Start with the "Review" tab plots 13--16. Plot 15 should show a reasonable Schuler oscillation in the difference, remaining approximately within the dotted red lines. Plot 16 should show only a minor Schuler oscillation for DVEWG/DVNSG (green lines), the difference between the result of the complementary-filter and the original GPS measurement. Use "brush" to zoom to problems. Look for general consistency with past projects. Also check both panels in plot 17, where (outside of turns) AKRD should be in approximate agreement with AOAREF and SSRD should be in reasonable agreement with SSREF.
Use the "Past Projects" -> "Maneuvers" tab to check any speed-run, pitch, circle, reverse-heading, or yaw maneuvers that are present.
As for all variables, use the "Known Problems" -> "out-of-range, frozen, spike" tab to check for problems with the basic (i.e., raw, not derived) measurements entering the wind calculation. These include the measurements from the INS and the pressure differences from the radome or pitot tube.


Image Localization
-
Tag:
Related works
- Tutorial
- Scene coordinate regression
- Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
- Backtracking regression forests for accurate camera relocalization
- Random forests versus Neural Networks—What’s best for camera localization?
- DSAC DSAC-Differentiable RANSAC for camera localization
- DSAC++ Learning Less is More – 6D Camera Localization via 3D Surface Regression
- DSAC++ angle loss Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
- Dense regression: Full-Frame Scene Coordinate Regression for Image-Based Localization: The same idea as mine
- Scene Coordinate and Correspondence Learning for Image-Based Localization
- Pose regression
- Posenet: A convolutional network for real-time 6-dof camera relocalization
- Modelling Uncertainty in Deep Learning for Camera Relocalization
- Image-based localization using LSTMs for structured feature correlation
- Geometric loss functions for camera pose regression with deep learning
- RelocNet: Continuous Metric Learning Relocalisation using Neural Nets: Retrieval + relative pose regression
- Temporal/RNN
- Traditional
- Feature selection
- Avoiding confusing features in place recognition: (ECCV 2010)
- feature re-weighting: Learned Contextual Feature Reweighting for Image Geo-Localization
- Recent works
- MapNet: Geometry-Aware Learning of Maps for Camera Localization: Leverage the full map (CVPR2018, spotlight)
- X-View: Graph-Based Semantic Multi-View Localization: Use segmentation
- Semantic Visual Localization: Use segmentation (CVPR2018, ETH)
- Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions: Outdoor benchmark (CVPR2018, spotlight, ETH)
- InLoc: Indoor Visual Localization with Dense Matching and View Synthesis: (CVPR2018, spotlight, ETH)
- Pose regression + visual odometry Deep Auxiliary Learning for Visual Localization and Odometry: (ICLA2018)
- encoder-decoder network
- Scene coordinate regression: Full-Frame Scene Coordinate Regression for Image-Based Localization
- Single-view depth estimation: Unsupervised Learning of Depth and Ego-Motion from Video Github
- Single-view depth estimation: UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
- Depth completion: Deep Depth Completion of a Single RGB-D Image
- DispNet: A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
- geometry + deep learning
- Bayesian Neural Network & MCMC Sampling
- Coursera: Bayesian Neural Networks
- SGLD: Bayesian Learning via Stochastic Gradient Langevin Dynamics: (ICML2011)
- preconditioned SGLD: Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks: (AAAI 2016)
- Bayesian Dark Knowledge: (NIPS2015)
- Modelling Uncertainty in Deep Learning for Camera Relocalization
- DSAC-Differentiable RANSAC for camera localization
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification
- Optical Flow
- FlowNet
- FlowNet2.0 [github]
- GeoNet: unsupervide depth, optical flow & pose [github]
- joint optical flow + segmentation
- Unsupervised depth & ego-motion [github] (CVPR 2017 Oral)
- SegFlow: optical flow + object segmentation [github]
- stereo + segmentation + optical flow
- Unsupervised, depth + stereo (CVPR 2017)
- CRF
PnP algorithm
“Perspective-n-Point (PnP) is the problem of estimating the pose of a calibrated camera given a set of n 3D points in the world and their corresponding 2D projections in the image.” from wikipedia
P3P
P3P is the PnP problem in its minimal form. Let P be the camera center, and , , be the 3D points in world frame. Let , , , , , , , , , , , , as illustrated in Figure 1.
Then the P3P equation system is derived based on the law of cosines (余弦定理):
where , , are variables to be determined.
To simply the equation system, we let , , , , , then the equation system is turned into
where , , are variables to be determined.
Eliminating , we have
Essentially, the two quatratic equations define two conics, which may have infinite or at most four intersections. Therefore, the P3P problem has either an infinite number of solutions or at most four physical solutions.
P4P, P5P, PnP
Using more than 3 points provides redundant data for the solution of pose estimation. The linear algorithm is generally used [1]. The points can form triangles and thus derive second degree polynomial equations based on 余弦定理. By eliminating the set of variables into a single one, we still have fourth degree polynomial equations. When n = 5, we have the homogeneous equation system:
The overdermined equation system can be easily solved by SVD decomposition, where equals to the right singular vector corresponding to the smallest singular value. The solution can be generalized to cases when .
When , things are a little bit more complicated since the equation system above is underdetermined. We refer the readers to [1] for details.
EPnP
EPnP algorithm is one of the most efficient PnP algorithm which has O(n) complexity. It is composed of three steps:
Step 1. In world coordinate system, find four control points: . One of them is the centroid of the 3D points and the other three are vectors aligned with principle directions of the 3D point set. Then all the 3D points are represented by the barycentric coordinates as a linear combination of the four control points: .
Step 2. In camera coordinate system, the equations between 3D points and control points still preserve: . Given the 2D projections of the 3D points , we have . Here, and are unknowns. By filling in the camera intrinsic matrix, the equation can be expressed as follow:
By eliminating , we get two linear equations:
Therefore, each 3D-2D correspondence yields two linear equations with respect to a 12-vector . If consider all the correspondences, we generate a linear system of the form
If we have normalized the coordinates of image pixels to , the equation system becomes
where denotes Kronecker product.
Step 3. The solution of the linear equation system lies in the null space or kernel of and can be expressed as , where are the right-singular vectors of corresponding to the zero singular values. Now the unknowns become . Based on the analysis in [2], the dimension of null-space of varies from 1 to 4. The algorithm then considers all the four cases and choose the best set of with the miminum reprojection error.
Other PnPs and insights
- RPnP: A Robust O(n) Solution to the Perspective-n-Point Problem
- OPnP: Revisiting the PnP Problem: A Fast, General and Optimal Solution
- REPPnP: Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection
- ASPnP: ASPnP: An Accurate and Scalable Solution to the Perspective-n-Point Problem
- The accuracy of solutions to the PnP problem is closely related to the configuration of the 3D point set [RPnP]. Ordinary 3D case, planar case, quasisingular case and “uncentered data” [EPnP].
- Under the assumption of independently and identically distribution (i.i.d.) Gaussian noise, minimizing the reprojection error is statistically optimal in the sense of maximum likelihood estimation (MLE).
- Pose ambiguity
Scene COordinate REgression Network (SCORE-Net)
Network architecture
-
Dilated convolution (空洞卷积) Fully convolutional betwork, dilated conv
-
Heteroscedasticity 异方差，变量的方差各不相同。Remidial actions for severe heteroscedasticity are necessary.
-
“Data normalization (or called pre-conditioning in the numerical literature) is an essential step in the DLT algorithm.” from Page 107
-
rank-constrained optimization
-
degenerate cases and the coplanar case


About
Newcastle is known for being an innovative and flourishing creative hub of Australia. Home to many world class art facilities, schools and galleries, such as Hunter street Tafe Art and Design, The Lock Up and the Newcastle Art Gallery. The city finds itself on the map as a destination for exposure to the arts. However, many do not realise that the arts, similarly to other forms of production, comes at an environmental cost that tends to be overlooked.
Picture yourself passionately painting a beautiful canvas, what you are left with is two things, a wonderful piece of art and a cup of murky paint water and messy brushes. Many agree that the issue of paint waste is one without an effective, easy to access solution, and unfortunately, in the end, many of us are guilty of simply washing paint down the drain.
Art institutions usually set the bench-mark for environmentally friendly practices. But Fact: Paint is a toxin and polluting substance, which when disposed of incorrectly, can be extremely harmful to the environment. Unless this issue is addressed, paint will continue to harm our systems by clogging our waterways, polluting our oceans and harming marine life.
Considering the damage paints can inflict on our environment, our solution aims to prevent creative paint from being disposed of in drains by providing artists and institutions with a device which functions to solve this problem by separating the paint from the water.
‘RePaint’ is an easy to install device and attachable to existing sinks and cleaning areas of studios and creative spaces. Artists and students will use RePaint to wash contaminated utensils and paint water, the device then collects the solid matter while returning clean water to the drain. The collected by-product (paint pigment and matter) can be recycled and used again in the studio in many fun and creative ways!


| Opcode | Instruction | Op/En | 64-Bit Mode | Compat/Leg Mode | Description |
| 0F 01/7 | INVLPG m | M | Valid | Valid | Invalidate TLB Entry for page that contains m. |
Notes: * See the IA-32 Architecture Compatibility section below.
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| M | ModRM:r/m (r) | NA | NA | NA |
Invalidates (flushes) the translation lookaside buffer (TLB) entry specified with the source operand. The source operand is a memory address. The processor determines the page that contains that address and flushes the TLB entry for that page.
The INVLPG instruction is a privileged instruction. When the processor is running in protected mode, the CPL must be 0 to execute this instruction.
The INVLPG instruction normally flushes the TLB entry only for the specified page; however, in some cases, it may flush more entries, even the entire TLB. The instruction is guaranteed to invalidates only TLB entries associated with the current PCID. (If PCIDs are disabled — CR4.PCIDE = 0 — the current PCID is 000H.) The instruction also invalidates any global TLB entries for the specified page, regardless of PCID.
For more details on operations that flush the TLB, see “MOV—Move to/from Control Registers” and Section <IP_ADDRESS>, “Operations that Invalidate TLBs and Paging-Structure Caches,” of the Intel® 64 and IA-32 Architectures Software Developer’s Manual, Volume 3A.
This instruction’s operation is the same in all non-64-bit modes. It also operates the same in 64-bit mode, except if the memory address is in non-canonical form. In this case, INVLPG is the same as a NOP.
The INVLPG instruction is implementation dependent, and its function may be implemented differently on different families of Intel 64 or IA-32 processors. This instruction is not supported on IA-32 processors earlier than the Intel486 processor.
Flush(RelevantTLBEntries); Continue; (* Continue execution *)
None.
| #GP(0) | If the current privilege level is not 0. |
| #UD | Operand is a register. If the LOCK prefix is used. |
| #UD | Operand is a register. If the LOCK prefix is used. |
| #GP(0) | The INVLPG instruction cannot be executed at the virtual-8086 mode. |
| #GP(0) | If the current privilege level is not 0. |
| #UD | Operand is a register. If the LOCK prefix is used. |


1. In a medium bowl, mix the vinegar, soy sauce, garlic, honey, olive oil, ground black pepper, Worcestershire sauce, onion powder, salt, liquid smoke, and cayenne pepper.
2. Place steaks in a shallow glass dish with the marinade, and turn to coat. For optimum flavor, rub the liquid into the meat. Cover, and marinate in the refrigerator for 1 to 2 days.
3. Preheat grill for medium-high to high heat.
4. Lightly oil the grill grate. Grill steaks 7 minutes per side, or to desired doneness. Discard leftover marinade.


Daddy Yankee to open reggaeton museum in Puerto Rico
SAN JUAN, Puerto Rico — Puerto Rico reggaeton star Daddy Yankee is opening a museum in the U.S. Caribbean territory that will be dedicated to his life and the music that made him famous.
The Daddy Yankee El Jefe Museum opens Friday in Plaza de Las Americas in San Juan and will be free of charge.
The space of 8,000 square feet (743 square meters) will feature the history of reggaeton starting in 1991 as well as the artist’s clothes, awards and memorabilia. The museum will remain open through January.
Daddy Yankee made the announcement Thursday in an Instagram post. He recently set a record at the Coliseo of Puerto Rico with 10 shows scheduled in December.
Among his best-known songs is "Gasolina" and "Despacito," a collaboration with pop singer Luis Fonsi that became the most watched video ever on YouTube.


The Indian audience has had a great history of viral videos, thus loving and sharing such videos whereever they see. So Topflick basically is a digital curated hub of those interesting viral videos, connecting people from all parts of the country. In November 2018 I got this project as a freelance, designing teh final mocks of the App as per the defined UI.


This page will be generated and filled with product specific information. Please check back in some minutes for an updated version.


Strange Case of Dr Jekyll and Mr Hyde is the original title of a novella written by the Scottish author Robert Louis Stevenson that was first published in 1886. The work is commonly known today as The Strange Case of Dr. Jekyll and Mr. Hyde, Dr. Jekyll and Mr. Hyde, or simply Jekyll & Hyde. It is about a London lawyer named Gabriel John Utterson who investigates strange occurrences between his old friend, Dr. Henry Jekyll, and the evil Edward Hyde.
Inspiration & Writing
Stevenson had long been intrigued by the idea of how personalities can affect a human and how to incorporate the interplay of good and evil into a story. While still a teenager, he developed a script for a play about Deacon Brodie, which he later reworked with the help of W. E. Henley and saw produced for the first time in 1882. In early 1884 he wrote the short story “Markheim”, which he revised in 1884 for publication in a Christmas annual. One night in late September or early October 1885, possibly while he was still revising “Markheim,” Stevenson had a dream, and upon wakening had the intuition for two or three scenes that would appear in the story. Biographer Graham Balfour quoted Stevenson’s wife Fanny Stevenson:
“In the small hours of one morning, I was awakened by cries of horror from Louis. Thinking he had a nightmare, I awakened him. He said angrily: ‘Why did you wake me? I was dreaming a fine bogey tale.’ I had awakened him at the first transformation scene.”
Main Characters
The story revolves around 8 main characters:
- Dr. Henry Jekyll/Edward Hyde
- Mr. Gabriel John Utterson
- Richard Enfield
- Dr. Hastie Lanyon
- Mr. Poole
- Inspector Newcomen
- Sir Danvers Carew
- Maid
Reception
Strange Case of Dr Jekyll and Mr Hyde was an immediate success and is one of Stevenson’s best-selling works. Stage adaptations began in Boston and London and soon moved all across England and then towards his home Scotland within a year of its publication and it has gone on to inspire scores of major film and stage performances.
The Strange Case of Dr Jekyll and Mr Hyde was initially sold as a paperback for one shilling in the UK and one dollar in the U.S. The American publisher issued the book on 5 January 1886, four days before the first appearance of the UK edition issued by Longmans; Scribner’s published 3000 copies, only 1250 of them bound in cloth. Initially stores would not stock it until a review appeared in The Times, on 25 January 1886, giving it a favourable reception. Within the next six months, close to forty thousand copies were sold. The book’s success was probably due more to the “moral instincts of the public” than any perception of its artistic merits; it was widely read by those who never otherwise read fiction, quoted in pulpit sermons and in religious papers. By 1901 it was estimated to have sold over 250,000 copies.


Nine-month-old boy dies after car reverses over him
Incident happened near GTB Hospital
by Staff ReporterA nine-month-old toddler was killed after he fell from his grandmother’s arms when the scooter they were riding on was hit by a reversing car at GTB Hospital gate on Friday, the police said.
The reversing car ran over the child.
Amit Sharma, DCP (Shahdara) said, while Arbaaz died on the spot, his uncle suffered injuries and has been admitted to a hospital.
“During initial probe, it was found that the scooter was riding on the wrong side,” said Mr. Sharma.
The family lives in Ghaziabad and the child’s father Mohammad Arshad is a local garment trader.
Arshad has been bedridden for a few months.
The child was suffering from pneumonia and he was taken to GTB Hospital on a scooter by his grandmother and uncle on Friday when the accident took place.
In her statement, the child’s grandmother told the police that the car driver was reversing the vehicle carelessly. She signalled him to stop but he hit the scooter and the child fell from her arms and was run over.
The police personnel present at the hospital rushed the child to the doctors where they declared him brought dead. A case under appropriate sections has been registered and action has been initiated, they said.


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Dumbledore Grants is a University institutions in Wizards Kingdom which provides funding for spells and innovative magic potions.
The User has applied for funding in your institution for conducting research on the magic properties of some rare magic beans.
After checking that the application is correct and meets the requirements you agree to provide funding for his research project.
One of the requirements for receiving a grant for conducting research and development on a magic genetic resource is to attach with the application information on prior informed consent(PIC), the source of the genetic resource and/or the establishment of mutually agreed terms(MAT). Depending on the circumstances, the applicant can also provide that information in the mid-term report after acquiring the research grant.


[§128D-9] Injunctive relief. The director may institute a civil action in any environmental court of competent jurisdiction for injunctive relief to prevent any violation of this chapter, of any rule adopted pursuant to this chapter, or of any order issued pursuant to this chapter. The environmental court shall grant relief in accordance with the Hawaii rules of civil procedure. [L 1990, c 298, pt of §17; am L 2014, c 218, §8]
Rules of Court
Injunctions, see HRCP rule 65.


In the Oxford dictionary hate is defined as an emotion of "intense dislike", for someone or something. Hate is verbalised in speech and used to express "hatred or intolerance of other social groups, especially on the basis of race or sexuality".
Within the framework of this wide definition, more restricted ones may be issued for different purposes, starting from the promotion of peace among humankind to the prohibition of unlawful acts, through the definition of what should be illegal but is currently not, on which all people do not necessarily agree.
The Committee of Ministers of the Council of Europe has stated, that they consider that “hate speech shall be understood as covering all forms of expression which spread, incite, promote or justify racial hatred, xenophobia, anti-Semitism or other forms of hatred based on intolerance, including: intolerance expressed by aggressive nationalism and ethnocentrism, discrimination and hostility against minorities, migrants and people of immigrant origin”.
Since there is no universal definition, some web pages and social platforms have adopted their own definitions of hate speech.
 This very clear and simple definition hides a very complex situation in practice, even within the E.U.
The E.U. Member States' legislations show huge disparities, which may lead to the punishment, non-punishment or lower punishment of certain behaviours or of certain grounds motivating the behaviour, depending on the precise circumstances that surround the potential offence, even in one single country, and depending on the country that will be competent to judge the case.
Taking this situation into account, the comparative study which is available in the MANDOLA intermediate deliverable D2.1, proposes a classification of hatred content into three categories: (1) illegal in all or almost all the studied EU Member States, (2) illegal or partly illegal in a majority of these countries; (3) illegal in a minority of these countries.
The following behaviours are illegal in all or almost all the studied EU Member States:
-
Publicly inciting hatred directed against a group of persons or a member of such a group determined on the basis/grounds of their race, national or ethnic origin, and religion, if the incitement is either carried out in a manner likely to disturb public order, or public peace, or which is threatening, abusive or insulting.
- Making available to the public xenophobic or racist material which incites hatred through a computer system, for the same illegal grounds as mentioned above
- Publicly insulting a person or a group of persons by reason of their racial or ethnic origin or religion.
- Public defamation, which is, in many countries, prohibited whatever the grounds. However, in certain countries an illegal motivation is necessary.
- Direct or indirect discrimination, in particular areas including harassment.
The following behaviours are illegal or partly illegal in a majority of the studied EU Member States:
- Establishing or participating in organisations that promote or incite discrimination or violence or hatred.
- Publicly condoning, denying or grossly trivialising crimes against peace, crimes of genocide, crimes against humanity and war crimes.
- Sending of grossly offensive and/or indecent or obscene or menacing character messages or whatever other conteny.
- Direct public incitement to commit any offence or crime, for any ground.
- Threatening of a natural person, motivated by racism or xenophobia, through a computer system.
- In addition, an illegal motivation is an aggravating circumstance of all the penal infringements in 5 countries out of 10, and of certain penal infringements in 3 countries out of 1.
- Insult to religion.
The following behaviours are illegal in a minority of the studied EU Member States:
- Sending of a message, or whatever content, which can cause annoyance, harassment and / or needless anxiety to another person, which the sender knows to be false, for any ground.
- Promotion or public incitement to hostility or violence between communities.
- Recording of images of the commission of a crime or offence against a person.
-
Realising a montage with the talk or the images of a third party without his or her consent, if it is not obvious that it is a montage or if it is not specified that it is a montage, for any ground.
- To misuse / usurp someone else's identity, for any ground.
Regarding specifically illegal grounds, the comparative study shows that all the following ones may be taken into consideration, depending on the country and depending on the exact behaviour to sanction and it s circumstances:
-
In relation with most infringements, including incitement to racial hatred: race; national and ethnic origins; religion; sexual preferences or orientation or hetero or homosexuality;disability or bodily, psychological or mental handicap; sex or sex and gender motives or gender; sexual identity or gender identity; national or ethnical ancestry or genealogical origin or descent or ethnic, racial or national belonging of family members; origins or social origins; nationality; skin colour; ideology or beliefs or political orphilosophical beliefs; familiar situation; age; civil status; birth; fortune; language; state of health; illness; physical or genetic characteristics; membership of the travelling community.
-
In relation with the making available of material inciting or promoting hatred, violence or discrimination, additional (to the paragraph above) illegal motivations are in one country flags, emblems, pins, uniforms, slogans, salutation formulas or any other signs which promote fascist, racist or xenophobic ideas, conceptions or doctrines.
-
In relation with the participation in organisations that promote or incite discrimination or violence or hatred, additional grounds might be those mentioned in relation with incitement to hatred above, and, in one country, social category, non-transmissible chronic disease, HIV infection, belonging to a disadvantaged category, as well as any criterion which would restrict or discharge the acknowledgement, use or exercise - in equal conditions - of human rights and fundamental liberties or rights acknowledged by law, in the political, economic, social and cultural fields or in any other field of the public life.
-
In relation with aggravating circumstances, additional grounds might be those mentioned in relation with incitement to hatred above, and racist, xenophobic or other inhuman (Germany); wealth, chronic non-contagious disease or HIV/AIDS infection, social category, inclusion in a unflavoured category, or any other criterion (Romania, in relation to harassment). We can notice that some of these grounds are widely open to interpretation.
Anyone may be a victim of hate speech. However, there are persons or groups or people much more likely to be the subjects of hate speech, partly due to the current socio-political context or the current existing conflicts between beliefs or personal morals.
Legally speaking, victims of hate speech are the persons who are targeted by the speech, or eventually, where the law provides for it, the people who belong to a group, a community or a religion which is the target of the hate speech.
Within the framework of some specific infringements, a complaint of the victim is necessary in order to prosecute the author of the illegal behaviour.


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I am a systems administrator working with Linux, Solaris and a wide variety of network equipment. In my spare time I hack on projects using Node.js, Java, Ruby and sometimes so C/C++. I live in Melbourne, Australia.


{% extends "base.html" %} {% block content %}
Scientific literature is growing at a rate of more than 2 new articles every single minute. It is impossible for scientists to consume and understand the rapidly expanding ocean of biomedical literature. You can help biomedical researchers find the information they need to discover cures faster.
The ability to read remains a uniquely human skill. Mark2Cure allows anyone that can read English, regardless of background, to help in the process of biomedical discovery. You can help them by identifying key pieces of information in scientific text to discover cures faster.
It wasn't that long ago that all of our NGLY1 kids were undiagnosed and without hope. With your help and support, a diagnosis can be found much faster.
If you can read you can help.
I want to help


A common use for @x:Act is to generate web pages. The @x:Act interpreter can be run under a web server as either a CGI or as a servlet.
Here the previous factorial example has been modified to produce XHTML output for displaying the result in a web browser.
Note how the example contains XML to be added to the program output. @x:Act supports two syntax variants: the @x:Act form that we saw in the previous example and the a pure XML form. This example combines the two forms.
An XML element is an alternative syntax for an @x:Act syntax command. The default rule for commands with no namespace or whose namespace is not x: is to render that command/element. Execution of an element to be rendered produces a string representation of that element.


Class EnTransformExtensions
Utility extensions for manipulating and working with Transforms.
...
#define DISABLE_ALL_EN_EXTENSIONS //Disable all Extensions
#define DISABLE_EN_TRANSFORM_EXTENSIONS // Disable this Extensions
Inheritance
System.Object
EnTransformExtensions
Namespace: UnityEngine
Assembly: ECHTNICE.EnUnityEngineExtensions.dll
Syntax
public static class EnTransformExtensions
Methods
| Improve this Doc View SourceGetLookAtRotation(Transform, GameObject)
Find the rotation to look at a GameObject
Declaration
public static Quaternion GetLookAtRotation(this Transform self, GameObject target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.GameObject | target | The thing to look at |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetLookAtRotation(Transform, Transform)
Find the rotation to look at a Transform
Declaration
public static Quaternion GetLookAtRotation(this Transform self, Transform target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Transform | target | The thing to look at |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetLookAtRotation(Transform, Vector3)
Find the rotation to look at a Vector3
Declaration
public static Quaternion GetLookAtRotation(this Transform self, Vector3 target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Vector3 | target | The thing to look at |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetLookAwayFromRotation(Transform, GameObject)
Find the rotation to look away from a target GameObject
Declaration
public static Quaternion GetLookAwayFromRotation(this Transform self, GameObject target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.GameObject | target | The thing to look away from |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetLookAwayFromRotation(Transform, Transform)
Find the rotation to look away from a target transform
Declaration
public static Quaternion GetLookAwayFromRotation(this Transform self, Transform target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Transform | target | The thing to look away from |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetLookAwayFromRotation(Transform, Vector3)
Find the rotation to look away from a target Vector3
Declaration
public static Quaternion GetLookAwayFromRotation(this Transform self, Vector3 target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Vector3 | target | The thing to look away from |
Returns
| Type | Description |
|---|---|
| UnityEngine.Quaternion |
GetTransformPath(Transform)
Declaration
public static string GetTransformPath(this Transform transform)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | transform |
Returns
| Type | Description |
|---|---|
| System.String |
LookAt(Transform, GameObject)
Look at a GameObject
Declaration
public static void LookAt(this Transform self, GameObject target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.GameObject | target | The thing to look at |
LookAwayFrom(Transform, GameObject)
Instantly look away from a target GameObject
Declaration
public static void LookAwayFrom(this Transform self, GameObject target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.GameObject | target | The thing to look away from |
LookAwayFrom(Transform, Transform)
Instantly look away from a target transform
Declaration
public static void LookAwayFrom(this Transform self, Transform target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Transform | target | The thing to look away from |
LookAwayFrom(Transform, Vector3)
Instantly look away from a target Vector3
Declaration
public static void LookAwayFrom(this Transform self, Vector3 target)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.Transform | self | |
| UnityEngine.Vector3 | target | The thing to look away from |


China
USA
Peru
What passport do you have?
{{x.name}}
How long you are staying for?
Less than one month
Between two weeks and two months
Greater than two months


GET STARTED¶
If you want to quickly know how to use PaddlePaddle, please refer to the following guide:
While using PaddlePaddle to build applications, please understand some basic concepts.
Here is an example of linear regression. It introduces workflow of PaddlePaddle, including data format, model configuration and training, etc.


Chicken-and-Rice
Few dishes conjure up ideas of dinnertime classics more than chicken and rice, and this recipe helps you have this family favorite on the table quickly. For this crowd-pleasing dish, cut the cooking time in half by using skinned-and-boned chicken breasts, and ready-to-serve long-grain rice. Frozen peas and canned mushrooms will also help you keep the preparation time to a minimum, but this dish will still taste absolutely delicious. Crispy bacon adds just that little touch of smoky, crunchy flavor, and, topped with fresh parsley, this dinner even presents beautifully. If you like, you can substitute the green peas for snow peas for a different texture. However you prefer it, this quick-fix supper is wonderful.
Ingredients
- 1 1/2 pounds skinned and boned chicken breasts
- 2 (8.8-ounce) pouches ready-to-serve long-grain rice
- 4 bacon slices, diced
- 1/2 cup chopped onion
- 1/2 cup frozen green peas, thawed
- 1 (4-ounce) can sliced mushrooms, drained
- 3/4 teaspoon salt
- 1/4 teaspoon pepper
- Garnish: fresh parsley sprigs
Directions
- Cut chicken into 1/4-inch slices, and set aside.
- Heat rice according to package directions; set aside.
- Sauté bacon in a large skillet over medium-high heat 8 minutes or until crisp; remove bacon with a slotted spoon, reserving 1 tablespoon drippings in skillet.
- Sauté onion in hot drippings in skillet 3 minutes or until tender. Stir in chicken, and sauté 8 minutes or until chicken is done. Stir in rice, bacon, peas, and next 3 ingredients; cook, stirring occasionally, 3 minutes or until thoroughly heated. Garnish, if desired.
- 4 ounces (about 1 heaping cup) snow peas may be substituted for green peas. Microwave snow peas and 1/4 cup water in a microwave-safe bowl 2 minutes before adding to chicken mixture. Proceed with recipe as directed.
- Note: For testing purposes only, we used Uncle Ben's Original Long Grain Ready Rice.


"Murder on the Orient Express"
A Review by Ryan Kohler
As my English Composition instructor commented recently, "Agatha Christie is the coolest mystery writer ever." This is true with her books; however, in many cases, film adaptions of her books have, in a word, sucked. An exception is "Murder on the Orient Express," a luxurious, quick-paced throwback to a different kind of time and place.
Made in 1974, "Orient Express" was directed by Sidney Lumet (known more for gritty, urban pictures like "Dog Day Afternoon" and "Serpico.") In Lumet's capable hands the film takes the viewer back to 1935 as the famed Orient Express travels from Istanbul to Calais, France. Midway through the trip a shady businessman, Ratchett (Richard Widmark), is murdered in his sleep. Master Belgian sleuth Hercule Poirot (Albert Finney, in the best incarnation of the character ever) is faced with the daunting task of discovering who is the murderer(s).
Shot with an all-star cast, including Sean Connery, Ingrid Bergman, Sir John Geilgud, and Lauren Bacall, among others, this film makes for a very entertaining time. All the actors do well in their roles, especially Bergman (who won an Oscar for this role) and Bacall. Only Anthony Perkins is a sore point--his character recalls too much of the personable side of Norman Bates to be effective here.
Best of the performances is, naturally, Finney. He imbibes Poirot with a fastidious, lively approach. At once bouncy and charismatic, Finney's Poirot is not one-dimensional. The high-point of the film, and of Finney's portrayal, is when Poirot announces his solution to the murder. Basically twenty minutes of just Poirot talking, Finney delivers a tour-de-force performance at this crucial juncture.
Paul Dehn's screenplay brims over with wit and sarcasm, with many good lines going to Bacall and Finney. Richard Rodney Bennett's score is throwback to another time as he uses strings and the piano to a thrilling effect. Tony Walton's glorious period costumes and the set design of the train is almost, quite simply put, amazing.
"Murder on the Orient Express" is a brilliantly shot film. Cinematographer Geoffrey Unsworth shoots almost everything in a brightly lit frame, giving all the actors a glowing aura about them. He also manages to pull off an outstanding camera feat as the train first pulls out of the station in Istanbul--combined with Bennett's score it must be seen and heard to be believed.
There is just so much to recommend in this film. Everything seems to come off without a hitch. Just sit back, relax, and enjoy "Murder on the Orient Express." Movies just are not made like this anymore.
"Murder on the Orient Express" (1974). Directed by Sidney Lumet. Written by Paul Dehn, from the novel by Agatha Christie. Music by Richard Rodney Bennett. With Albert Finney, Lauren Bacall, Ingrid Bergman, Sean Connery, Anthony Perkins, Michael York, and many other big name stars. Distributed by Paramount. Running time: 127 minutes. Rated PG. AVAILABLE ON HOME VIDEO.
Rating (out of a 4.0 cumulative average): 4.0
--- 5 April 1998
**Visit My Film Review Multiplex: http://pages.prodigy.com/XWKZ01B/index.htm**
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The ultimate potato gratin dauphinoise recipe
by Georgina HaydenWhen the nights are long and cosy, all I want to do is cook things slowly and curl up on the sofa.
Prep time: 15 minutes | Cooking time: 1 hour 30 minutes
SERVES
six
INGREDIENTS
- 1kg waxy potatoes
- 400ml double cream
- 400ml whole milk
- 2 garlic cloves
- nutmeg, for grating
- 1 bay leaf
- a large knob of butter
- a handful of breadcrumbs
- a few sprigs of thyme
- 60g Gruyère or Comté
- olive oil, for drizzling
METHOD
- Preheat the oven to 180C/ 160C fan/gas mark 4.
- Cut the potatoes into thin slices (no need to peel them; they are especially pretty left unpeeled if red-skinned). Ideally, use a mandolin or the slicing attachment on a food processor to speed things up.
- Pour the cream and milk into a large saucepan and bring to the boil. Peel the garlic and add one whole clove to the pan with a good grating of nutmeg and the bay leaf. Season generously, and when the mixture is bubbling add the potatoes. Reduce the heat and cook for six minutes, so the potatoes just begin to cook.
- Meanwhile, halve the remaining garlic clove and rub the inside of a roasting tray with its cut sides. Grease liberally with butter and then gently pour or spoon in the potato and cream mixture, discarding the garlic. Cover the tray with foil and bake for 45 minutes.
- Place the breadcrumbs in a bowl and pick in the thyme leaves. Grate in the cheese and toss everything with a little olive oil.
- After 45 minutes, remove the tray of potatoes from the oven, scatter over the breadcrumb mixture and place back in the oven, uncovered, for a further 30 minutes, until golden and cooked through. Allow to cool for five minutes before serving.


[SYMBOL]
[A]
[B]
[C]
[D]
[E]
[F]
[G]
[H]
[I]
[J]
[K]
[L]
[M]
[N]
[O]
[P]
[Q]
[R]
[S]
[T]
[U]
[V]
[W]
[X]
[Y]
[Z]
backslash (\) directory paths
Windows
line continuation
regular expressions
string literals
backtick (`), string conversion
backward compatibility
exception objects, strings as
base64 module
decode function
decodestring function
encode function
encodestring function
BaseHandler class
BaseHTTPRequestHandler class
BaseHTTPServer module
2nd
web server implementation
basename function (os.path module)
BaseRequestHandler class (SocketServer module)
client_address method
handle method
request method
server method
Bastion class (Bastion module)
Bastion module
Bastion class
bbox method (Canvas object)
benchmarking
2nd
Berkeley Database library
[See BSD DB] binaries downloading
installing from
third-party installers for various platforms
Binary class (xmlrpclib module)
binary data, encoding as text
base64 module
quopri module
uu module
binary file mode
binary function (xmlrpclib module)
bind method socket object
Widget object
bind_all method (Widget object)
binding, references
bisect function (bisect module)
bisect module bisect function
insort function
BitmapImage class
BlackAdder IDE
blank lines
blitz function (Numeric module)
Boa Constructor IDE
body method (NNTP object)
bool function (built-in)
2nd
Boolean class (xmlrpclib module)
Boolean context
boolean function (xmlrpclib module)
Boolean values
Boost Python Library
bound methods
2nd
[See also methods][See also methods] braces
[See curly braces] brackets
[See square brackets] break command (pdb module)
break statement
browsers
BSD DB (Berkeley Database library)
2nd
bsddb module
btopen function
close method
first function
has_key function
hasopen function
keys function
last method
next method
previous function
rnopen function
set_location function
btopen function (bsddb module)
buffer function (built-in)
buffering cmd.py and
writing buffer out to OS
build_opener function (urllib2 module)
built-in exception classes ArithmeticError
AssertionError
AttributeError
EnvironmentError
Exception
FloatingPointError
ImportError
IndentationError
IndexError
IOError
KeyboardInterrupt
KeyError
LookupError
MemoryError
NameError
NotImplementedError
OSError
OverflowError
StandardError
SyntaxError
SystemError
TypeError
UnboundLocalError
UnicodeError
ValueError
WindowsError
ZeroDivisionError
built-in functions
abs
apply
bool
2nd
buffer
callable
chr
cmp
2nd
coerce
compile
exec statement and
delattr
dir
divmod
2nd
encode
eval
2nd
execfile
filter
getattr
globals
hasattr
hash
2nd
hex
id
__import__
2nd
input
2nd
safer variant of
isinstance
2nd
3rd
issubclass
2nd
iter
2nd
len
2nd
3rd
locals
map
max
2nd
min
2nd
oct
open creating file object
restricted execution
ord
pow
2nd
range
2nd
raw_input
2nd
reduce
reload
2nd
repr
round
setattr
slice
unichr
vars
xrange
2nd
zip
built-in modules
__import__ function
loading
built-in types
classmethod
2nd
complex
2nd
dict
2nd
file (open)
2nd
float
2nd
int
2nd
list
2nd
long
2nd
object
2nd
property
2nd
staticmethod
2nd
3rd
str
2nd
3rd
date/time string conversions
super
2nd
tuple
2nd
type
2nd
3rd
unicode
2nd
3rd
4th
built-in variables __debug__
__builtin__ class
__builtin__ module
2nd
[See also built-in functions][See also built-in functions] __builtins__ attribute
__builtins__ attribute (built-in module)
Button class (Tkinter module)
flash method
invoke method
byteswap method (array object)
byteswapped method (array object)
|


As soon as I see the car I rush downstairs. Mother is in the kitchen making noises but I run right by her. Outside, the car pulls into the driveway. I run to it smiling but I slow down. Something is different about the car. Whose car is this?
The door opens. I stop. Dad gets out. He's got that grumpy look he usually has. He's wearing his pajamas but they have no buttons. Mom gets out of the car too. She comes out of the same door. She's wearing her blue dress. I start to cry and run to her and hug her legs.  It's OK."
"Where did you go?" I ask. I am crying like a baby. "Why did you leave me? Why did you go?"
"We went to the store," mom says.
"But you were gone so long," I say. My face is smushed up against her side.
"We went to the store and bought some dresses and dad got some stuff for his car."
I look up at her. Her face is all blurry because I am crying. I wipe my face. She looks down at me smiling. Her face is smooth and glowing. "We stayed at the store a few days," she says and pats me on the head.
It doesn't make sense to me.  I ask.
Mom stops smiling. "Monster?"
"There's a monster in the house."
"Nick," she says like she thinks I'm telling stories.
"You weren't at the store for three days! Where were you?"
"Nick," my dad says in his grumpy voice. "That's enough."
I look at him. The shape of his face is weird. He usually has freckles on his cheeks but they're not in the right places. I let go of mom and look at her. She makes a little smile like she always does when she sees me. It's her. It's mom. It's her face. But it's too... What's wrong with it?
Mom's shirt moves. There's something underneath it. It's pushing and trying to get out. I step back.
I scream and mom falls apart. Her face falls to pieces and her whole body hits the ground like a sack of potatoes. The same thing happens to dad. Their clothes are just lying on the driveway but there's something inside the clothes moving around inside. I scream and something screams back.  A kitty cat.
Other cats slip out of the bottom of the dress and out of my dad's pajamas. A whole bunch of cats all different colors.  A few cats run away. Some of them cry.
Something pinches my shoulder and I scream. It's Mother's crab hand. She yanks my arm and drags me back to the house. I shout and scream but she holds me tight.   They're missing eyeballs and covered with big golden flies and all of them are tweeting and cackling at me.
"Your magic isn't strong enough to make whomever you want," she says in a deep voice.
The birds all giggle. "Never will be!" one of them shouts.


| Details | Tiger | Panda | Lion | Giraffe |
|---|---|---|---|---|
| Image | ||||
| Scientific Name | Panthera tigris | Ailuropoda melanoleuca | Panthera leo | Giraffa |
| LifeSpan | 8 – 10 years (In the wild) | 20 years (In the wild) | Female: 10 – 15 years (Adult, In the wild), Male: 8 – 10 years (Adult, In the wild) |
20 to 25 years |
| About |
The tiger is the largest living cat species |
The giant panda,also known as the panda |
The lion is a large felid of the genus Pant |
The giraffe is an African artiodactyl mammal, |
| Link For Visit | Extra Information About Tiger | Extra Information About Panda | Extra Information About Lion | Extra Information About Giraffe |


repl
nix is a functional language and has a repl!
this makes it easy to try examples out while learning the synax and operations
https://nixos.wiki/wiki/Nix-repl
run :q
to exit the repl!
links to tutorials
- official “nix pills” https://nixos.org/nixos/nix-pills/index.html
- multipart nix shell tutorial in blog format https://www.sam.today/blog/environments-with-nix-shell-learning-nix-pt-1/
- learn x in y minutes nix edition https://learnxinyminutes.com/docs/nix/
- official nix expression docs https://nixos.org/nix/manual/#ch-expression-language
- a “gentle introduction” community writeup https://ebzzry.io/en/nix/#nix
- another “by example” blog series https://medium.com/@MrJamesFisher/nix-by-example-a0063a1a4c55


fun encodeChecked(version: Int, payload: ByteArray): String
Encodes the given version and bytes as a base58 string. A checksum is appended.
version
- the version to encode
payload
- the bytes to encode, e.g. pubkey hash
Return
the base58-encoded string


| Introducing Infusion 2017 |
| Happy August! As we get ready to move back to State College this month, we excitedly look forward to seeing all our favorite alumnis when they come visit. In the upcoming months, we look forward to updating you on the latest Infusion news using our newsletters. Thank you to those of you who have already filled out the information sheet we made to build a stronger relationship for the alumni and future boards. Please continue filling it out if you have not completed it yet.  We wish you and Zuniara a very happy life filled with laughter and joy! |
|
|
Latch on to your Dreams
We hope you are settled in well with your full time job at Southco, Anuj. Can't wait to see what you conquer around Philadelphia! |
|
|
|
Stapled to Staples
|
Congratulations Ayushi for starting a full time job with Staples in New York City this past month! |
|
|
Ah, La Vache!!
Congratulations on your full time job with your french makeup company, Rachel! We are extremely proud of you and hope you are settling in well. |
|
|
|
Dreams Come True
|
Congratulations Shalini for being placed in your dream city, New York City, for your first full time job! |
|
|
Back to School
Good luck at Shippensburg University for graduate school this fall, Kelsey! We are excited to see what amazing things you do there! |
|
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|
An Apple a Day...
|
Good luck starting medical school at Northwestern University this fall, Reshma! You can officially become a real doctor now! |
|
|
| Question Time |
|
What is your favorite Infusion memory? Send us your favorite Infusion memory and it may come up in our next newsletter!
|
|
| Riddle of the Month |
|
There are two Indians: a big Indian, and a little Indian. The little Indian is the big Indian's son but the big Indian is not the little Indian's father. How is this possible? (Highlight below with your cursor to see the answer)
The big Indian is the little Indian's mother!
|
|


Journeys
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Click below when you're ready to embark on a new journey!
Friends already on a trip? Type in their trip code to join them!
This trip was created by {{ recentTrip.creator.username }}.
There were {{ recentTrip.expenses.length }} expenses totaling {{ totalExpenses | currency }}.
Here are your friends who were a part of this trip:
{{ friend.username }}
{{expense.expenseString}} (${{ expense.amount.toFixed(2) }}) was split among:
{{ transaction.username }} owes {{ transaction.payee.username }} {{ transaction.payee.amount | currency }}.


GeneratedKeysMode
Modes of generated keys' gathering.
| Methods |
| static int |
valueOf(Object generatedKeysRequest)
Determines mode of generated keys' gathering.
|
| static int |
valueOf(Object generatedKeysRequest)
Determines mode of generated keys' gathering.
Parameters:
generatedKeysRequest - {@code null} or {@code false} if generated keys are not
needed, {@code true} if generated keys should be configured
automatically, {@code int[]} to specify column indices to
return generated keys from, or {@code String[]} to specify
column names to return generated keys from
Returns:
mode for the specified generated keys request
|
NONE
= 0
Generated keys are not needed.
AUTO
= 1
Generated keys should be configured automatically.
COLUMN_NUMBERS
= 2
Use specified column indices to return generated keys from.
COLUMN_NAMES
= 3
Use specified column names to return generated keys from.


Generates and returns a version-specific (CVSS v2) scoring engine loaded with a vector.
the vector to load in to the scoring engine
the loaded scoring engine
Converts an access complexity, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts an access vector, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts an authentication level, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts a collateral damage potential, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts an impact magnitude, represented as a string, into an enum value.
the converted enum value
Converts an impact magnitude, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts an impact subscore value, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts a remediation level, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts a report confidence level, represented as a string, into an enum value.
the string to convert
the converted enum value
Converts a target distribution, represented as a string, into an enum value.
the string to convert
the converted enum value
Generated using TypeDoc
Implements a service offering CVSS v2 vector parsing.
Consumers should be aware that parse will raise an exception if an invalid CVSS v2 vector string is passed. This includes strings containing incorrect keys/values and those that are missing required entries.


In this example a folder tree is created with different types of file system objects: folders, files and links.
On UNIX each object is associated with a structure called the i-node, this reference is in the folder entries where the objects are.
Reminder 1: the folders' color is green and the links are blue. The files color is defined when created.
Reminder 2: In the simulation the folder size is 1 block, while the links do not occupy disk space, they are simply entries in its folder data.
Reminder 3: The structure of an i-node is
For each new file data blocks are filled in the order of the i-node, first direct, then indirections single, double and triple.
| Tree | Type | Size Units |
|||
| / | Folder | 1 | |||
| folder1 | Folder | 1 | |||
| file1.txt | File | 101 | |||
| file2.txt | File | 200 | |||
| link1 | Hard link (to file1.txt) |
1 | |||
| folder2 | Folder | 1 | |||
| file3.txt | File | 460 | |||
| Folder3 | Folder | 1 | |||
| file4.txt | File | 120 | |||
| link2 | Hard link (to link1) |
0 |
Block size is 1 unit.
Comments:


Below, we visualized an example where we tracked a calibration wand in a rocky underwater environment at Corsica.
3D tracking a calibration wand by pnuehrenberg on Sketchfab
Here we show the 3D trajectories of a small fish school (Lamprologus c.) that was recorded while diving in Lake Tanganyika.
Lamprologus c. @ Kasakalwe, Tanganyika by pnuehrenberg on Sketchfab


---
layout: post
title: ToxoDB 4.2 is released
category: [ToxoDB]
---
ToxoDB 4.2 provides microarray expression data and mass-spec data, and queries against them. The Genome Browser has new tracks for the proteomics data, and cosmid and BAC ends.
The ToxoDB 4.2 web site also has significant improvements to the query and results pages. Many of the queries have been reorganized. For example, the SNPs based on Gene ID query now allows an input of a list of genes, either put in by hand or uploaded from a file. The results from this and all other queries can be sorted on various columns. It is also now possible to add columns of interest (e.g., protein features, GO annotation, even expression characteristics for gene results) and sort on them.
New data and queries in ToxoDB 4.2:
- Two expression experiments carried out on the Toxo Affymetrix array, namely:
- Effects of Glucose Starvation in Toxoplasma gondii
- Expression profiling of the 3 archetypal T. gondii lineages
have been added to the ToxoDB Database, and queries are provided to find genes by differential expression and by percentile of expression.
- Proteomics data provided by two labs (Wastling and Murray) have been loaded, and these are visible on the Genome Sequence Browser. Queries to find genes, or ORFs, that show evidence of protein expression based on Mass Spec analyses are also available.
- BAC ends and cosmid ends sequences have been loaded, and can be viewed in the Genome Sequence Browser
- A mapping of epitopes to Toxoplasma provided by the Immune Epitope Database and Analysis Resource (IEDB) . A query is now provided to identify genes whose encoding protein has an epitope at different levels of confidence.
- Updated E.C. number and metabolic pathway assignments, Interpro protein domains and associated GO predictions,
- A new build of ortholog groups from Orthomcl for use in ToxoDB. The list of genomes represented has increased from 55 to 87 and includes all the organisms covered by the different NIH/NIAID funded Bioinformatics Resource Centers.
- ToxoCyc, a database of computationally predicted metabolic pathways for Toxplasma, is available online and is cross-referenced on the gene pages of ToxoDB.
New features in ToxoDB 4.2:
- Most of the queries have been reorganized to make them more intuitive. Some include a new "Advanced Parameters" section. See for example "Identify Genes based on EST evidence."
- The columns displayed in query result pages are improved. The result pages also offer options for sorting, adding new columns, and moving columns around.
- Downloading results is improved. A choice of formats is provided including tab-delimited, text, GFF, and FASTA to meet different needs.
- Tab delimited provides an expanded list of annotations that can be directly exported as an Excel spreadsheet.
- Text provides a detailed summary for each gene including SNPs, metabolic pathways, notes, sequence, and many other choices.
- GFF is a format recognized by many applications such as the genome browser, gbrowse.
- FASTA also provides a common format used for BLAST and other applications. The FASTA option provides the functionality of the SRT (sequence retrieval tool).
- As per request, we have provided for Blast against GT1, VEG and RH (Chr Ia, Ib) genome sequences, in addition to the reference ME49 genome.


Data
HELP
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Script
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CLEAR
DEFAULT
UPLOAD SCRIPT
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Script File
Graph 1:
Database (y-axis):
Populations
Population Female %
Population Female % at Birth
Life Expectancy - Continents
Median Age
Births
Births Per Woman
Births Per 1000 People
Child Deaths
Child Mortality Rates
Survival Rate to Age 65 - Male
Survival Rate to Age 65 - Female
Military Personnel
Military Personnel % Population
Military Spending
Military Spending % GDP
GDP
GDP Per Capita
Economic Freedom Scores
CO2 Emissions
CO2 Emissions Per Capita
CO2 Emissions %
CO2 Emissions Cumulative
CO2 Emissions Cumulative %
Location:
Year Range (x-axis):
Graph type:
line
bar
Color:
SUBMIT
Graph 2:
Database (y-axis):
Populations
Population Female %
Population Female % at Birth
Life Expectancy - Continents
Median Age
Births
Births Per Woman
Births Per 1000 People
Child Deaths
Child Mortality Rates
Survival Rate to Age 65 - Male
Survival Rate to Age 65 - Female
Military Personnel
Military Personnel % Population
Military Spending
Military Spending % GDP
GDP
GDP Per Capita
Economic Freedom Scores
CO2 Emissions
CO2 Emissions Per Capita
CO2 Emissions %
CO2 Emissions Cumulative
CO2 Emissions Cumulative %
Location:
Year Range (x-axis):
Graph type:
line
bar
Color:
SUBMIT
Light
Dark
Graphs


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Line Chart Example


Publications
Preprints
- G.M. Stine, E.M. Trautmann, D. Jeurissen, and M.N. Shadlen (2022). A neural mechanism for terminating decisions. BioRxiv.
PDF
Journal publications
-
D. Jeurissen*, S. Shushruth*, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2022). Deficits in decision-making induced by parietal cortex inactivation are compensated at two time scales. Neuron, 110, 1-8. (Similar to: BioRxiv.) * Equal contribution.
PDF
Data and code
Journal Club Slides (Google doc) -
Y.H.R. Kang*, A. Löffler*, D. Jeurissen*, A. Zylberberg^, D.M. Wolpert^, and M.N. Shadlen^ (2021). Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation. eLife, 10, e63721. (Similar to: BioRxiv.)
PDF
Journal Club Slides (Google doc)
2 Minute video about this research
15 Minute Cosyne talk related to this research
*/^ Equal contribution -
M.W. Self*, D. Jeurissen*, A.F. van Ham, B. van Vugt, J. Poort, and P.R. Roelfsema (2019). The segmentation of proto-objects in the monkey primary visual cortex. Current Biology, 29, 1019-1029.
PDF
Journal Club Slides (Google doc)
* Equal contribution -
C.R. Fetsch, N.N. Odean, D. Jeurissen, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2018). Focal optogenetic suppression in macaque area MT biases direction discrimination and choice confidence, but only transiently. eLife, 7, 36523. (Similar to: BioRxiv.)
PDF -
P.C. Klink, D. Jeurissen, J. Theeuwes, D. Denys, and P.R. Roelfsema (2017). Working memory accuracy for multiple targets is driven by reward expectation and stimulus contrast with different time-courses. Scientific Reports, 7(9082), 1-13.
PDF -
D. Vartak, D. Jeurissen, M.W. Self, and P.R. Roelfsema (2017). The influence of attention and reward on the learning of new stimulus-response associations. Scientific Reports, 7(9036), 1-12.
PDF -
D. Jeurissen, M.W. Self, and P.R. Roelfsema (2016). Serial grouping of 2D-image regions with object-based attention in humans. eLife, 5, e14320.
PDF
Journal Club Slides (Google doc) -
M.W. Self*, J.C. Peters*, J.K. Possel*, J. Reithler, R. Goebel, P. Ris, D. Jeurissen, L. Reddy, S. Claus, J.C. Baayen, and P.R. Roelfsema (2016). The effects of context and attention on spiking activity in human early visual cortex. PLoS Biology, 14(3), e1002420. * Equal contribution
PDF -
B. Rubio*, A.D. Boes*, S. Laganiere, A. Rotenberg, D. Jeurissen, and A. Pascual-Leone (2016). Noninvasive brain stimulation in pediatric attention-deficit hyperactivity disorder (ADHD): A review. Journal of Child Neurology, 31(6), 784-796. * Equal contribution
PDF -
D. Jeurissen, A.F. Van Ham, and M.W. Self (2015). The neural representation of multiple objects in the primate visual system. Journal of Neuroscience, 35(37), 12612-12614.
PDF -
D. Jeurissen, A.T. Sack, A. Roebroeck, B.E. Russ, and A. Pascual-Leone (2014). TMS affects moral judgment, showing the role of DLPFC and TPJ in cognitive and emotional processing. Frontiers in Neuroscience, 8, 1-9.
PDF -
D. Jeurissen, M.W. Self, and P.R. Roelfsema (2013). Surface reconstruction, figure-ground modulation, and border-ownership. Cognitive Neuroscience, 4(1), 50-52.
PDF -
I. Korjoukov, D. Jeurissen, N.A. Kloosterman, J.E. Verhoeven, H.S. Scholte, and P.R. Roelfsema (2012). The time-course of perceptual grouping in natural scenes. Psychological Science, 23(12), 1-8.
PDF
Posters, talks, and abstracts (Presenting author only)
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D. Jeurissen*, A. Löffler*, Y.H.R. Kang*, A. Zylberberg^, D.M. Wolpert^, and M.N. Shadlen^ (2021). Serial time-multiplexed incorporation of evidence to make two decisions about one object. Talk at the Virtual Cosyne 2021 Conference, February 24 - 26. */^ Equal contribution. 15 Minute talk available on YouTube.
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D. Jeurissen*, S. Shushruth*, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2019). Deficits in decision making after pharmacological and chemogenetic inactivation of Area LIP. Poster at the Howard Hughes Medical Institute Science Meeting, December 3 – 5, Chevy Chase, MD, USA. * Equal contribution
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D. Jeurissen*, S. Shushruth*, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2019). Deficits in decision making after pharmacological and chemogenetic inactivation of Area LIP. Poster at the Society for Neuroscience Meeting, October 19 – 23, Chicago, IL, USA. * Equal contribution
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D. Jeurissen*, S. Shushruth*, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2019). Chemogenetic and pharmacological inactivation of parietal cortex during perceptual decision-making. Poster at the Columbia University Postdoctoral Symposium, October 4, New York, NY, USA. * Equal contribution
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D. Jeurissen*, S. Shushruth*, Y. El-Shamayleh, G.D. Horwitz, and M.N. Shadlen (2019). Deficits in decision making after Inactivation of Area LIP in the Macaque Monkey. Poster at the Simons Foundation SCGB Annual Meeting, September 8 – 10, New York, NY, USA. * Equal contribution
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D. Jeurissen, Y.H.R. Kang, and M.N. Shadlen (2018). Serial integration of two sensory sources of information for a single perceptual decision. Poster presentation at the Simons Foundation annual meeting, September 5 - 7, 2018, New York, NY, USA.
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D. Jeurissen (2016). Flexible routing of information through specialized networks in the brain. Talk at the Simons Foundation Annual Meeting, September 11 - 13, New York, NY, USA.
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D. Jeurissen, A.F. Van Ham, M.W. Self, and P.R. Roelfsema (2015). The development of figure-ground segregation across time and space in the visual cortex of the macaque monkey. Poster presentation at the Society for Neuroscience, October 17 - 21, 2015, Chicago, IL, USA.
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M.W. Self, D. Jeurissen, A.F. Van Ham, M. Senden, and P.R. Roelfsema (2015). Border-ownership tuning predicts effective connectivity between V4 and V1 in macaque visual cortex. Poster presentation at the Society for Neuroscience, October 17 - 21, 2015, Chicago, IL, USA.
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A.F. Van Ham, M.W. Self, D. Jeurissen, and P.R. Roelfsema (2015). Global scene interpretation affects figure-ground modulation and contrast perception. Poster presentation at the Society for Neuroscience, October 17 - 21, 2015, Chicago, IL, USA.
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D. Jeurissen, M.W. Self, and P.R. Roelfsema (2015). Object-based attention spreads at multiple spatial scales to achieve perceptual grouping. Poster presentation at the Federation of European Neuroscience Societies Brain Conference Meeting on Bridging Neural Mechanisms and Cognition, April 19 - 22, 2015, Rungstedgaard, Denmark.
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D. Jeurissen, M.W. Self, and P.R. Roelfsema (2014). Perceptual organization and object-based attention in the human visual system. Oral presentation at the 21st ONWAR annual meeting, November 27 - 28, 2014, The Netherlands. (Awarded with best oral presentation award)
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D. Jeurissen, M.W. Self, A.F. van Ham, and P.R. Roelfsema (2014). Figure-ground modulation of Gestalt objects in the visual cortex of the macaque monkey. Poster presentation at the Society for Neuroscience Meeting, November 15 - 19, 2014, Washington, DC, USA.
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D. Jeurissen, M.W. Self, J. Poort, B. van Vugt, and P.R. Roelfsema (2014). Figure-ground modulation for complex shapes in macaque V1. Poster presentation at the Gordon Research Conference on the Neurobiology of Cognition, July 20 - 25, 2014, Newry, ME, USA.
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D. Jeurissen, M.W. Self, and P.R. Roelfsema (2013). Object-based attention spreads at multiple spatial scales. Poster presentation and short oral presentation at the 20th ONWAR annual meeting, November 28 - 29, 2013, The Netherlands.
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D. Jeurissen, M.W. Self, and P.R. Roelfsema (2013). Object based attention spreads at multiple spatial scales. Poster presentation at the Donders Discussions Meeting, October 31 - November 1, 2013, Nijmegen, The Netherlands.
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D. Jeurissen*, M.W. Self*, J. Poort, B. van Vugt, and P.R. Roelfsema (2012). Figure-ground modulation in the primary visual cortex of the macaque monkey. Poster presentation at the 19th ONWAR annual meeting, November 22 - 23, 2012, The Netherlands. * Equal contribution
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D. Jeurissen*, M.W. Self*, J. Poort, B. van Vugt, and P.R. Roelfsema (2012). Figure-ground modulation for complex shapes in the primary visual cortex of the macaque monkey. Poster presentation at the Society for Neuroscience, October 13 - 17, 2012, New Orleans, LA, USA. * Equal contribution
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D. Jeurissen and P.R. Roelfsema (2012). Image Parsing, From Curves to Natural Images. Oral presentation at the Vision Science Society Conference, May 11-16, 2012, Naples, FL, USA.
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D. Jeurissen, I. Korjoukov, N.A. Kloosterman, H.S. Scholte, and P.R. Roelfsema (2011). Object Recognition and Image Parsing. Poster presentation at the 18th ONWAR annual meeting, November 24 - November 25, 2011, The Netherlands.
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D. Jeurissen, I. Korjoukov, N.A. Kloosterman, H.S. Scholte, and P.R. Roelfsema (2011). Object Recognition and Image Parsing of Natural Images. Poster presentation at the 34th European Conference on Visual Perception, August 28 - September 1, 2011, Toulouse, France.
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D. Jeurissen, A.T. Sack, A. Roebroeck, and A. Pascual-Leone (2010). The brain connectivity network underlying moral judgment. Poster presentation at the Federation of European Neuroscience Societies 7th Forum of European Neuroscience, July 3 - 7, 2010, Amsterdam, The Netherlands.


--- layout: archive title: "Publications" permalink: /publications/ author_profile: true --- {% if author.googlescholar %}
You can also find my articles on my Google Scholar profile.


Aviva to keep Singapore, China businesses
LONDON : British life and general insurer Aviva will keep its operations in Singapore and China, it said on Monday, two days ahead of an expected strategy update.
Aviva began a review of its Asia business earlier this year under new chief executive Maurice Tulloch.
"Following a thorough review of options for the Singapore business, including seeking offers... Aviva has concluded that the best value for shareholders will be achieved by retaining the business," it said.
Aviva also said it would keep its joint venture business in China.
The company added, however, that it was looking at strategic options for its operations in Vietnam, Indonesia and Hong Kong, where recent pro-democracy protests have threatened the territory's position as one of Asia's major financial centres.
Aviva's structure is considered clunky by analysts and investors, who expect Mr Tulloch to unveil a streamlining of operations at the firm's investor day this week.
Sources familiar with the divestment plans told Reuters that very few bidders were interested in the Singapore business given the price tag and also as most global and regional insurers have an existing presence in Singapore.
Reuters reported in September that Aviva was hoping to sell the Singapore and Vietnam businesses, its only wholly owned businesses in Asia, for up to US$2.5 billion (S$3.4 billion).
Bloomberg reported earlier on Monday that Japan's MS&AD Insurance Group Holdings and Canada's Manulife Financial were looking to buy Aviva's assets in Singapore and Vietnam. - REUTERS


Widget Statistic
lorem ipsum dolor sit amet, consectetur adipisicing elit2,563
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8,654
Likes
3,550
Reviews
Published Project
532+1.69%
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Successfull Task
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Ongoing Project
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3.56k
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3k
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2k
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May 23 - June 01 (2017)
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May 23 - June 01 (2017)
Ticket Answered
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3 Year
623
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Unique Innovation
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1 Month
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Clients
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New Orders
231
Orders
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15,678
Total Sales
Total Income :
$2,451
10%
1,678
Total Users
Total Revenue :
$2,451
30%
15,678
Total Project
Active Projects :
$2,451
10%
2476
Total Product
64%
843
New Orders
34%
63%
Conversion Rate
64%
41M
Conversion Rate
54%


An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. C language is rich in built-in operators and provides the following types of operators:
This tutorial will explain the arithmetic, relational, logical, bitwise, assignment and other operators one by one.
Following table shows all the arithmetic operators supported by C language. Assume variable A holds 10 and variable B holds 20, then:
| Operator | Description | Example |
|---|---|---|
| + | Adds two operands | A + B will give 30 |
| - | Subtracts second operand from the first | A - B will give -10 |
| * | Multiplies both operands | A * B will give 200 |
| / | Divides numerator by de-numerator | B / A will give 2 |
| % | Modulus Operator and remainder of after an integer division | B % A will give 0 CHAPTER |
| % | Decrements operator decreases integer value by one | B % A will give 0 |
Try the following example to understand all the arithmetic operators available in C programming language:
#includemain() { int a = 21; int b = 10; int c ; c = a + b; printf("Line 1 - Value of c is %d\n",c); c = a - b; printf("Line 2 - Value of c is %d\n",c); c = a * b; printf("Line 3 - Value of c is %d\n",c); c = a / b; printf("Line 4 - Value of c is %d\n",c); c = a % b; printf("Line 5 - Value of c is %d\n",c); c = a++; printf("Line 6 - Value of c is %d\n",c); c = a--; printf("Line 7 - Value of c is %d\n",c); }
When you compile and execute the above program, it produces the following result:
Line 1 - Value of c is 31 Line 2 - Value of c is 11 Line 3 - Value of c is 210 Line 4 - Value of c is 2 Line 5 - Value of c is 1 Line 6 - Value of c is 21 Line 7 - Value of c is 22
Following table shows all the relational operators supported by C language. Assume variable A holds 10 and variable B holds 20, then:
| Operator | Description | Example | == | Checks if the values of two operands are equal or not, if yes then condition becomes true. | (A == B) is not true. |
| != | Checks if the values of two operands are equal or not, if values are not equal then condition becomes true. | (A != B) is true. |
| > | Checks if the value of left operand is greater than the value of right operand, if yes then condition becomes true. | (A > B) is not true. |
| << /td> | Checks if the value of left operand is less than the value of right operand, if yes then condition becomes true. | (A < B) is true. |
| >= | Checks if the value of left operand is greater than or equal to the value of right operand, if yes then condition becomes true. | (A >= B) is not true. |
| <=< /td> | Checks if the value of left operand is less than or equal to the value of right operand, if yes then condition becomes true. | (A <= B) is true. |
#includemain() { int a = 21; int b = 10; int c ; if( a == b ) { printf("Line 1 - a is equal to b\n"); } else { printf("Line 1 - a is not equal to b\n"); } if ( a < b ) { printf("Line 2 - a is less than b\n"); } else { printf("Line 2 - a is not less than b\n"); } if ( a > b ) { printf("Line 3 - a is greater than b\n"); } else { printf("Line 3 - a is not greater than b\n"); } /* Lets change value of a and b */
a = 5; b = 20; if ( a <= b ) { printf("Line 4 - a is either less than or equal to b\n"); } if ( b >= a ) { printf("Line 5 - b is either greater than or equal to b\n"); } }
When you compile and execute the above program, it produces the following result:
Line 1 - a is not equal to b Line 2 - a is not less than b Line 3 - a is greater than b Line 4 - a is either less than or equal to b Line 5 - b is either greater than or equal to b
Following table shows all the logical operators supported by C language. Assume variable A holds 1 and variable B holds 0, then:
| Operator | Description | Example |
| && | Called Logical AND operator. If both the operands are non-zero, then condition becomes true. | (A && B) is false. |
| || | Called Logical OR Operator. If any of the two operands is nonzero, then condition becomes true. | (A || B) is true |
| ! | Called Logical NOT Operator. Use to reverses the logical state of its operand. If a condition is true, then Logical NOT operator will make false | !(A && B) is true. |
Try the following example to understand all the logical operators available in C programming language:
#includemain() { int a = 5; int b = 20; int c ; if ( a && b ) { printf("Line 1 - Condition is true\n"); } if ( a || b ) { printf("Line 2 - Condition is true\n"); } /* lets change the value of a and b */ a = 0;b = 10; if ( a && b ) { printf("Line 3 - Condition is true\n"); } else { printf("Line 3 - Condition is not true\n"); } if ( !(a && b) ) { printf("Line 4 - Condition is true\n"); } }
When you compile and execute the above program, it produces the following result:
Line 1 - Condition is true Line 2 - Condition is true Line 3 - Condition is not true Line 4 - Condition is true
When you compile and execute the above program, it produces the following result:
Line 1 - Value of c is 12 Line 2 - Value of c is 61 Line 3 - Value of c is 49 Line 4 - Value of c is -61 Line 5 - Value of c is 240 Line 6 - Value of c is 15
There are following assignment operators supported by C language:
| Operator | Description | Example |
| = | Simple assignment operator, Assigns values from right side operands to left side operand | C = A + B will assign value of A + B into C |
| += | Add AND assignment operator, It adds right operand to the left operand and assign the result to left operand | C += A is equivalent to C = C + A |
| -= | Subtract AND assignment operator, It subtracts right operand from the left operand and assign the result to left operand | C -=A is equivalent to C = C - A |
| *= | Multiply AND assignment operator, It multiplies right operand with the left operand and assign the result to left operand | C *= A is equivalent to C = C * A |
| /= | Divide AND assignment operator, It divides left operand with the right operand and assign the result to left operand | C /= A is equivalent to C = C / A |
| %= | Modulus AND assignment operator, It takes modulus using two operands and assign the result to left operand | C %= A is equivalent to C = C % A |
| <<=< /td> | Left shift AND assignment operator | C <<= 2 is same as C=C << 2 |
| >>= | Right shift AND assignment operator | C >>= 2 is same as C = C << 2 |
| &= | Bitwise AND assignment operator | C &= 2 is same as C = C & 2 |
| ^= | bitwise exclusive OR and assignment operator | C ^= 2 is same as C = C ^ 2 |
| |= | bitwise inclusive OR and assignment operator | C |= 2 is same as C = C | 2 |
Try the following example to understand all the assignment operators available in C programming language:
#includemain() { int a = 21; int c ; c = a; printf("Line 1 - = Operator Example, Value of c = %d\n",c); c += a; printf("Line 2 - += Operator Example, Value of c = %d\n",c); c -= a; printf("Line 3 - -= Operator Example, Value of c = %d\n",c); c *= a; printf("Line 4 - *= Operator Example, Value of c = %d\n",c); c /= a; printf("Line 5 - /= Operator Example, Value of c = %d\n",c); c = 200; c %= a; printf("Line 6 - %= Operator Example, Value of c = %d\n",c); c <<= 2; printf("Line 7 - <<= Operator Example, Value of c = %d\n",c); c >>= 2; printf("Line 8 - >>= Operator Example, Value of c = %d\n",c); c &= 2; printf("Line 9 - &= Operator Example, Value of c = %d\n",c); c ^= 2; printf("Line 10 - ^= Operator Example, Value of c = %d\n",c); c |= 2; printf("Line 11 - |= Operator Example, Value of c = %d\n",c); }
When you compile and execute the above program, it produces the following result:
Line 1 - = Operator Example, Value of c = 21 Line 2 - += Operator Example, Value of c = 42 Line 3 - -= Operator Example, Value of c = 21 Line 4 - *= Operator Example, Value of c = 441 Line 5 - /= Operator Example, Value of c = 21 Line 6 - %= Operator Example, Value of c = 11 Line 7 - <<= Operator Example, Value of c = 44 Line 8 - >>= Operator Example, Value of c = 11 Line 9 - &= Operator Example, Value of c = 2 Line 10 - ^= Operator Example, Value of c = 0 Line 11 - |= Operator Example, Value of c = 2
There are few other important operators including sizeof and ? : supported by C Language.
| Operator | Description | Example |
| sizeof() | Returns the size of an variable. | sizeof(a), where a is integer, will return 4. |
| & | Returns the address of an variable. | &a; will give actual address of the variable. |
| * | Pointer to a variable | *a; will pointer to a variable. |
| ?: | Conditional Expression | If Condition is true ? Then value X : Otherwise value Y |
Operator precedence determines the grouping of terms in an expression. This affects how an expression is
evaluated. Certain operators have higher precedence than others; for example, the multiplication operator has
higher precedence than the addition operator.
For example, x = 7 + 3 * 2; here, x is assigned 13, not 20
because operator * has higher precedence than +, so it first gets multiplied with 3*2 and then adds into
7.
Here, operators with the highest precedence appear at the top of the table, those with the lowest
appear at the bottom. Within an expression, higher precedence operators will be evaluated first.
| category | Operator | associativity |
| postfix | () [] -> ++ -- | Left to right |
| unary | + - ! ~ ++ -- (type)*& sizeof | right to left |
| multiplicative | * / % | Left to right |
| additive | + - | Left to right |
| shift | <<>> | Left to right |
| relational | < <=> >== | Left to right |
| equality | == != | Left to right |
| bitwise AND | & | Left to right |
| bitwise XOR | ^ | Left to right |
| bitwise OR | | | Left to right |
| logical AND | && | Left to right |
| logical OR | || | Left to right |
| conditional | ?: | Right to left |
| assignment | = += -= *= /= >>= <<= &=^=|= | Right to left |
| comma | , | Left to right |
Try the following example to understand the operator precedence available in C programming language:
#includemain() { int a = 20; int b = 10; int c = 15; int d = 5; int e; e = (a + b) * c / d; // ( 30 * 15 ) / 5 printf("Value of (a + b) * c / d is : %d\n", e ); e = ((a + b) * c) / d; // (30 * 15 ) / 5
printf("Value of ((a + b) * c) / d is : %d\n" ,e); e = (a + b) * (c / d);// (30) * (15/5)
printf("Value of (a + b) * (c / d) is : %d\n",e); e = a + (b * c) / d;// 20 + (150/5)
printf("Value of a + (b * c) / d is : %d\n" ,e); return 0; }
When you compile and execute the above program, it produces the following result:
Value of (a + b) * c / d is : 90 Value of ((a + b) * c) / d is : 90 Value of (a + b) * (c / d) is : 90 Value of a + (b * c) / d is : 50


[...] In this paper, we introduce a new method that attaches an explicit uncertainty statement to the probabilities of classification using deep neural networks. Precisely, we view that the classification probabilities are sampled from an unknown distribution, and we propose to learn this distribution through the Dirichlet mixture that is flexible enough for approximating any continuous distribution on the simplex. We then construct credible intervals from the learned distribution to assess the uncertainty of the classification probabilities. Our approach is easy to implement, computationally efficient, and can be coupled with any deep neural network architecture. Our method leverages the crucial observation that, in many classification applications such as medical diagnosis, more than one class labels are available for each observational unit. We demonstrate the usefulness of our approach through simulations and a real data example.
[...] We aim to fill this gap and introduce ATMC, an adaptive noise MCMC algorithm that estimates and is able to sample from the posterior of a neural network. ATMC dynamically adjusts the amount of momentum and noise applied to each parameter update in order to compensate for the use of stochastic gradients. We use a ResNet architecture without batch normalization to test ATMC on the Cifar10 benchmark and the large scale ImageNet benchmark and show that, despite the absence of batch normalization, ATMC outperforms a strong optimization baseline in terms of both classification accuracy and test log-likelihood. We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.
To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.
In this article, we’ll look at how deep learning can be used to compress images in order to improve performance when working with image data.
If you liked this article, make sure to check the author's page. You will find other '2019 guides' on object detection, pose estimation, semantic segmentation,...
Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on fixed and handcrafted priors that certainly are insufficient in characterizing clean images and blur kernels, and usually adopt specially designed alternating minimization to avoid trivial solution. [...] To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution. [...]. The process of neural optimization can be explained as a kind of 'zero-shot' self-supervised learning of the generative networks, and thus our proposed method is dubbed SelfDeblur.
The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method.
It is important whenever designing new technologies to ask “how will this affect people’s privacy?” This topic is especially important with regard to machine learning, where machine learning models are often trained on sensitive user data and then released to the public. For example, in the last few years we have seen models trained on users’ private emails, text messages, and medical records. This article covers two aspects of our upcoming USENIX Security paper that investigates to what extent neural networks memorize rare and unique aspects of their training data.


CS 740: Network Bandwidth Visualization Tool
Network Bandwidth (in Gbps)
Between master[<IP_ADDRESS>] & worker-1[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
Between master[<IP_ADDRESS>] & worker-2[<IP_ADDRESS>]
CPU Utilization & Memory Consumption Across The Cluster
Figures shown are in percentages.
Network Bandwidth (in Gbps)
Between master[<IP_ADDRESS>] & worker-3[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
Between master[<IP_ADDRESS>] & worker-3[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
Between worker-1[<IP_ADDRESS>] & worker-2[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
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Network Bandwidth (in Gbps)
Between worker-1[<IP_ADDRESS>] & worker-4[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
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Network Bandwidth (in Gbps)
Between worker-2[<IP_ADDRESS>] & worker-4[<IP_ADDRESS>]
Network Bandwidth (in Gbps)
Between worker-3[<IP_ADDRESS>] & worker-4[<IP_ADDRESS>]
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Project
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a description