Oral statement to Parliament
Reform of police stop and search powers
- From:
- Home Office and The Rt Hon Theresa May MP
- Published:
- 30 April 2014
- Delivered on:
Statement of proposals to reduce the use of stop and search, make it more intelligence-led and improve stop-to-arrest ratios.
With permission, Mr Speaker, I would like to make a statement about the use of stop and search powers by the police.
As I have told the House before, I have long been concerned about the use of stop and search. While it is undoubtedly an important police power, when it is misused stop and search can be counter-productive. First, it can be an enormous waste of police time. Second, when innocent people are stopped and searched for no good reason, it is hugely damaging to the relationship between the police and the public. In those circumstances it is an unacceptable affront to justice.
That is why I commissioned Her Majesty’s Inspectorate of Constabulary to inspect every force in England and Wales to see how stop and search powers are used. And it is why, last year, I launched a consultation to make sure members of the public – particularly young people and people from minority ethnic communities – could have their say.
Today I am publishing a summary of the responses to the consultation and placing a copy in the House Library. The consultation generated more than 5,000 responses, and it was striking that those on the receiving end of stop and search had very different attitudes to those who are not. While 76% of people aged between 55 and 74 thought stop and search powers are effective, only 38% of people aged between 18 and 24 agreed. While 66% of white people thought stop and search powers are effective, only 38% of black people agreed.
The findings of the HMIC inspection were deeply concerning. The inspectorate reported that 27% of the stop and search records they examined did not contain reasonable grounds to search people, even though many of these records had been endorsed by supervising officers. If the HMIC sample is accurate, that means more than a quarter of the one million or so stops carried out under the Police and Criminal Evidence Act last year could have been illegal.
This is not the only worrying statistic. Official figures show that if you are black or from a minority ethnic background, you are up to seven times more likely to be stopped and searched by the police than if you are white, and only about ten per cent of stops result in an arrest.
In London, thanks to the leadership of Sir Bernard Hogan-Howe, changes to stop and search show that it is possible to reduce the number of stops, improve the stop-to-arrest ratio and still cut crime. Since February 2012, the Metropolitan Police have reduced their overall use of stop and search by 20%. They have reduced no-suspicion stop and search by 90%. In the same period, stabbings have fallen by a third and shootings by forty per cent. Complaints against the police have gone down and the arrest ratio has improved.
I want to see further progress in London and across the whole of England and Wales. So I can tell the House that I intend to revise the Police and Criminal Evidence Act Code of Practice A to make clear what constitutes “reasonable grounds for suspicion” – the legal basis upon which police officers carry out the vast majority of stops.
The revised code will emphasise that where officers are not using their powers properly they will be subject to formal performance or disciplinary proceedings.
HMIC’s study into the use of stop and search revealed that more than half the police forces in the country are ignoring the requirement set out in the Police and Criminal Evidence Act Code of Practice A to make arrangements for public scrutiny of stop-and-search records. This is an important duty that should empower local communities to hold police forces to account, and so I have written to all chief constables and police and crime commissioners to tell them to adhere to the code. I have told them that if they do not do so, the government will bring forward legislation to make this a statutory requirement.
Earlier today I commissioned Alex Marshall, the chief executive of the College of Policing, to review the national training of stop and search with a view to developing robust professional standards for officers on probation, existing officers, supervisors and police leaders. I have asked the College to include in this work unconscious bias awareness training to reduce the possibility of prejudice informing officers’ decisions.
As part of that review, I have also asked the College to introduce an assessment of officers’ fitness to use stop and search powers. I want this to send the clearest possible message – if officers do not pass this assessment, if they do not understand the law or they do not show they know how to use stop and search powers appropriately, they will not be allowed to use them.
In order to save as much police time as possible, I have asked my officials in the Home Office to work with chief constables and police and crime commissioners to explore the possibility of recording information on the use of stop and search via the new Emergency Services Network.
In addition to all these changes, I can tell the House that, this summer, the Home Office and the College of Policing will launch a new ‘Best Use of Stop and Search’ scheme. This scheme already has the backing of the Metropolitan Police – the biggest user of stop and search in the country – and today I have written to all other police forces in England and Wales inviting them to sign up.
Forces participating in the scheme will record the outcome of stops in more detail to show the link – or the lack of a link – between the object of the search and its outcome. This will allow us to assess how well forces are interpreting the “reasonable grounds for suspicion” they are supposed to have to use their stop and search powers in accordance with law. The scheme will also require forces to record a broader range of outcomes, such as penalty notices for disorder and cautions. This will allow us to understand better how successful each stop and search really is.
In order to improve the public’s understanding of the police, forces participating in the scheme will introduce lay observation policies, which enable members of the local community to apply to accompany police officers on patrol.
The scheme will also require forces to introduce a stop and search complaints “community trigger” whereby the police must explain to the public how stop and search powers are being used where there is a large volume of complaints.
Forces participating in the scheme will make clear that they will respect the case law established in ‘Roberts’ by using no-suspicion stop and search when it is “necessary to prevent incidents involving serious violence” rather than just “expedient” to do so. They will raise the level of authorisation to a chief officer and that officer must reasonably believe that violence “will” take place rather than “may”, as things stand now. This will bring no-suspicion stop and search more into line with the stop and search powers under Section 47A of the Terrorism Act 2000, and I hope it will reduce the number of no-suspicion stops significantly.
The scheme will also require forces to limit the application of no-suspicion stop and search to fifteen hours. It will also require them to communicate with local communities in advance and afterwards, so residents can be kept informed of the purpose and success of the operation.
In addition to these changes, in order to improve transparency and accountability, we will add stop and search data to the government’s hugely successful and popular crime maps at www.police.uk. I have also asked Her Majesty’s Chief Inspector of Constabulary to include the use of stop and search in HMIC’s new annual general inspections which begin towards the end of this year. And I have commissioned HMIC to review all other police powers similar to stop and search – including Section 163 of the Road Traffic Act – with a view to eliminating any unfair or inappropriate use of those powers.
Mr Speaker, the proposals I have outlined today amount to a comprehensive package of reform. I believe that they should contribute to a significant reduction in the overall use of stop and search, better and more intelligence-led stop and search and improved stop-to-arrest ratios. But I want to make myself absolutely clear: if the numbers do not come down, if stop and search does not become more targeted, if those stop-to-arrest ratios do not improve considerably, the government will return with primary legislation to make these things happen.
Because, Mr Speaker, nobody wins when stop and search is misapplied. It is a waste of police time. It is unfair, especially to young, black men. It is bad for public confidence in the police. That is why these are the right reforms and it’s why I commend this statement to the House.


monoidalEstimator.Rmd
The causalRisk
package provides an estimator of the cumultative distribution of right-censored outcomes:
\[ \widehat{\Pr}(Y < t) = \frac{1}{n} \sum_{i = 1}^n \frac{\Delta_i I(Y_i < t)} { {\widehat{\Pr}} (\Delta_i = 1)} \]
where \(\Delta = I(Y < C)\) with a bit of algebra we can write this function as:
\[ h(t) = \frac{ \sum_i g(t)}{ \sum_i b } \]
where:
\[ g(t) = a_1(t) \cdot a_2(t) \]
\[ a_1(t) = I(Y_i < t)/1 \]
\[ a_2(t) = \Delta_i / \widehat{\Pr} (\Delta_i = 1) \]
\[ b = 1 \]
The monoidalEstimator
package provides a function make_estimator
that creates the functions g
and h
above from a list
of functions that create the a
terms and a single b
term. Each term is created curried function that takes in vs
(a list
of data elements) and returns a function
of whatever arguments are necessary (in this case t
) and ...
. For this particular estimator, assuming the data are sorted by min(Y, C)
, these could be written as:
a1 <- function(vs){ Y <- vs$V1 Y <- `if`(is.na(Y), Inf, Y) function(t, ...){ # (Y <= t) * 1 # Don't need because I *assume* data is sorted by time 1 } } a2 <- function(vs){ C <- vs$V2 Y <- vs$V1 # Handle two data structures: # 1) The one of V1 or V2 that is not missing is min(V1, V2) # 2) Both V1 and V2 are available Del <- `if`( is.na(Y) || is.na(C), c(TRUE, FALSE)[!is.na(c(Y, C))], Y < C) PrDel <- vs$PrDel function(...){ Del / PrDel } } b <- function(vs){ one } as <- list(a1, a2)
Since the estimator assumes the data is sorted, by accumulating the sum of the numerator (\(g(t)\)), we get an unscaled cumulative risk. Then by scaling those values by \sum_i b
, we get estimates at each subject’s time. The collector function accumsum_collectsum_seq
., accummlates its first argument and collects it second argument. The accumulation starts at the monoid’s unit (i.e. 0
for sum of numbers).
For example:
x <- replicate(3, list(function(...) { one() }, function(...) { one() }), simplify = FALSE) accumsum_collectsum_seq(x) #> [[1]] #> [1] 0 1 2 3 #> #> [[2]] #> [1] 3
Now we can make our estimator:
cumrisk <- make_estimator(as, b, collector = accumsum_collectsum_seq)
At this point, cumrisk
is a function
that returns a function
:
cumrisk #> function (vsl) #> { #> gvsl <- purrr::map(vsl, ~list(aFUN(vs = .x), bFUN(vs = .x))) #> function(...) { #> post(Reduce(`/`, collector(fs = gvsl, ...))) #> } #> } #> <bytecode: 0x7f808290afd0> #> <environment: 0x7f808290de40>
In order to use the estimator, we need to bind data to it. Here, I use the example1
data from the causalRisk
package.
dt <- select( causalRisk::example1, V1 = Death, V2 = EndofEnrollment ) %>% mutate( Ymin = pmin(V1, V2, na.rm = TRUE), del = !is.na(V1) ) %>% arrange(Ymin) m <- summary(survfit(Surv(Ymin, !del) ~ 1, data = dt), censored = TRUE) dt$PrDel <- m$surv[match(dt$Ymin, m$time)] dt <- dt %>% mutate(PrDel = if_else(is.na(PrDel), lag(PrDel), PrDel))
Now, we bind the data (remember it needs to be a list
of list
s).
crisk <- cumrisk(purrr::transpose(dt))
Now, we can evaluate our estimator at different times:
res <- dt %>% mutate(crisk = crisk()[-1]) monoid_res <- filter(res, del) km <- summary(survfit(Surv(Ymin, del) ~ 1, data = dt)) km_res <- tibble(!!! km[c("time", "surv")]) %>% mutate( crisk = 1 - surv ) %>% select(Ymin = time, crisk) all.equal(select(monoid_res, Ymin, crisk), km_res) #> [1] "Component \"crisk\": Mean relative difference: 3.515722e-08"
causalRisk
One can run this locally if you have causalRisk
installed.
library(causalRisk) #> Loading required package: ggplot2 #> Loading required package: splines #> #> Attaching package: 'causalRisk' #> The following object is masked from 'package:survival': #> #> leukemia models = specify_models(identify_censoring(EndofEnrollment), identify_outcome(Death)) #> Registered S3 method overwritten by 'pryr': #> method from #> print.bytes Rcpp fit1 = estimate_ipwrisk(example1, models, # times = seq(0,24,0.1), labels = c("Overall cumulative risk"))
stype
library(survival) library(stype) ctimes <- list( v_event_time(c(5, 6, 10, NA_integer_, 1, NA_integer_, 19), internal_name = "cA"), v_event_time(c(4, 1, 15, NA_integer_, NA_integer_, NA_integer_, 21), internal_name = "cB") ) otimes <- list( v_event_time(c(2, 6, 11, 12, NA_integer_, NA_integer_, 25), internal_name = "oA"), v_event_time(c(1, NA_integer_, 10, NA_integer_, NA_integer_, NA_integer_, 23), internal_name = "oB") ) x1 <- v_rcensored(outcomes = otimes, censors = ctimes, end_time = 15)


My Education
I am now a second-year master student at University of Michigan, Ann Arbor.
University of Michigan, Ann Arbor, School of Information
August 2020 - April 2022
M.S. in Information, focusing on Data Science
University of Michigan-Shanghai Jiao Tong University Joint Institute
September 2017 - August 2021
B.S.E in Electrical and Computer Engineering, minor in Data Science
Technical University of Berlin
January 2019 - February 2019
Winter program, exchange student
High School Affiliated to Fudan University
September 2014 - June 2017
Senior high school diploma
Shanghai Huayu Private Middle School
September 2010 - June 2014
Junior high school diploma


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{% extends "base_tabs.html" %} {% load i18n %} {% load bootstrap3 %} {% block head_title %}Reimbursement{% endblock %} {% block panel %} {% if reimbursement.can_submit_receipt %} {% include 'include/r_status.html' %}
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Sensors serve as powerful devices for measuring our environment. In this project, we generated predictive models for weather conditions with data captured with indoor sensors.
Find Out MoreIn this project, we like to address the question of whether we can predict outdoor weather conditions, based on indoor sensor measurements. Specifically, we are interested in two sub-questions:
The weather is critically important in the lives of many people, and dramatically impact a wide range of economic and human activities. For instance, people are generally less likely to perform outdoor activities when it is freezing and rainy outdoors, than if the weather is nice and sunny. On this note, the measurement of weather conditions is critically important to help guide different human activities.
The miniaturization of mobile devices has led to an emerging Internet of Things (IoT) revolution. Specifically, a wide range of devices, including sensors used in the detection of weather conditions, can now be widely and readily installed across multiple locations. Given that these sensors can be connected via the internet, such devices enable in principle real time measurements of weather conditions.
However, many of these sensors make use of sensitive electronics that will degrade when directly exposed to different elements of the weather. As such, the use of these devices are largely limited to indoor settings, which may prohibit the accurate measurement of outdoor weather conditions. Thus, given the limitations of these sensors to largely indoor settings, we are interested in whether it will be possible to predict outdoor weather conditions based on measurements from these indoor sensors.
In this project, we like to address the question of whether we can predict outdoor weather conditions, based on indoor sensor measurements, we also obtained the following weather data from weather stations. We have installed sensors to measure various environmental attributes in two different locations in Boston. Specifically, the sensors were installed in (A) the dormitory room of one of our groupmates, and (B) the attic of another groupmate of our ours. The sensors were installed to capture the following environmental attributes over numerous days at 1 second interval:
Sensor measurements (Predictors): (i) Temperature, (ii) Humidity, (iii) Pressure, (iv) Light, (v) Motion, (vi) Location of sensor, (vii) Time of measurement
Weather Conditions (Response): (i) Temperature in Boston, (ii) Sunrise, sunset timings, (iii) Wind speed, (iv) Wind direction, (v) Amount of precipitation, (vi) Cloudy or notWe performed initial exploration of the data obtained from the sensors.These plots are depicted in Figure 1 and Figure 2 for sensors A and B respectively.
Figure 1 Measurements from sensor A. (Left panels) Raw data from the sensor, (middle panels) data after cleaning, (right panels) autocorrelation of the data. The top to bottom panel represents data from humidity, light, motion, pressure and temperature data collected from the sensor.
Figure 2 Measurements from sensor B. (Left panels) Raw data from the sensor, (middle panels) data after cleaning, (right panels) autocorrelation of the data. The top to bottom panel represents data from humidity, light, motion, pressure and temperature data collected from the sensor.
We can see from the plot (Figure 1 and 2) that there are clear discordant points in the Humidity, Light, Motion and Pressure data represented by regular sharp spikes, but none in Temperature. Thus, our goal was to clean the data as thoroughly as possible without losing any signal. First, we checked if the discordant points were truly periodic. Unfortunately, while they appear periodic on the plot - this is only due to scale. The points are slightly off periodic and differ between each sensor. As such, each predictor must be cleaned separately. We thus applied the following data cleaning strategies for each of the following data:
1. Light & Motion were cleaned by eliminating points outside a bound. For “Light” we ignore points larger than 4095 12-bit scale specified in the sensor manual. For “Motion” we ignore points that are greater than 1 (since the motion predictor is binary).
2. Pressure was also cleaned by eliminating points outside a bound, but in this case the parameters of the sensor is insufficient. Instead we eliminate results below 500hPa. This is about half of typical atmospheric pressure on earth, and far below the record low pressure recorded on earth 850 hPa, in a tornado measured by Tim Samaras in South Dakota.
3. Humidity was the most difficult, since the discordant points are plausible values for an observed humidity. To deal with this we take the finite difference between consecutive observations and remove those whose change is not within 3 standard deviations of the average change. (Sigma clip). The 3 sigma setting is somewhat arbitrary but works well on our test data sets.
3.4.1 Certain sensor measurements correlate better with ground truth weather measurements.
From Figure 3, we see that the sensor measurements for pressure seems to fit and track the ground truth pressure measurements collected from the weather stations for both Sensor A and Sensor B, which were placed in the room of a dormitory and an attic respectively. On the other hand, temperature measurements and humidity from our indoor sensors did not track the outdoor weather conditions as well. Notably, as expected, we found that the sensor placed in the attic was more similar to the measurements reported by the weather station, as compared to the sensor placed in the dormitory room which is highly affected by human activity.
3.4.2 Autocorrelation in data implies possibility to do weather forecasting
The weather condition and/or sensor measurement at a particular time point might be highly correlated with that found at a later time point. If this is indeed true, it might allow us to predict future weather conditions (e.g. using sensor measurements at 2pm, to predict weather conditions at 3pm). To assess if it might be possible for us to forecast future weather conditions, based on current sensor measurements, we calculated the auto-correlation of the current sensor measurement with sensor measurements staggered by a timestep, using the pandas.Series.autocor function. The autocorrelation is defined as the normalized Pearson correlation coefficient between time series values and their lags:
Notably, we see a gradual decay in correlation of the data at delta_t increases (Figure 1 and 2). This indicates that data that is closer to the current time is more correlated than data that is further in time. Interesting, there is a periodic pattern in autocorrelation of light data for sensor B (Figure 2). Further, the autocorrelation in the data implies that forecasting of the weather conditions might be possible.
Figure 3 A summary of all the data collected as of Nov 17. Sensor A (sensor in dormitory room) is in blue and Sensor B (sensor in attic) is in red. The lines show various measurements made at Boston-Logan airport that we might try to predict. It appears that our Humidity and Pressure measurements are highly correlated, but the temperature seems more weakly correlated. Boston Logan did not provide any good analogs for our Light or Pressure measurements.
For ARIMA description and understanding of Autoregression based algorithms.
Jason Brownlee - https://machinelearningmastery.com/sarima-for-time-series-forecasting-in-python/
For code examples and some error metrics
Raphael Larrosa - https://towardsdatascience.com/how-to-forecast-sales-with-python-using-sarima-model-ba600992fa7d
A bit more on the ARIMA process
Jason Brownlee - https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
On the mathematics behind ARIMA.
Introduction to ARIMA: nonseasonal models
Fuqua School of Business, Duke University -
https://people.duke.edu/~rnau/411arim.htm
TensorFlow RNN introduction and tutorial
https://www.tensorflow.org/guide/keras/rnn
Zaytar, Mohamed Akram, and Chaker El Amrani. "Sequence to sequence weather forecasting with long short-term memory recurrent neural networks." International Journal of Computer Applications 143.11 (2016): 7-11.
Chen, Changsong, et al. "Online 24-h solar power forecasting based on weather type classification using artificial neural network." Solar energy 85.11 (2011): 2856-2870.
5.1 Baseline model
5.1.1 Preparing data for modelling
To begin creating a model between our Sensor data and the ground truth from Boston Logan airport we need to get them on the same scale. Our data is taken with a 1s cadence, whereas the Boston Logan data is taken slightly off each hour - but not every hour.
To deal with the 1 second cadence from our sensor we bin data taken within an hour (to conform with the Boston Logan cadence) into a mean and standard deviation - this effectively gives us an hourly measure of the average (mean) and variance (std) of a given parameter over the hour long block. The variance measure allows us to take advantage of our high cadence - by giving some insight into the variation over a block - rather than just considering it as a single mean; this may help prediction in the future.
The Boston-Logan data was highly non-uniform in cadence. Some observations were taken on the hour, some were taken 6minutes before the hour. To deal with this we simply rounded to the nearest hour and removed any times that were repeated, this could result in a small offset between our sensor data and the Boston Logan ground truth, however; since this offset is much smaller than the cadence and weather is highly correlated on short timescales we don’t expect this to be much of an issue. The Boston Logan pressure data was converted to pascals from inHg and the temperature data was converted from Fahrenheit to Celsius.
After this we created two data frames for each sensor. One is our sensor measurements (our predictors) and the other ground truth for times that were recorded both by the sensor and by Boston Logan.
5.1.2 Modeling of preliminary data
As a preliminary analysis, we used simple linear regression to model the dry-bulb temperature, station pressure, and relataive humidity measured at the National Weather Service station at Boston Logan airport (KBOS) based on the preliminary measurements of one of our devices that were taken between 11/1 and 11/13. For each of the three values of interest, two different kinds of linear regression models were used. For the first kind of model, the training set (80:20 train:test split) was randomly distributed over all times. For the second kind of model, data measured after 11/9 was used to train a model and tested on data measured prior to 11/3. The later-time measurements were used as the training set rather than the early-time measurements because the late-time set of data is larger. Although a proper “forecast” would use early-time measurements to predict later weather values, general knowledge about how time-separation between our training and test sets affects the accuracy of our model will give us an idea of how capable we are of making predictions about future weather values. The test-set values of the six models are given below:
For the models with the training set randomly distributed over all times:
for temperature: 0.76
for pressure: 0.996
for humidity: 0.74
For models trained on “late-time” data and tested against “early-time” data:
for temperature: -4.13
for pressure: 0.99
for humidity: -0.5
About RNN modeling
As the time series data are highly auto-correlated, this provides a way to do forecasting.
Figure 3 A summary of all the data collected as of Nov 17. Sensor A (sensor in dormitory room) is in blue and Sensor B (sensor in attic) is in red. The lines show various measurements made at Boston-Logan airport that we might try to predict. It appears that our Humidity and Pressure measurements are highly correlated, but the temperature seems more weakly correlated. Boston Logan did not provide any good analogs for our Light or Pressure measurements.
Based on this very preliminary modelling, we are able to contemporaneously predict external weather conditions based on indoor measurements, but with the data we have obtained and using linear regression models we cannot make predictions about weather that is 1 week time-separated from the data our model is trained on.
5.2 Subsequent modelling strategy and expansion on preliminary results
From the preliminary modelling efforts we find that we are able to predict contemporaneous external weather phenomena from the results of indoor measurements. However, the failure of an initial linear regression model to forecast weather at times that are far separated from the time period over which the model was trained highlights the need for more sophisticated modelling techniques to achieve this goal. Thus, the strategy that we pursued for subsequent modelling is as follows:
6.1 Predicting the contemporaneous outdoor temperature from indoor measurements
In this section, we train several different types of models to predict the outdoor dry-bulb temperature from contemporaneous indoor measurements taken over the whole month of November. Two different sensors were used for the indoor measurements. 'Sensor A' was left to record in one of our dorm rooms, while 'Sensor B' was left to record in an infrequently-used attic next to a window. We will attempt to build models to predict the outdoor dry-bulb temperature from each of these sensors separately.
The relevant variables for the models and analyses in this section are:
Model features:
`time`: the date and time of the sample. For each variable corresponding to sensor measurements we average hourly. We feature engineer the `time` data by creating Fourier components `time_sin` and `time_cos`, which themselves are used as predictors for modelling.
`motion_mean`: the average value of the motion measurements obtained over that hour.
`light_mean`: the average value of the light measurements obtained over that hour (NOTE: high values of `light_mean` correspond to LOW light levels).
`pressure_mean`: the average value of the pressure measurements obtained over that hour.
`temperature_mean`: the average value of the sensors' temperature measurements obtained over that hour.
`humidity_mean`: the average value of humidity measurements obtained over that hour.
Model response:
`HourlyDryBulbTemperature`: the outdoor dry-bulb temperature measured at the Boston Logan National Weather Service station (KBOS), measured hourly. Throughout this section we will refer to this variable as the 'outdoor temperature'.
6.1.1: Linear Regression Models
We begin by training simple linear regression models to the data obtained from each sensor and calculating the $R^2$ values for the training and test sets. We standardize the data so that the magnitudes of the coefficients can be compared to one another as a metric of feature importance.
Performance of linear regression models:
Sensor A:
R^2 on training set: 0.49
R^2 on test set: 0.57
Sensor B:
R^2 on training set: 0.70
R^2 on test set: 0.68
We see that our simple linear regression models for sensor A performs better on the test set than the training set, while the opposite is true for sensor B. It appears that the model based on measurements by sensor B performs better than the model based on measurements by sensor A. This is expected, because sensor B was kept in a more isolated environment than sensor A.
We then compare the coefficient values for the features in these linear regression models. Based on this analysis, `humidity_mean` is the most important feature for the sensor A model while `temperature_mean` is the most important feature for the sensor B model. Since sensor B was kept next to a window, we would indeed expect the temperature measurements of sensor B to more accurately reflect the outdoor temperature and as a result the coefficient for `temperature_mean` should be very high. Since sensor A is kept within a dorm room far from a window, its temperature measurements will not reflect the outdoor temperature nearly as well and its model must depend more on other variables that correlate with the outdoor temperature. The most important coefficient for sensor A is apparently `humidity_mean`, which positively correlates with the outdoor temperature. This is consistent with the coefficient values for sensor B, which also detects a positive correlation between `humidity_mean` and the outdoor temperature.
We also detect a sizeable negative coefficient value for `motion_mean` in the sensor A linear regression model, while the magnitude of the `motion_mean` coefficient is negligible for sensor B. This is consistent with the occupants of the dorm room spending more time inside when it is cold. Since sensor B is in an attic which is infrequently visited by people, it rarely detects significant motion and thus would not register any relationship between outdoor temperature and motion.
It should be noted that high values of `light_mean` correspond to darkness, whereas low values correspond to bright conditions. For sensor B we see a significant negative value of the coefficient for `light_mean`, which indicates that darkness is positively associated with low outdoor temperatures. This is of course reasonable, as it gets colder at night. Sensor A, on the other hand, has a positive value of this coefficient, indicating that its `light_mean` data is negatively associated with the outdoor temperature. This is also reasonable: during the day (when the outdoor temperature is high) no one is in the room and the lights are off since everyone is at work or in class, whereas at night the indoor lights are on and the occupant is at home--presumably staying up all night working on AC209a problem sets.
Sensor A also has a sizeable negative `time_cos` coefficient, whereas the `time_cos` and `time_sin` coefficients for sensor B are both relatively large and negative. This is intuitive; when these respective variables are positive it is either late at night or early in the morning, when it is expected to be coldest.
6.1.2 Polynomial regression (order 2) with pairwise interaction terms and LASSO regularization
Next, we train 2nd-order polynomial models with all pairwise interaction terms to the measaurement data of each sensor in order to study the effects of interactions between our features. We also employ LASSO regularization for feature selection, cross-validating on the regularization penalty. We also standardize all variables prior to calculating the quadratic and interaction terms.
Performance of linear regression models:
Sensor A:
R^2 on training set: 0.61
R^2 on test set: 0.61
Sensor B:
R^2 on training set: 0.78
R^2 on test set: 0.79
We see that the regularized polynomial models with interactions do indeed perform a bit better than the simple linear regression models for each of the sensors.
We plot the coefficient values for each feature in these models. We see that LASSO regularization has set many of the coefficients to zero, particularly for sensor B. This makes sense, because sensor B's `temperature_mean` is such a strong predictor for outdoor temperature given its location near a window that the space of other predictors can be made very sparse without sacrificing much accuracy. For the linear terms we see the same breakdown in most important feature for the two sensors as we did for the simple linear regression models--sensor A's most important feature is still `humidity_mean`, while sensor B's most important feature is still `temperature_mean`. Interestingly, for sensor B `pressure_mean` is now very negatively associated with outdoor temperature, whereas the model for sensor A features a small positive correlation. We also still see the negative association between outdoor temperature and `motion_mean` for sensor A. Additionally, both Fourier components for time have negative values for both sensors, corresponding to early morning generally being the coldest time of day.
For both sensors we see a sizeable negative association between `pressure_mean^2` and the outdoor temperature. This, combined with the fact that Sensor B has a strongly negative coefficient for `pressure_mean`, indicates that the outside temperature decreases more strongly with increasing `pressure_mean` than it increases with decreasing `pressure_mean`.
We also detect a sizeable negative association between the interaction term `temperature_mean*humidity_mean` and the outdoor temperature for sensor B. Since both `temperature_mean` and `humidity_mean` are the two most important linear features, it is not unexpected that their interaction term would also be quite significant. The fact that these coefficients are negative but the corresponding linear-term coefficients are very positive indicates that when both `temperature_mean` and `humidity_mean` are high, the outdoor temperature does not increase as quickly with these variables as it would if only one of these variables were high.
For the interaction term `temperature_mean*pressure_mean`, we see that sensor A's coefficient is positive whereas sensor B's coefficient is negative. This is the only interaction term which is both significant for both sensors but opposite in sign. It should be noted that sensor B's negative value of the coefficient of `pressure_mean` is counterintuitive because barometric pressure and temperature are usually positively associated--hotter air generally has a higher barometric pressure, barring other weather phenomena.
6.1.3 Bagging models
We now try to improve our prediction accuracy by bagging with regression trees. We begin by cross-validating on the depth of trees in the ensemble. The CV score as a function of tree depth for both sensor A and sensor B bagging models saturates by a tree depth of 15, so we will use this value for the depth of the trees in an ensemble of 100.
Performance of linear regression models:
Sensor A:
R^2 on training set: 0.97
R^2 on test set: 0.84
Sensor B:
R^2 on training set: 0.98
R^2 on test set: 0.85
For the bagging model with a tree depth of 15 we obtain much higher test-set $R^2$ values for both sensors.
From the variable importance plot above we see that the most important variable for Sensor A is `humidity_mean` while the most important variable for Sensor B is `temperature_mean`. This breakdown agrees well with the variable importance analysis of the previous regression models. We also see that Sensor A has a strong reliance on `pressure_mean` and the Fourier components `time_sin` and `time_cos` for accurately determining the outdoor temperature. As we stated previously, the temperature measurements of sensor A will reflect the outdoor temperature much less well than those for sensor B since it is not near a window. Thus, the model for sensor A must rely relatively more on variables that correlate with the outdoor temperature. In general, humidity, barometric pressure, and time of day do just that.
Given that the bagging models performed the best of all three models we have considered for predicting the contemporaneous outdoor temperature, we will now use them to predict on both the training and test sets:
6.2 RNN modeling for weather forecast
A recurrent neural network (RNN) is a type of neural network where the output from the previous step are fed as input to the current step in order to predict am sequence of data. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. It can be used in weather forecasting, stock market prediction, handwritten recognition, speech recognition, music composition, etc.where the data are ordered in sequence. Long short-term memory (LSTM) networks are a special kind of RNN, which are capable of learning long-term dependencies.
In the notebook, we use LSTM to demonstrate preliminary weather forecasting, trained on three different datasets: (1) Boston Logan Airport Weather Station Data, (2) Data collected by CS109A sensor A, (3) Data collected by CS109A sensor B. Below is an example of predicting temperature 12 hours into the future based on 72 hours history (each time step corresponds to one hour). More prediction plots are in the notebook.
The advantage of RNN is it can learn special patterns in a sequence and predict a series of points one by one. The corresponding downside is that RNN error increases as the number of prediction points increases. This is because if the first point is not predicted correctly, the error will be taken to the second point prediction, and the second point error will be taken to later point prediction, such that the error gets amplified. To study this, we train RNN models separately to predict different hours into the future, and record the prediction errors. The result is plotted here:
As can be seen, the error increases monotonically as we increase the number of hours to predict. This is in contrast with the SARIMA model, as it can handle long-time prediction.
6.3 SARIMA modeling
SARIMA is a more general version of the ARIMA model (Autoregressive Integrated Moving Average). ARIMA in turn stands for Auto-Regressive Integrated Moving average. It works basically by differencing the time series until it becomes stationary. Then predicts over that series by a combination of auto-regressive methods and moving average. Stationary here means that the series joint probability distribution does not change with time. I.e. a stationary series variations about the mean are constant and have no trend.
SARIMA It is more general in the sense that it can accommodate seasonal (periodic) variations. Whereas ARIMA expects non-seasonal input or input with the seasonal component removed. SARIMAX has a total of 7 free parameters. It inherits the p,d,q parameters from ARIMA (trend autoregression order, trend difference order, and the trend moving average order) and adds four more : sp, sd, sq & f, which are respectively the seasonal autoregressive order, seasonal difference order, the seasonal moving average order and the number of timesteps for a given seasonal period.
In our modeling, we’ve fixed the frequency to be 24. This is natural since our data is sampled every hour and many of our predictors (light, temperature, humidity etc.) will likely also fluctuate on a daily timescale. We treated the number of terms as hyperparameters and simply compared many different values in a grid search to find the best model. To judge the quality of each model we aim to minimize the AIC score defined to be: AIC = . Where k is the number of estimated parameters in the model and L is the maximum likelihood. As such we can account both for the risk of overfitting, (the presence of k is a penalizes of overfit models) and the risk of underfitting (by minimizing AIC).
While the AIC is useful for discriminating between other models with AIC values to properly compare with the RNN forecasting we also compute the maximum error as a function of time.
One of the advantages of this SARIMA for time series prediction is its presumption of periodicity. This ensures our predictions remain relatively reasonable even over long timescales. The disadvantage is lower order fits have difficulty capturing nuances in the behavior, whereas higher order fits take quite a while to run. Ideally one would like to conduct a grid search on the entire space with many different orders, but that is computationally costly.
Thus, our modeling we vary the trend parameters among 0th,1st, and 2nd order and do the same with the seasonal orders, but we keep the order of each parameter seasonal parameter and trend parameter the same within each run. (i.e we compare the cartesian product of ({[0,0,0],[1,1,1],[2,2,2]} with {[0,0,0],[1,1,1],[2,2,2]}). In principle varying each parameter individually would be more rigorous, but since SARIMA models take quite a while to run, imposing the individual parameters must be the same cuts down on the parameter space while allowing us to still sample from a few higher order models.
For forecasting the temperature we get, for sensors A, B, and the Boston Logan station are respectively:
Here the shaded areas are 95% confidence bounds. We see that we recover periodic variations in the Sensor B model, but the temperature recorded by Sensor A is best fit by a constant. This makes sense since Sensor A was kept in a dorm room and sensor B was kept in an attic, largely without human presence. While the model does very well for the two sensors, it fails to capture the recent downward trend in temperature shown by the weather station. This makes sense since we’ve limited the order of the terms rather stringently, higher order terms might help improve the fit. For more direct comparison with the RNN modeling we also show the absolute error versus time, again for sensors A, B and Boston Logan respectively:
As expected, since SARIMA works of a fundamentally periodic model, the residuals don’t grow very strongly with time. This also makes it ideal for modeling variations in the light from the two sensors. As usual for Sensor A and B:
The room sensor A was left in was largely darkened for the period, so SARIMA simply predicts a constant. Whereas the room containing sensor B had a window, and so the sensor picked up the sunrise and sunset times. SARIMA forecasts this very nicely.
The forecasts for Humidity and Pressure were near constant for both Sensors A and B, which seems reasonable given the data.
Sensor A and B Humidity:
Sensor A and B Pressure:
As mentioned above, the SARIMA models have significant room for improvement given more rigorous searches and longer computational times.
We believe the SARIMA models in particular have significant room for improvement given more rigorous searches and longer computation times. In addition to the previously described grid search we also conducted a deeper search rigorously checking all combinations of parameters for select parameters. Each such run takes about 12hrs per feature so it was impractical to do this for the entire dataset. The following is an example of such a result predicted for the Sensor A temperature data. It was obtained using the orders (2,1,2) for the trend component and (2,1,2) for the seasonal (since the orders are different it’s an example of a model that wouldn’t be found by our less rigours search). We can see in this fit SARIMA attempts to fit the periodicity in the downward temperature spikes. (Caused by going to bed with the windows open). It manages to understand the periodicity in the spikes, but fails to predict when the occur location, therefore it predicts temperature fluctuations where none exist in some cases.
This is a good example of how our results could potentially be further improved given more time and computational resources.
In this project, we set out to address if it might be possible to predict outdoor weather conditions using indoor sensor measurements. Specifically, we are interested in whether indoor sensor measurements can determine outdoor weather conditions. Further, we're interested in whether the sensor measurements can help us forecast future weather conditions.
In our project, we show that certain weather conditions like pressure and humidity are quite straight forward to measure indoors and show good concordance with outdoor measurements, though this did not apply for temperature. As such, we developed a series of models to predict outdoor temperature. Eventually, we settled upon a bagging model which showed good prediction accuracies. Interestingly, sensor temperature and humidity were more important for predicting outdoor temperatures for sensor B (attic) and Sensor A (dorm room) respectively
To forecast weather conditions, we applied RNN and SARIMA time series models. Despite numerous attemps, we found that these models did not achieve great prediction accuracy, with fairly high mean absolute errors especially when we try to predict further into the future. This might reflect the highly stochastic nature of weather which makes weather forecasting a generally difficult endeavour. By further improving our models, we expect to achieve better predictions. Thus, this is something which we strive to do in the future by further enhancing, tuning, and training of our forecasting models
Overall, we have achieved our goal in this project to predict outdoor weather conditions based on indoor sensor measurements.
This project was developed for the AC209A (2019 Fall) project at Harvard University. Members of the group (Group #14) are:
Ryan Duncan
Richard Zachary Murray
Kar-Tong Tan
Ziwei Qiu


The following table defines important Algorithm Framework terminology:
| Term | Description |
|---|---|
| Universe Selection model | A framework module that selects assets for your algorithm. |
| Alpha model | A framework module that generates trading signals on the assets in your universe. |
| Insight | An object that represents a trading signal. The Alpha model generates these objects for the Portfolio Construction model. |
| Portfolio Construction model | A framework module that determines position size targets based on the Insight objects it receives from the Alpha model. |
| PortfolioTarget | An object that represents the target position size for an asset in the universe. The Portfolio Construction model generates these objects for the Risk Management model. |
| Risk Management | A framework module that manages market risks and ensures the algorithm remains within target parameters. This model adjusts the PortfolioTarget objects it receives from the Portfolio Construction model before they reach the Execution model. |
| Execution | A framework module that places trades to reach the target risk-adjust portfolio based on the PortfolioTarget objects it receives from the Risk Management model. |


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Elton John forced Robbie Williams into rehab after he arrived drunk at his home
Robbie Williams revealed Elton John forced him into rehab after he turned up at the Rocketman’s home covered in wine stains.
The Angels hitmaker appeared on The Jonathan Ross Show where he celebrated being sober for 19 years.
And while discussing his past addiction, the 45-year-old confessed the Honky Cat singer had a hand in getting him to rehab.
Chatting with the host, Robbie said: ‘He really tried to help. In the early days when I was first getting sober, there was a week until I had to go to rehab and I had two vocals to do on my first album, one of them was Angels and one of them was Let Me Entertain You.
‘I was going to do these vocals then go to Rehab. Elton invited me to his house to listen to the work that I’d done so far.
‘That morning I got in a black cab and I had this game that I used to play where I’d stop at every pub I saw and have half a pint.
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‘I’d had ten half pints before I’d got to the studio. I ended up under the mixing desk, had a bit of red wine and then I knew I had to get to Elton’s for three o’clock.’
The rocker made it to Elton’s house looking worse for wear with wine stains down his shirt.
And when opening the door, the now 72-year-old told the star that he had to get to rehab, with Robbie agreeing with the icon, saying: ‘I’ve got to go to Rehab now’.
He confessed this exchange went on for a while at the door before Elont and his husband David Furnish put him up for a night at their home in Windsor.
‘I wake up but I haven’t opened my eyes and there’s a knock on my shoulder and I look and there’s… Elton and David Furnish and a doctor and a psychiatrist and they are here to take me away.’
Praising the You Song hitmaker, Robbie continued: ‘Elton is the most loving, generous man you could imagine and he has helped so many people over the years.’
Elton has also battled addiction, and recently celebrated 29-years of being sober.
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At the time he confessed: ’29 years ago today, I was a broken man.
‘Thank-you to all the selfless people who have helped me on my journey through sobriety. I am eternally grateful.’
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


Academic Qualifications
- Graduated with a Bachelor of Commerce Degree from H. R. College, Mumbai in 1982.
- Graduated with a Master of Business Administration from Babson College, Wellesley, U.S.A. in 1985.
Work Experience and Profile
Co-founded Godrej Foods Limited (a joint venture between The Great Eastern Shipping Co. Ltd. and Godrej Soaps Ltd.) and worked as Vice-President (Finance) from 1985 to 1989.
Joined The Great Eastern Shipping Co. Ltd. on July 1, 1990 and is currently its Executive Director. The Great Eastern Shipping Co. Ltd. is India's largest private sector shipping company, owning and operating a fleet comprising crude and product tankers, gas carrier and bulk carriers. For the Financial Year ended March 31, 2008, The Great Eastern Shipping Co. Ltd. recorded revenue of Rs.3204 crores and net profits of Rs. 1357 crores. It was rated as Asia's 3rd most profitable company by Forbes Asia, 200, best under a billion.
Presently, the Managing Director of Greatship (India) Limited, a wholly owned subsidiary of The Great Eastern Shipping Co. Ltd., with a projected capital outlay in excess of US$ 900 million.
He is a strategic investor in companies with good governance and robust revenue model. He has been instrumental in identifying potential growth stories with sound business fundamentals.
Other Directorships
- The Great Eastern Shipping Co. Ltd.
- Greatship (India) Limited
- Financial Technologies (India) Ltd.
- A. H. Bhiwandiwalla Investments Pvt. Ltd.
- Artex India Pvt. Ltd.
- Greatship Global Holdings Ltd.
- Greatship DOF Subsea Projects Pvt. Ltd.


| DOSE: |
1 tablet |
oral |
MDMA |
(pill / tablet) |
A few monts ago I decided that I wanted to try extasy, should the opportunity present itself. I wouldn't go searching, but I would be open to it. My boyfriend had a week off this week, and suggested that he get some pills and we have an art day. I made arrangements so that I could disappear for the day and turn off the phone. (I've discovered with pot that I internalize alot, and didn't want to be thinking about ANYTHING else other than the experience.)
He insisted that I research experiences on the erowid vaults, and I was really glad I did. My nerves were calmed by having some sort of an idea of what to expect.
I took the pill at 10:00AM and we decided to go get coffee and sit and chill for a while. I was STARVING and ordered a bagel (despite the warnings that I wouldn't want it in another 15 minutes). We were sitting on couches, chatting, watching people and I kept waiting. But it wasn't anxious, it was just a soft feeling of 'I wonder when it'll happen'.
I kept looking at the people, and all of a sudden I knew it was coming. (This was about 10:30) A few seconds later I felt a washing feeling of warmth over my body. I felt a slight vertigo as if I was falling gently backward in a semi circle. But it wasn't a surprising feeling at all, I felt that the feeling was extremely familiar. He asked to see my eyes and asked how I felt, and I was aware that I felt GOOD and very giggly. Just happy and giddy and REALLY excited to be sitting on a big comfy couch. He put his arm around me and just rubbed my hands and my hair. It felt so content and mellow. And I sure didn't want that bagel. I got really cold at one point, but a sweater cured that.
After a little bit he suggested that we go somewhere else. Which was good because I was VERY stuck on that couch. As soon as we were outside I was excited to be moving on the street, as the movement felt more like floating and really fulfilling. But once again I felt that this was a place I had been before...I have felt that excited at life before, but this time it seeemed like it was more pure.
Then he pointed out a siren and I stopped in the middle of the street and listened and looked at him, and I felt as if we were the only two people on earth and it was spinning around us.  I just wanted to stop and be there together...I wanted him to be where I was at but I felt that he wasn't so we kept walking.
We walked but it was really hot, and so we decided that we should go somewhere. I really felt a strong desire to lay in the grass and people watch but he wasn't there with me. (We would've had to drive and find parking and it was way too hot anyway). I was bummed for about 3 seconds. I just didn't want to be in transit. I wanted to be somewhere. So we got in the car and I layed in the seat all the way back and he put on electronic music for me. (It all of a sudden made sense).
We went back to his house and just got naked, mostly for comfort. The skin to skin touch was amazing, We started playing with our bodies, and eventually started having sex. OH MY GOD. People supposedly say that sex and extasy don't mix. I totally disagree. Perhaps it's because I'm an extremely sexual person by nature, so to go to that place is very comfortable for me. We had sex continually for over 2 hours. It was amazing. I kept feeling like I would have an orgasm, but never did, but didnt' feel bummed about it....I just wanted to feel the way I was feeling. It was unihibited and animal but at the same time tender...Uhhhhhh. We had music on and at one time I realized that I was moving my body with the music, but not to the music. I wasn't even aware of it. It was as if the music came into my body and moved it around.
But every once in a while (and I think it was when I was rolling in) I would feel like I needed more than just physical affection, and really longed for him to be tender and loving. I would feel myself becoming a little preoccupied with being in a different place than he was, or feeling more intense feelings that he was. It was a softer version of the paranoia I get from pot, and instead of feeling nervous about it, I just felt aware of it. I just wanted to look in his eyes because the love I felt inside needed to be reciprocated to be realized. I would start to internalize but it didn't upset me. And I knew that emotions were running high because of the drug, so I didn't get too preoccupied with it. Then it would pass.
We smoked some pot and layed around. That really mellowed me out even more. It dulled my extreme feelings and made me totally content with touch and sleep. I kept drifting in and out of consciousness, and I just felt soooo relaxed. We eventually got up and showered although I was very hard to motivate. I felt like I wanted to be in the couch forever. (And I felt aware that on a regular basis I would get up if he said so just because, but i was too relaxed to care and definitely made him MAKE me get up.)
We went and picked up a friend and drove to our respective activities for the night. Was I mellow. It was rediculous. I was so zoned out, and really felt completely wiped out. But I drank a large coffee and went to my rehearsal. Now at this point it was definitely way over, but I still was very aware of those feelings. Not the physical affects, but the feelings. My rehearsal was very content and I loved talking to the people there that I didnt' really know very well. And it was a very touchy rehearsal, and I was still digging that. (Although I very much enjoy touch on a regular basis, but suddenly I felt very aware and unapologetic for it). Afterward, a friend and I sat outside and smoked some more pot, and it just felt good. But tired.
I woke up today and didn't feel bad. A few times yesterday toward the end I felt down, but it passed quickly. But I'm realizing that what seems to be most affecting me is my interactions with him. When I realized that I wouldn't see him all day, I got VERY down. But being very self aware and knowing that depression can follow, I'm not going to let that disappoint me. But what I've kept with me is the amazing way that I felt walking down the street, and talking to people, and that seems to keep picking me back up. And since its something I've felt before, I feel like I can definitely recall it without extasy. (That's not to say that I'm done with it.....)
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Version 0.7!
A new version has been released, featuring new player models and
some updated graphics. A few balance fixes have also been added
and minor improvements have been made to the patcher and lobby.
The warrior jumpstrike attack is still broken, a fix is soon
to be awaited.
Players should redownload the patcher from the above link.
//chili. - 2013-11-15
New Version
The version 6 has gone live sucessfully. Many bugfixes and improvements
have been incorporated. In this latest release the Patcher was updated,
it will now be more responsive. Small patches are planned for the lobby,
the lobby should soon send multiple attempts for connection and also
provide more feedback.
Big changes are planned for the game, a gamemode with scoring and round
winners. Mobs and bosses should be implemented, Player AI's should be
refined and tuned to scale better. Player AI's should also make use of
attributes, to make them really strong. The warrior AI is having a huge
nerf while we're buffing the thief AI and leaving the mage as is.
Players should redownload the patcher from the above link.
//chili. - 2013-09-01
More Shiny Stuff
More shiny stuff have been added. Arts such as a brand new playable
map, 15 fresh graphic effects. Player classes have been added, including
the Mage, Thief and Warrior. Each wielding the power of 5 unique abilities
and 3 basic abilities (basic attack, heal & haste). A MAP Editor was added
to the Toolset. Major changes and bugfixes including multiplayer support,
optimizations and Unicode support.
This version will come live week 20, The BETA Test will also begin
after this release has gone live, labeled as 'Beta 0.6', "EnterTheDungeon".
//chili. - 2013-05-14
Major Changes
Major changes has been committed. The suite was updated with
new tools: Game Lobby, Patcher & a KeyGen. Major changes to
game art, large engine improvements, art assets updated.
A Bandwidth of 3KB/s upstream is recommended (8 player).
A Bandwidth of 30KB/s downstream is recommended (8 player).
A Processor of 650Mhz Dual or 925Mhz Single
Integrated Graphics With Support for any of the
following, DirectX7, DX8, DX9, DX10, DX11 or OpenGL 2.0
The current release suffers from delays on network
play, this is intended UNTIL the post-BETA patch.
The delays will be disabled by flow reverse. ;-)
//chili. - 2013-05-08
First Version
The first version compiled for win32. All is running well.
This version is released as 'Pre-ALPHA 0.5', codename "JustGrass".
//chili. - 2013-04-30
Website Launched
The website was launched. - 2013-04-24
//chili.


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ENGR30003 Assignment 2 Feedback: yuxliang
Key: Grading for (R) Report, (C) Code
Rootfinding
- (R) Analytical solution for theta = 0 deg shown (0.5 for each beta) [1 mark]: 1
- (R) Graphical solution for M = 1.5 shown and discussion on change in beta [1 mark]: 1
- (R) Graphical solution for M = 5.0 shown and discussion on change in beta [1 mark]: 1
- (R) Initial guess for M = 5.0 and theta = 20 deg [0.5 marks]: 0.5
- (R) Graph for M = 5.0 and variable theta [0.5 marks]: 0.5
- (R) Graphs for variable M and variable theta [1.5 marks]: 1.5
- (C) Code implemented for M = 5.0 and theta = 20 deg [1.5 marks]: 1.5
- (C) Code extension for M = 5.0 and variable theta [1 marks]: 1
- (C) Code extension for variable M and variable theta [0.5 marks]: 0.5
- (C) Output the correct result to file [0.5 marks]: 0
Regression
- (R) Correct derivation [3 mark]: 3
- (R) Condition for regression fail [1 marks]: 1
Linear Algebraic Systems
- (R) Derivation shown for transformed matrix [2 marks]: 2
- (C) Implementation for solving using Thomas algorithm [2 marks]: 2
- (C) Output the correct result [1 marks]: 1
Interpolation
- (R) Solution for x = 4 for both splines [0.5 marks]:<0/b>
- (R) Plot of curve and discussion [1.5 mark]: 1.5
- (C) Cubic splines implementation [1 mark]: 1
- (C) Dynamic checking of spline intervals [0.5 mark]: 0.5
- (C) Output of result for x = 3 [0.5 marks]: 0.5
Differentiation, differential equations
- (R) Discussion on effect of varying grid spacing with graph [1 marks]: 1
- (R) Discussion on effect of varying CFL or timestep with graph [1 marks]: 1
- (R) Discussion on changing CFL to larger than 1 [1 marks]: 1
- (R) Plots for showing two Nx and 3 CFLs for two schemes (12 plots) [3 marks]: 3
- (C) Initial condition implementation [1 mark]: 1
- (C) Boundary condition implementation [1 mark]: 1
- (C) Upwind scheme implementation [2 marks]: 2
- (C) Central scheme implementation [2 marks]: 2
- (C) Output from both methods at out_iter [1 mark]: 1
Total marks:
34
Overall comments from marker:
hockwave code keeps iterating after M=8.0, printing NaN for 3 extra lines, with no M value
No value reported for interpolation at x=4
Assignment Marker: David Rodriguez
If you have any questions regarding your mark, please contact the lecturer and cc the tutor who graded your assignment


Contents
This is a design document about the innards of Ganeti’s job processing. Readers are advised to study previous design documents on the topic:
Some Ganeti operations want to execute as many operations in parallel as possible. Examples are evacuating or failing over a node (gnt-node evacuate/gnt-node failover). Without changing large parts of the code, e.g. the RPC layer, to be asynchronous, or using threads inside a logical unit, only a single operation can be executed at a time per job.
Currently clients work around this limitation by retrieving the list of desired targets and then re-submitting a number of jobs. This requires logic to be kept in the client, in some cases leading to duplication (e.g. CLI and RAPI).
The job queue lock is guaranteed to be released while executing an opcode/logical unit. This means an opcode can talk to the job queue and submit more jobs. It then receives the job IDs, like any job submitter using the LUXI interface would. These job IDs are returned to the client, who then will then proceed to wait for the jobs to finish.
Technically, the job queue already passes a number of callbacks to the opcode processor. These are used for giving user feedback, notifying the job queue of an opcode having gotten its locks, and checking whether the opcode has been cancelled. A new callback function is added to submit jobs. Its signature and result will be equivalent to the job queue’s existing SubmitManyJobs function.
Logical units can submit jobs by returning an instance of a special container class with a list of jobs, each of which is a list of opcodes (e.g. [[op1, op2], [op3]]). The opcode processor will recognize instances of the special class when used a return value and will submit the contained jobs. The submission status and job IDs returned by the submission callback are used as the opcode’s result. It should be encapsulated in a dictionary allowing for future extensions.
Example:
{
"jobs": [
(True, "8149"),
(True, "21019"),
(False, "Submission failed"),
(True, "31594"),
],
}
Job submissions can fail for variety of reasons, e.g. a full or drained job queue. Lists of jobs can not be submitted atomically, meaning some might fail while others succeed. The client is responsible for handling such cases.
Instead of requiring the client to wait for the returned jobs, another idea was to do so from within the submitting opcode in the master daemon. While technically possible, doing so would have two major drawbacks:
Instead of returning the job IDs as part of the normal opcode result, introducing a new opcode field, e.g. op_jobids, was discussed and dismissed. A new field would touch many areas and possibly break some assumptions. There were also questions about the semantics.


--- layout: page title: "About" description: "This is what I do." header-img: "img/about-bg.jpg" ---
I graduated form the University of Michigan as a Chemical Engineer. I worked in the lab as researcher support, building small scripts to help with day to day activities. I ended going into software development instead and I haven't turned back.
I have worked on everything to web applications to scripts to enterprise desktop applications. I primarily work on web applications but also have a ton of experience with C#.
My current goal on this site is to experiment with javascript frontend frameworks. I want to try building out the same application with React, Aurelia, and Angular 2 not to find out which framework is the best but the one that works the best for me.


The trees lining the icy pavement on the avenue two floors below are frozen limbs in the dead of night. A stray pair of feet here and there walk the new streets and do little else but cast shadows over the cold. A new window looms before me; a new unknown. Unfamiliar street names and a horizon that I’ve only recently met as the sun went down on another chapter of my life.
I’ve been away a long time, haven’t I?
So it seems, to me anyway. But this is the new scene, the new vantage for my viewing, the new base for my wanderings. There are no horse hooves clipping and clopping on the cobblestones; there are no cobblestones at all, actually. Just headlights and tires rolling over the thick ice that covers everything. Yes. There is asphalt and there is ice, and over these two layers a fool tries to make his way; tries to find his footing.
–
Jesus. Over the past two years I’ve been everywhere, man. From Tangier to Prague and from Oslo to Riyadh, I’ve covered Europe and the Middle East. Covered it. Hit the sweet spots, find the juice, move along. That’s been the motto, the driving force. And what a rush. What a mad, fulfilling, fast rush. Like crack but with more airline miles and hotel points.
So I was a bit surprised when I found myself overwhelmed by the buroughs of New York. The whole move started to hit me – the fact that it was happening, that is – much like it hit me when I’d moved to Amsterdam: later than it should have. In Amsterdam it wasn’t until the plane hit the ground that I realized I had no idea what was going to happen next when I got out of my seat and headed out the jet way. For New York at least, it was sometime halfway into the flight from Germany though it only occurred to me because of a situation on board.
Careening over the north Atlantic at 35,000 ft is no place to have a maniac on your hands. The third time she yelled “DON’T TOUCH ME! DON’T TOUCH ME!!” to the flight attendant, I checked the flight monitor and sure enough, flight 4677 out of Frankfurt was somewhere between Ireland and Iceland.
That is a bad place for violence.
I leaned my head back on my seat and turned so my cranium rolled up and out on the headrest to more discreetly look at the large woman in the rear corner of the 777 who was sitting a few rows behind me. She was clearly having a fit of some kind but it seemed there was nothing that could be done but clear the area and give her room to flail around and yell at people. The flight attendants seemed to know enough to form a perimeter around the woman and just hang back until the episode passed and then give her peanuts or something.
“Wow,” I said to the empty seat next to me, “it’s a good thing the professionals know what they’re doing.”
And just as suddenly, I caught myself, realizing how ridiculous that sounded coming from a guy who knows that the only thing that makes an expert is that he know more than the person next to him.
 I haven’t the foggiest reference for how to make this work.
I thought about this for a while. I might have dozed off for a bit, or maybe just had too much scotch, but the next thing I knew I saw the city come into view from behind the wing.
“Ok, New York,” I said, “here I come…”
A small child walking up the aisle with daddy in tow stopped at my seat and gave me a serene look. I had a moment of thinking that the innocence of that child, that smooth face and soft hair would be symbolic of the city showing me that no matter what tribulations I might pass, what doubts I might have, there was a side of the city that had good intentions, that would put a smile on my face, even if eventually.
Then the kid threw up on the seat next to me.
“Too soon?” I asked the kid.
“Dah!” it said, though I think it meant ‘duh’.__
Thanks, New York. I’m coming anyway.
–
Even having studied satellite images of New York on Google Maps, I was surprised at the spread of the thing. Another scar on the surface, I had to keep reminding myself that I’d seen bigger, lived through tougher. New York has nothing on São Paulo and Bangkok, even if only for the sheer savageness of those places. But New York has a way of making you forget all that and focus on that Apple. Maybe it’s something in that awesome tap water they have.
Yeah. Unfortunately, I think this is what happens to people who move to New York for the ‘New York experience’. If you’re from a small town or haven’t traveled much, you’re doomed to be eaten alive by the city. Everyone knows that. But even for those who’ve been around, whom come from large cosmopolitan places, who’ve seen the dark corners of the asphalted world, even for them New York offers a unique challenge.
It’s a problem of expectations. People are told that the city will toss them around if they’re not careful. But what’s missing from that is that it’s not a question of being careful. The city will toss you around no matter what. You’ve just got to stay afloat, hang on, get up again.
That’s one of the things about New York. When you live in New York, you’re not in control. The city is in control. Its traffic and its subways are in control. Its crazies and its people are in control. Its size and its attitudes are in control and you are along for the ride. Like the rivers that split it, New York has a current, and if you’re going to use the river to get somewhere, you can’t fight that current. You have to go with it, be prepared to take it and stand up again.
If you haven’t caught on yet, I’d missed a crucial step in preparing for the situation of finding a flat in New York.
Sure, I’m familiar with the housing markets of San Francisco and Amsterdam and have done well in finding housing and good flatmates in both places, but those are villages compared to New York City. Those are straw and mud communes next to the steel and concrete that litters the grid of Manhattan, the industrial complexes of Brooklyn, the immigrant populations of Queens, the ghetto of the Bronx and the trashiness of Staten Island. To say nothing of the other areas around the city.
And if you thought that working in Paris, Istanbul, Oslo, Riyadh, Madrid, Copenhagen, Amsterdam and Budapest all in a matter of a month was a trying thing on the body, you should try to find a flat in New York in 5 days.
Maybe I’ll tell you about it sometime.


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➜ ~ git clone https://github.com/fsegouin/css-shell.git
remote: Counting objects: 4, done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 4 (delta 0), reused 0 (delta 0)
Receiving objects: 100% (4/4), done.
Checking connectivity... done.
➜ ~


I am a multi-disciplined and energetic individual with a strong passion in software development and in mid-2017, I renounced my civil engineering position to pursue a digital career!
As a self-starter in front-end web development and programming, I operate with adept analytical and problem solving skills, am highly adaptable and a quick study. As a project manager, I bring along the ability to foster lasting client relationships and successful cradle-to-grave project oversight.
I love to learn and I am looking for opportunities to work in creative, challenging yet exciting environments and collaborate with like-minded folks. Let's connect!
You can't plow a field simply by turning it over in your mind.
Reach out and touch faith.


The verification request contains eight bytes.
The offset is an offset into aim.exe when it is mapped during execution
on Win32. So far, AOL has only been requesting bytes in static regions
of memory.
When the client recieves the request, it adds it to the current ds
(0x00400000) and dereferences it, copying the data into a buffer which
it then runs directly through the MD5 hasher. The 16 byte output of
the hash is then sent back to the server.
If the client does not send any data back, or the data does not match
the data that the specific client should have, the client will get the
following message from "AOL Instant Messenger":
"You have been disconnected from the AOL Instant Message
Service (SM) for accessing the AOL network using unauthorized software.
You can download a FREE, fully featured, and authorized client, here
http://www.aol.com/aim/download2.html"
The connection is then closed, recieving disconnect code 1, URL
http://www.aim.aol.com/errors/USER_LOGGED_OFF_NEW_LOGIN.html.
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