Why Is My Qt Widget’s Focus Being Changed?

I am searching further. Problem: In the tactic GFrame::updateRect at least the Delhi Call Girl to QWidget::adjustSize is causing the focus to be stolen from the QLineEdit. Probably setMinimumSize has something to do with it as properly, but I did not take a look at it. Solution: I’d advise you to comment each calls to GFrame::updateRect in GFrame::eventFilter. Then it should work as you count on. Further recommendation: you might remark the calls to QLineEdit::setFocus in GFrame::eventFilter as properly, as they are not essential. Let me attempt something and I will get again to you with the end result. Well, the decision to setMinimumSize alone does not trigger the focus to be stolen. The decision to adjustSize alone does not not trigger the main focus to be stolen both. I am afraid I don’t agree with you. In GFrame::eventFilter comment both calls to updateRect and it’ll work as anticipated. Then substitute them with calls to adjustSize and the problem is back. Thanks for contributing an answer to Stack Overflow! Please you’ll want to answer the question. Provide particulars and share your research! Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or private experience. To learn more, see our recommendations on writing nice solutions. Not the reply you are searching for? Wish to be great at UX research? Take a cue from cultural anthropology (Ep. Can PCs cause thunder harm (sonic harm) with simple goal-constructed non-magic gadgets? Why can’t Ukraine ship grain from Mariupol? Can I buy my firm internship laptop computer? Will they hunt me down if I keep it?

Miller’s public statements first piqued the committee’s interest. He vowed “alternate electors” would keep Trump in power in an interview with Fox News in late 2020. Those alternate electors sent bogus slates from battleground states to the National Archives for certification on Dec. 14, 2020. The Archives rejected them because those that had signed were not acknowledged by their respective state officials as certified electors. Miller also helped write the speech that could be delivered from the Ellipse on Jan. 6. He was at the White House on the morning of Jan. 6 and accompanied Trump on their short trip to ‘Stop the Steal’ rally. During his deposition in April, it was broadly reported that Miller fielded questions concerning the language within the speech, particularly how Trump typically referred to “we” in his remarks, together with those occasions when he told the crowd gathered at the Ellipse that “we are going to stroll down Pennsylvania Avenue” and “we are going to the Capitol.” Miller allegedly defended the language by saying it was political rhetoric.

We make full use of this native property to evaluate the entire matching. Related Work. Recently, the Hospitals/Residents drawback with decrease quotas is kind of fashionable in literature, however most of them are on the settings without ties. The problem can primarily be categorised into three fashions. The closest to ours is the mannequin by Hamada et al. In this model, the enter is precisely the identical but the arduous and tender constraints are opposite to ours, i.e., their resolution should fulfill all the decrease quotas, while the target is to maximize the stability (e.g., to reduce the variety of blocking pairs). Another model, introduced by Biró et al. They confirmed NP-completeness of figuring out the existence of a stable matching. Paper Organization. The remainder of the paper is organized as follows. Part 2 formulates our problem HR-MSLQ and Part 3 describes our algorithm. Sections four and 5 are respectively dedicated to the proof of technique-proofness and approximation ratio. Part 6 gives hardness results comparable to inapproximability results for each mannequin and NP-hardness in a number of restrictive settings.

ML, where the price is the euclidean distance between vertices. In addition, it is also offered a vector indicating whether or not that edge is in the present partial answer or not. ResNet architecture introduced by Mele et al. The latter exhibits the best performance on the test set. The ML-Constructive gives good approximated tours, which can nonetheless nonetheless be improved. These tours have some flaws, because it is possible to get some crossing edges in them. Generally, such local search compares every attainable couple of edges. However, since we are fairly assured about what has been achieved in the first part, here we try to improve solely the edges obtained throughout the second part. The edges that have been inserted by the ML fashions (first part) will not be modified. 54 situations chosen by Mele et al. The size of the situations varies between a hundred and 1748. A brief recap of the results – with the heuristic executed using several ML fashions in the primary part – is proven in Table 2. A more detailed version of this table could be present in the web compendium, the place the results of every occasion are proven and mentioned. The first column B is the baseline, while NN confirms an edge if it connects the closest node within the CL. The opposite columns show the efficiency using some ML fashions; the column ML-C exhibits the results of the execution of the unique ML-Constructive algorithm.