Logger class to log the inbag risk. More...
#include <logger.h>
Public Member Functions | |
| LoggerInbagRisk (const bool &, loss::Loss *, const double &) | |
| Default constructor. More... | |
| void | logStep (const unsigned int &, const arma::vec &, const arma::vec &, blearner::Baselearner *, const double &, const double &) |
Log current step of compboost iteration for class LoggerInbagRisk More... | |
| bool | reachedStopCriteria () const |
Stop criteria is fulfilled if the relative improvement falls below eps_for_break More... | |
| arma::vec | getLoggedData () const |
| Return the data stored within the logger. More... | |
| void | clearLoggerData () |
| Clear the logger data. More... | |
| std::string | printLoggerStatus () const |
| Print status of current iteration into the console. More... | |
| Public Member Functions inherited from logger::Logger | |
| bool | getIfLoggerIsStopper () const |
| Just a getter if the logger is also used as stopper. More... | |
| virtual | ~Logger () |
Private Attributes | |
| loss::Loss * | used_loss |
| Used loss. Note that you can specify a different loss than the loss used for training. More... | |
| std::vector< double > | tracked_inbag_risk |
| Vector of inbag risk for every iteration. More... | |
| double | eps_for_break |
| Stopping criteria, stop if \((\mathrm{risk}_{i-1} - \mathrm{risk}_i) / \mathrm{risk}_{i-1} < \mathrm{eps\_for\_break}\). More... | |
Additional Inherited Members | |
| Protected Attributes inherited from logger::Logger | |
| bool | is_a_stopper |
| Tag if the logger is used as stopper. More... | |
Logger class to log the inbag risk.
This class loggs the inbag risk for a specific loss function. It is possible to define more than one inbag risk logger (e.g. for 2 different loss functions). For details about logging and stopping see the description of the logStep()
function.
| logger::LoggerInbagRisk::LoggerInbagRisk | ( | const bool & | is_a_stopper0, |
| loss::Loss * | used_loss, | ||
| const double & | eps_for_break | ||
| ) |
Default constructor.
Default constructor of class LoggerInbagRisk
| is_a_stopper0 | bool specify if the logger should be used as stopper |
| used_loss | Loss* used loss to calculate the empirical risk (this can differ from the one used while training the model) |
| eps_for_break | double sets value of the stopping criteria` |
|
virtual |
Clear the logger data.
This is an important thing which is called every time in front of retraining the model. If we don't clear the data, the new iterations are just pasted at the end of the existing vectors which couses some troubles.
Implements logger::Logger.
|
virtual |
Return the data stored within the logger.
Return the data stored within the OOB risk logger.
This function returns the logged OOB risk.
arma::vec
of elapsed time Implements logger::Logger.
|
virtual |
Log current step of compboost iteration for class LoggerInbagRisk
This logger computes the risk for the given training data \(\mathcal{D}_\mathrm{train} = \{(x_i,\ y_i)\ |\ i \in \{1, \dots, n\}\}\) and stores it into a vector. The empirical risk \(\mathcal{R}\) for iteration \(m\) is calculated by:
\[ \mathcal{R}_\mathrm{emp}^{[m]} = \frac{1}{|\mathcal{D}_\mathrm{train}|}\sum\limits_{(x,y) \in \mathcal{D}_\mathrm{train}} L(y, \hat{f}^{[m]}(x)) \]
Note:
The implementation to calculate \(\mathcal{R}_\mathrm{emp}^{[m]}\) is done in two steps:
risk_temp
of losses for every observation for given response \(y^{(i)}\) and prediction \(\hat{f}^{[m]}(x^{(i)})\).risk_temp
.This procedure ensures, that it is possible to e.g. use the AUC or any arbitrary performance measure for risk logging. This gives just one value for risk_temp
and therefore the average equals the loss function. If this is just a value (like for the AUC) then the value is returned.
| current_iteration | unsigned int of current iteration |
| response | arma::vec of the given response used for training |
| prediction | arma::vec actual prediction of the boosting model at iteration current_iteration |
| used_blearner | Baselearner* pointer to the selected baselearner in iteration current_iteration |
| offset | double of the overall offset of the training |
| learning_rate | double lerning rate of the current_iteration |
Implements logger::Logger.
|
virtual |
Print status of current iteration into the console.
The string which is created in this functions must have exactly the same length as the string from initializeLoggerPrinter()
. Those strings are printed line by line.
std::string
which includes the log of the current iteration Implements logger::Logger.
|
virtual |
Stop criteria is fulfilled if the relative improvement falls below eps_for_break
The stopping criteria is fulfilled, if the relative improvement at the current iteration \(m\) \(\varepsilon^{[m]}\) falls under a fixed boundary \(\varepsilon\). Where the relative improvement is defined by
\[ \varepsilon^{[m]} = \frac{\mathcal{R}_\mathrm{emp}^{[m-1]} - \mathcal{R}_\mathrm{emp}^{[m]}}{\mathcal{R}_\mathrm{emp}^{[m-1]}}. \]
The logger stops the algorithm if \(\varepsilon^{[m]} \leq \varepsilon\).
bool
which tells if the stopping criteria is reached or not (if the logger isn't a stopper then this is always false) Implements logger::Logger.
|
private |
Stopping criteria, stop if \((\mathrm{risk}_{i-1} - \mathrm{risk}_i) / \mathrm{risk}_{i-1} < \mathrm{eps\_for\_break}\).
|
private |
Vector of inbag risk for every iteration.
|
private |
Used loss. Note that you can specify a different loss than the loss used for training.


A sore or white tongue is not usually serious and is often easily treated.
Things you can do yourself
Do
-
use a soft toothbrush to brush your teeth
-
brush your tongue or use a scraper to help improve a white tongue
-
use a straw to drink cool drinks
-
take paracetamol or ibuprofen
Don’t
-
do not use a toothpaste that contains sodium lauryl sulphate
-
do not eat hard, spicy, salty, acidic or hot food and drink that may irritate your tongue
-
do not smoke
-
do not drink alcohol
You can ask a pharmacist about:
- what's causing your sore or white tongue
- if you can buy anything to help with any pain or irritation
- if you should see a dentist or GP
Non-urgent advice: See a GP or dentist if you:
- have pain or itchiness that does not go away or gets worse
- have white patches on your tongue
Common causes of a sore or white tongue
Biting or burning your tongue with hot food or drink can cause pain and swelling. But this should only last a few days.
A white tongue can be a sign of a health condition.
Do not self-diagnose – see a GP if you're worried.
Lichen planus
Read about lichen planus.
Leukoplakia
Read about leukoplakia.
Geographic tongue
Read about geographic tongue.
Mouth ulcer
Read about mouth ulcers.
Oral thrush
Read about oral thrush.
Page last reviewed: 02 June 2020
Next review due: 02 June 2023


Tree Subcommands
In the preceding book, the apply command was used to access the attributes of the function_def node associated with the symbol “main”. In this case, the AST node for this function_def was readily available by converting the symbol “main”. However, for most of the nodes in the tree, such a ready conversion from the indexed, pre-related world of symbols is not available. Instead, queries on AST nodes typically involve navigating a portion of the AST looking for nodes of interest. The tree command provides an interface for examining all the nodes in a subtree, starting with a defined node and visiting all of its descendants.
If passed something other than an AST node (or set of AST nodes), the tree command attempts to convert its arguments into AST nodes prior to navigating the subtrees.
tree print
tree print ast_nodes
This command, already introduced, navigates the tree starting with the node it is passed. It formats the output to show the tree structure, with increasing levels of indentation to indicate children, and vertical lines tying together siblings.
tree find
tree find is the workhorse of TPM. Its syntax is:
tree find attribute-expression ast_nodes
tree find examines the AST nodes in ast_nodes and all of their descendants. It applies the attribute expression to each node, and if the attribute-expression returns true, that node is added to the return set.
Before using tree find in an interactive session, set the printformat to ast_info:
printformat %s ast_info
This makes the results returned in interactive Access sessions much more readable.
From the index, you can determine that main uses a number of functions:
uses -functions [data symbol main]
returns 21 functions.
But using the tree find command, you can find those exact calls:
tree find call_expr [data node main]
returns
1 call_expr 14095 17 ‘basename(argv[0])’
2 call_expr 14128 16 ‘strlen(progname)’
3 call_expr 0 0 ‘’
. . .
34 call_expr 17301 14 ‘do_exit(ERROR)’
35 call_expr 17637 74 ‘fprintf(stder ...,progname)’
36 call_expr 17795 80 ‘fprintf(stder ... name,optarg)’
37 call_expr 17885 14 ‘do_exit(ERROR)’
38 call_expr 18530 26 ‘treat_file(argv[optind++])’
39 call_expr 18597 13 ‘treat_stdin()’
40 call_expr 18663 15 ‘do_list(-1, -1)’
41 call_expr 18709 18 ‘do_exit(exit_code)’
There are some things to note about this output. First, the number of call expressions is greater than the number of functions called. In this excerpt, there are two calls to fprintf and three calls to do_exit. This should not be surprising in light of the differences between the symbols in the index and the detail in the AST. Second, the third call_expr has no starting offset and no length: it cannot be mapped to the source text. In this case, this is because the function call is the result of the expansion of the macro “strequ”. This does not imply that no information is available about that call. In fact, the opposite is true:
tree print 3
call_expr 0 0
global_id 0 0
minus_expr 14226 18 : ‘progname+proglen-4’
| plus_expr 14226 16 : ‘progname+proglen’
| | global_id 14226 8 : ‘progname’
| | local_id 14235 7 : ‘proglen’
| global_id 14243 1 : ‘4’
convert_expr 0 0
global_id 0 0
The first child—the global_id—is the function called; its cname attribute is interesting. While easier ways of getting at this node are presented shortly, it is already possible to obtain (albeit imprecisely) the attribute for this node (and a few others):
printformat %s%s cname ast_info
tree find global_id 3
returns
1 strcmp global_id 0 0 ‘’
2 progname global_id 14226 8 ‘progname’
3 4 global_id 14243 1 ‘4’
4 .exe global_id 0 0 ‘’
tree filter
The syntax for tree filter is the same as tree find: tree filter attribute-expression scope, and its semantics are quite similar: it returns a set of nodes that match the attribute expression. However, unlike tree find, tree filter does not consider the descendents of scope: it only considers those nodes that are explicitly passed.
The only difference between filter and tree filter is that tree filter converts its arguments to AST nodes before filtering through application of the attribute expression.
tree navigate
tree navigate is very similar to tree filter; it applies attribute-expression to each node in scope, and returns a node for each time the attribute-expression succeeds. The navigate comes in because the returned node may not be a node in scope, but instead may be related to it in a way described by an extended attribute expression. The semantics of tree navigate are discussed later in this book.


Resume Summary
Full time undergraduate student at the department of Computer Science and Engineering at University of Liberal Arts Bangladesh (ULAB). Dedicated, versatile and efficient with a total of 4+ years of teaching experience, paired with taking part in various extracurricular activities while maintaining outstanding academic performance. Diversified skills include programming skills, analytical skills, presentation skills, project and team management, event organization, communication skills and writing skills.
Experiences
Job responsibilities included communicating with customers and suppliers, setting up meetings, receiving and distributing communications, collecting and mailing correspondences, maintaining supplies by checking stock to determine inventory levels, anticipating requirements; placing and expediting orders; verifying receipt; stocking items; mainaintaining equipment by completing preventive maintenance, troubleshooting failures and calling for repairs.
Job responsibilities included teaching English, Mathematics, Physics, General Science and Computer Science.
Projects
Programming Skills & Proficiency
Structured Programming:
C
Object Oriented Programming:
C++ & JAVA
Web Programming:
HTML & CSS & JavaScript & PHP
Database:
MySQL
Microcontroller embedded systems:
Arduino & Intel Gallileo
Agent based Modeling:
NetLogo
Other Activities
Programming Contests
- ACM ICPC Dhaka Regional (2016)
- ACM ICPC Dhaka Regional (2015)
- State University of Bangladesh IUPC (2016)
- BUET IUPC (2015)
- United International University IUPC (2015)
- ULAB Code Combat (2015)
- Eastern University IUPC (2015)
Seminars & Workshops
- Student Leadership Development Workshop 2014, by Bangladesh Center for Communication Programs
- “Robi4You”, Daylong visit at Robi Axiata on behalf of ULAB
- Data Analysis, by American International University Bangladesh
- Google Developers Group Bangladesh Android Study Jam 2015
- Archeology, Tourism and Development, by Center for Archaeological Studies, ULAB
- ‘Practical Software Engineering’ by LEADS Corporation Ltd.
- ‘Formal Program Verification using Hoare’s Logic’ facilitated by Dr. Mahmudul Faisal Al Ameen
- ‘Simple Computational Rules to Develop Pioneer Vertebrate Neuronal Network’ cfacilitated by Dr. Abul Kalam Al Azad
- ‘A Dialogue on Employability to Bridge Skills Gap’ organized by Metropolitan Chamber of Commerce and Industry
- ‘Research and Extracurricular activities in Computer Science and Engineering’ conducted by Dr. Mohammadd Kaykobad
- ‘Innovation at Google’ conducted by Mr. Kazi Monirul Kabir
- ‘Google I/O Recap Celebration Bangladesh’, organized by Google Developer Group Dhaka
- Embedded Systems with Intel Galileo


10
6.1.6.a constraint property
You must see an xforms-valid message when you activate the Valid Value trigger.
You must see an xforms-invalid message when you activate the Invalid Value trigger.
From
To
xforms-valid
xforms-invalid
Valid Value
25
Invalid Value
5


Skeleton
Skeleton is a new powerful feature of RV2 that enables fast and interactive generation of complex patterns. This example presents how to use the From Skeleton
feature to generate the topology of pattern.
Skeleton features in RV2 are part of the compas_skeleton package.
0. Initialising
First, initialise the RV2 engine.
1. Pattern
From Skeleton
takes a set of conencted lines as the input (not polylines). In Rhino, draw a network of connected lines, something like what is shown in the image below.
In the RhinoVAULT 2 drop down menu, select “Create Pattern”, then “From Skeleton.” Select the network of lines.
There are three main parameters of the skeleton that will define the overall shape of the pattern. For the first parameter, the user will need to interactively define how wide the pattern is going to be at the leaf vertices. Using the mouse, simply click at the location that is suitable for the size and shape of the skeleton.
For the second parameter, the user will need to interactively define how wide the pattern is going to be at the inetrior vertices.
For the third and the last parameter, the user will need to interactively define the inlincation of the edges at the leaf vertices.
Once the three main parameters have been defined, the user can explore various other skeleton features, which can facilitate in refining the skeleton further. In this example, we will use just the Subdivide function, which automatically subdivides and smoothes the pattern. Subdivide function can be applied repeatedly. Merge function un-subdivides the pattern.
In this example, Subdivide function was applied twice.
2. Boundary conditions
Once the pattern has been successfully created, the boundary conditions need to be defined.
In the RhinoVAULT 2 drop down menu, select “Define Boundary Conditions”, then “Identify Supports.”
By using the vertex selection mode ByContinuousEdges, select the edges at the leaf vertices, which we will define as the supports of the vault (shown in red).
In certain applications, additional refinment and tweaking of the opening gometries (and relaxation) can improve the speed of the equilibrium algorithms later on in the workflow. However, Update Openings is not applied in this example.
3. Form and force diagrams
Create form diagram
Once the boundary conditions have been defined, the Pattern can now be converted into a FormDiagram.
In the RhinoVAULT 2 drop down menu, select “Create FormDiagram.”
Create force diagram
Once the FormDiagram has been created, the ForceDiagram can now be created.
In the RhinoVAULT 2 drop down menu, select “Create ForceDiagram.”
4. Equilibrium
Horizontal equilibrium
Once the FormDiagram and ForceDiagram have been created, the horizontal equilibrium algorithm perpendicular-ises either or both diagrams, which converts them from dual to reciprocal diagrams.
In the RhinoVAULT 2 drop down menu, select “Horizontal Equilibrium.”
Vertical equilibrium
With the FormDiagram and ForceDiagram now reciprocal, the coordinates of the ThrustDiagram can be iteratively computed based on a desired z-max (target height) value.
In the RhinoVAULT 2 drop down menu, select “Vertical Equilibrium.”


Sydney is the largest city in Australia with a population of 22,000 in central Sydney and 4 million people in greater Sydney.
Sydney will leave you with not a minute spare during your stay between visiting the many unique cultural activities, innovative restaurants, world renowed shopping and of course the spectacular Sydney Harbour and Opera House.
The people are friendly, the air is clean and it is one of the safest cities in the world.
Things to do and see
1. Climb the Harbour Bridge!
No doubt you have often marvelled at the stunning Sydney Harbour Bridge for its sheer
size and architectural wonder. Now you have the chance not only to walk over it and touch
it, but to climb it! This is a once in a lifetime opportunity, one that you should not
pass up. Bookings can be made through the Bridgeclimb web site.
limbing the bridge!">
2. Visit The Rocks!
The historic site of Sydney's first European settlement, The Rocks contains many beautifully restored buildings from Sydney's early days as a colonial town. Explore the maze of original, cobblestone lanes that lead to a range of shops, cafes and restaurants. The shopping ranges from Australian design in fashion, art, craft and jewellery to unique collectables and souvenirs. More information can be obtained from the
NSW Tourism Web Site.
3. See Fox Studios in Australia!
Fox Studios is Sydney's most vibrant and
colourful meeting place, within the setting of a
real working movie studio. As you wander along the curved streets you'll catch the best
in entertainment shopping and dining. All about you is the buzz of urban culture and
entertainment. Eating, Shopping, Movies or just having a coffee - it's all here.
Tourist Services
1. General Information and services.
NSW Tourism Web Site.
2. Accommodation specialising in European Travellers.
Bed and Breakfast Services.
3. Limousine, Coach Tours and Airport Shuttle.
Sydney ARTS Tours.
4. Harley Davidson motorbike tours of Sydney.
Rolling Thunder Motorcycle Tours.
5. Car Rental Services.
Budget Rent A Car.
6. Language Translation Services.
German Translation Online.
| Monthly avg. Temp (C) | Monthly avg. Clear Days | Monthly avg. Rainfall (mm) |
|---|---|---|
| 22.1 | 8.5 | 92.1 |


beautiful
not
pretty
SQUARES
007
double layer <div> cakes
A <div> inside of a <div> times three. With "borrowed" code for the second and third backgrounds.


--- title: Combined filters for MEMS-based respiratory motion estimation. keywords: fastai sidebar: home_sidebar nb_path: "nbs/index.ipynb" ---
pip install minmotion_respiration_filters
Fill me in please! Don't forget code examples:
1+1
Original Paper:
Eero Lehtonen, Jarmo Teuho, Juho Koskinen, Mojtaba Jafari Tadi, Joaquin Rives Gambin, Tuija Vasankari, Antti Saraste. University of Turku, Finland
Abstract: We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.
@article{lehtonen2021respiratory,
title={A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography},
author={Lehtonen, Eero and Teuho, Jarmo and Koskinen, Juho and Jafari Tadi, Mojtaba and Kl{\'e}n, Riku and Siekkinen, Reetta and Rives Gambin, Joaquin and Vasankari, Tuija and Saraste, Antti},
journal={Sensors},
volume={21},
number={12},
pages={3983},
year={2021},
publisher={Multidisciplinary Digital Publishing Institute}
}