical pig, was not to be seen. But we know where he was, even if his mamma and papa and brothers and sisters did not. Squinty was in the cornfield, trying to find his way back to the pen.
“Why, where can Squinty be?” asked Mrs. Pig. “Squinty! Squinty!” she called, grunting and squealing as she always did. “Come to the trough!” she went on. “Supper is ready!”
But Squinty did not come. The farmer poured the sour milk down the slide, where it ran into the trough, and the little pigs began to eat. But Mr. and Mrs. Pig began looking for Squinty. They turned up the straw, thinking he might be asleep under it. No Squinty was to be seen. Then Mr. Pig saw the hole under the side boards of the pen.
“Ha!” exclaimed Mr. Pig, speaking to Mrs. Pig, “I think perhaps Squinty went out there.”
“Oh, so he did!” said Mrs. Pig. “What shall we do?”
Just then the farmer looked over in the pen to see how fat the pigs were getting. He counted the little pigs.
“Hello!” he exclaimed. “Only six here! One of those pigs has gotten out. I must look into this!”
Quickly he glanced all about the pen. He saw the hole out of which Squinty had run away.
“I thought so!” exclaimed the farmer. “One of the pigs has rooted his way out. I’ll have to go after him. Here, Don!” he called to his dog. “A pig is loose! We must catch him!” and he whistled for the big black and white dog, who ran up, barking and leaping about.
At first Squinty’s brothers and sisters were paying so much attention to drinking their sour milk, that they did not notice what the farmer said, even though they missed Squinty at the trough. But when they heard the dog barking, they wondered what had happened. Then they saw their mamma and papa looking anxious, and talking together in their grunting language, and Wuff-Wuff asked:
“Has anything happened?”
“Squinty is lost!” said Mrs. Pig, rubbing her nose up against that of Curly Tail, the littlest girl pig of them all. “He must have run out of the pen when we were asleep.”
“Oh dear!” cried all the little pigs, and they felt very badly.
“Never mind,” said Mr. Pig, “I heard the farmer call Don, the dog, to go off and find Squinty. I think he’ll bring him back.”
“Oh, but maybe Don will bite Squinty,” said Wuff-Wuff.
“I guess not,” answered Mr. Pig. “Don is a gentle dog. But, anyhow, we want Squinty back, and the only way we can get him is to have the farmer and his dog go after him.”

“Bow wow! Bow wow! Bow wow!” barked Don. That meant, in dog language, “I’ll find Squinty and bring him back.”
Meanwhile Squinty had tried his best to find a way out of the cornfield. But all he did was to walk up one row, and down another. If he had been tall enough to stand up and look over the tops of the corn stalks, he might have seen which way to go, but he was not yet large enough for that.
Pretty soon Squinty looked up, and he saw that the sun was not as bright as it had been. Squinty knew what this meant. The sun was going down, and it would soon be night.
“Oh dear! I wonder if I shall have to stay out all alone in the dark night,” thought poor Squinty. “Oh, I’ll never run away again; never!”
Just then he heard, off through the rows of corn, a dog barking.
“Bow wow! Bow wow! Bow wow!” went the dog.
“Oh, what shall I do? Where shall I hide?” thought Squinty. “A bad dog is after me.”
He ran this way and that, stumbling and falling down. The barking of the dog sounded nearer. Then Squinty heard a man’s voice saying:
“Get after him, Don! Find him! Find that pig!”
“Bow wow!” was the barking answer.
“Ha!” thought Squinty. “Don! That’s the name of the good dog on our farm! I wonder if he is coming after me?”
Just then the farmer, who had been following the tracks left in the soft ground by Squinty’s feet, came to the cornfield. The farmer saw where the pig had been walking between the green rows of corn.
“He’s here, somewhere, Don,” the farmer said. “Find him!”
“Bow wow!” barked Don. “I will!”
Just then Squinty stumbled over a big stone, and he could not help grunting. He also gave a little squeal.
“Here he is, Don!” called the farmer. “Take him by the ear, and lead him back to the pen. Easy, now!”
Squinty stood still. He did not want to run away from Don. Squinty was only too anxious to be found, and taken home.
The next minute, through the rows of corn, came bounding Don, the dog. He was followed by the farmer.
“Ah, there he is! The little runaway!” cried the farmer man as he saw the pig. “After him, Don! But don’t hurt him!”
Don raced up beside Squinty, and took him gently by the ear.
“Bow wow!” barked the dog, and that meant: “Come along with me, if you please. You have been away from your pen quite long enough.”

“I must make that pig pen a great deal tighter, if they are going to get out and run away every day,” said the farmer to himself, as he walked along behind Don and Squinty.
 The farmer picked the little pink fellow, now all tired out and covered with dirt, up in his arms and dropped him down inside the pen with the other pigs.
“There!” cried the farmer. “I guess you’ll stay in after this.”
“Bow wow!” barked Don, jumping about, for he thought it was fun to chase runaway pigs.
And so Squinty got safely back home. But very soon he was to have some more adventures.


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Class: Aws::Firehose::Types::CopyCommand
| 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-firehose/lib/aws-sdk-firehose/types.rb |
Overview
Describes a COPY
command for Amazon Redshift.
Constant Summary
Instance Attribute Summary
-
#copy_options ⇒ String
rw
Optional parameters to use with the Amazon Redshift
COPY
command. -
#data_table_columns ⇒ String
rw
A comma-separated list of column names.
-
#data_table_name ⇒ String
rw
The name of the target table.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#copy_options ⇒ String
(rw)
Optional parameters to use with the Amazon Redshift COPY
command.
For more information, see the "Optional Parameters" section of
Amazon ::Aws::Redshift
COPY command. Some possible examples that would
apply to ::Aws::Kinesis
Data ::Aws::Firehose
are as follows:
delimiter '\t' lzop;
- fields are delimited with "\t" (TAB
character) and compressed using lzop.
delimiter '|'
- fields are delimited with "|" (this is the
default delimiter).
delimiter '|' escape
- the delimiter should be escaped.
fixedwidth
'venueid:3,venuename:25,venuecity:12,venuestate:2,venueseats:6'
-
fields are fixed width in the source, with each width specified
after every column in the table.
JSON 's3://mybucket/jsonpaths.txt'
- data is in JSON format, and
the path specified is the format of the data.
For more examples, see Amazon ::Aws::Redshift
COPY command examples.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 682
class CopyCommand < Struct.new( :data_table_name, :data_table_columns, : ) SENSITIVE = [] include Aws::Structure end
#data_table_columns ⇒ String
(rw)
A comma-separated list of column names.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 682
class CopyCommand < Struct.new( :data_table_name, :data_table_columns, : ) SENSITIVE = [] include Aws::Structure end
#data_table_name ⇒ String
(rw)
The name of the target table. The table must already exist in the database.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 682
class CopyCommand < Struct.new( :data_table_name, :data_table_columns, : ) SENSITIVE = [] include Aws::Structure end


When to use help
Only use help to explain things like business processes. It shouldn't be relied upon to solve design problems - services should always be simple and intuitive (service stand point 12). Contextual help is more suited to complex internal services.
An example of authentication
How to use help
Help should be easy to close, move and minimise until the user needs it.
If contextual help doesn’t answer a user’s question, links to more comprehensive help should be easy to find.
GDS has guidance for help content.
User research
Users find side-by-side guidance more helpful and easier to use.
Small overlay windows offer the smallest interference with the users flow
This is a good article on contextual help from Nielsen Norman
Services using this pattern
- Application platform
- Entities
Related patterns
Get in touch
If you’ve got a question or suggestion share it on Slack #interaction-designers channel or on GitHub.


CARLITO'S WAY A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating (Linear 0 to 10): 5.5
Date Released: 11/12/93 Running Length: 2:20 Rated: R (Violence, language, nudity)
Starring: Al Pacino, Sean Penn, Penelope Ann Miller, John Leguizumo Director: Brian DePalma Producers: Martin Bregman, Will Baer, and Michael S. Bregman Screenplay: David Koepp based on the novels CARLITO'S WAY and AFTER HOURS by Justice Edwin Torres Music: Patrick Doyle Released by Universal Pictures
The first scene of CARLITO'S WAY is in black-and-white, and features a close-up of a bearded Al Pacino as someone puts three bullets into him. He slides to the ground and, as the stretcher arrives to wheel him away for medical care, he begins a mental monologue that forms the running narrative for the movie--the story of how Carlito Briganti (Pacino) came to be in this unenviable position.
It is 1975 in New York City, and Carlito has just been released from prison as the result of a clever appeal by his good friend and lawyer David Kleinfeld (Sean Penn). After delivering an impassioned speech to the court about how he now intends to go clean, the former assassin and "J.P. Morgan of the smack business" returns to his Puerto Rican neighborhood to run a disco club with the aim of legitimately earning the $75,000 he needs to buy into a rental car business in the Bahamas.  Balancing the negative influences in Carlito's life is his girlfriend Gail (Penelope Ann Miller), one of the few people he truly trusts.
Brian DePalma has been on a skid recently. Although several years in the past, his disastrous version of THE BONFIRE OF THE VANITIES is still fresh in the minds of everyone unfortunate enough to have endured it, and his "return" to the thriller genre with RAISING CAIN wasn't a whole lot more promising. CARLITO'S WAY is a step up, but this is not the same DePalma who got the adrenaline moving with THE UNTOUCHABLES.
CARLITO'S WAY has a number of superbly-directed action sequences, and there are moments of absolute brilliance, but these are connected by a plodding narrative that sputters to find an appropriate pace. Two hours and twenty minutes is too long for this motion picture; there are numerous scenes that could have found their way to the cutting room floor without sacrificing much of the story. DePalma, however, appears unwilling to have cut anything with Pacino in it (the shooting script was nearly identical to the finished product).
The last half-hour has the electricity of any good thriller, except for one little problem: we already know the ending. Some films (NO WAY OUT comes to mind) are aided by the method employed here: starting at the end, then going back and telling the bulk of the story as a flashback. CARLITO'S WAY is not one of these. By knowing that Carlito is going to be shot (not to mention how it's going to happen), it drains away suspense from the big chase through Grand Central Station. In fact, it's amazing that DePalma manages to raise the finale to the level of excitement he achieves.
Presenting CARLITO'S WAY in this curious fashion is apparently motivated by a desire to have Al Pacino narrate the story. This is a bad decision in more than one way, since the voice-overs are largely redundant, consisting of observations like "I had a bad feeling about this" or "Bad move," most of which the viewer can figure out on his or her own.
Coming off of his virtuoso performance in SCENT OF A WOMAN, one expects a lot from Pacino, and he really doesn't deliver. His portrayal of Carlito is competent, but not masterful. While it's helpful to remember that a below-par offering from Pacino is often better than an outstanding job by many other actors, that doesn't prevent the viewer from comparing this to SCARFACE, SERPICO, SCENT, AND JUSTICE FOR ALL, and THE GODFATHERs. Pacino fails to invest Carlito with much energy, and there are times when the performance is flat.
Also, it becomes something of a challenge to identify Pacino's constantly-shifting accent. Sometimes it sounds Puerto Rican (which it's supposed to be), but there are moments when it strays towards Italian or is marked by a leftover Southern strain from SCENT OF A WOMAN. There are not, however, any "Hoo-haa"s.
Perhaps the biggest surprise with respect to the cast is that Pacino gets out-acted by Sean Penn, who hardly looks like himself with frizzy hair and nerdy glasses. Penn plays an exceptionally annoying lawyer who spends most of his time snorting cocaine, and he does an excellent job in this low-key, but critical, role.
For the most part, Penelope Ann Miller is "just there." Her Gail is perhaps too sweet to work in a strip joint and be the boyfriend of a gangster; it isn't until one of her last scenes when we see a harder edge, and even then her performance verges on histrionics. Gail's love is supposed to represent Carlito's redemption, and the reason he thinks dreams are worth having, but I'm not sure the connection between them is strong enough for this to come across.
 The movie is based on two novels (CARLITO'S WAY and AFTER HOURS) by New York Justice Edwin Torres, one of the city's harshest judges (in sentencing one criminal, Torres informed him that "your parole officer hasn't been born yet"). Torres writes from his experience of what passes through his courtroom, so parts of CARLITO'S WAY are based, however loosely, on reality.
Technically, CARLITO'S WAY is a combination of the innovative and the banal. The camerawork is invigorating, if sometimes a bit too exotic. DePalma makes good use of the steadicam during the chase sequences, and this heightens whatever tension is present. Jellybean Benitez, a former DJ and club manager, is the music supervisor, and his choice of about a dozen mid-seventies hits helps to establish the time-frame. Patrick Doyle's score, however, is horribly out-of-place. The composer may do exceptional work for Kenneth Branagh, but his other movie music isn't particularly noteworthy.
CARLITO'S WAY probably should have been a taut thriller, but choices by DePalma in both presentation and editing have hamstrung it. With the expectations for this film as high as they were, the impact should have been more forceful, and the focus better tuned.
- James Berardinelli<EMAIL_ADDRESS>.
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