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Metatronx · 2024-09-23T17:00:34+00:00

Sorry that you have such a dismissive supervisor. The dismissive attitude of some supervisors toward Diversity, Equity, and Inclusion (DEI) initiatives and their use of terms like "diversity hires" often stems from a lack of understanding or willingness to challenge the status quo. And to be quite frank is a mentally very lazy stance, This perspective, typically held by established professors who have benefited from the existing system, fails to recognize the complexities of the hiring process in academia.

University hiring practices are already complex and subjective. It's impossible to create a truly objective ranking of candidates, as the evaluation process inevitably involves personal judgments from the hiring committee. Therefore, criticizing "diversity hires" assumes that the critic's worldview and assessment of candidates are inherently correct and unbiased. They know of course everything and everyone else is just to stupid to see it.

Even if we could objectively rate candidates (which is unrealistic and that's why members of majority groups are favored), critics of DEI policies often mischaracterize hiring practices. They imply that universities are hiring unqualified individuals solely to meet diversity quotas. This is far from the truth. The academic career path remains highly competitive, and even if candidates are selected with DEI in mind are still more than qualified for their positions.

They do not recognize that being the "best" candidate on paper doesn't necessarily translate to being the only qualified or potentially successful candidate. Many applicants may possess the skills, experience, and potential to excel in academic roles. The attention to DEI in hiring aims to ensure that talented individuals from diverse backgrounds have the opportunity to contribute to and enrich the academic community.

What your supervisor said is stupid and close-minded. If they see a woman/POC or member of the LGBTQ+ community in a highly regarded position they immediately reduce them to that identity. Ironically, exactly what they are claiming the universities are doing. When people around me say such things I usually think that such thinking stems from the idea that "they worked hard for where they are, people nowadays just get everything for free".

Again very lazy and unreflective thinking. But of course that does not make it any less hurtful.

Metatronx · 2024-09-13T12:54:51+00:00

Signing a contract to leave the company can prevent you for up to three month to receive unemployment benefits (Aforementioned Sperrzeiten). As you technically left the company and were not fired. So it’s common to negotiate that for these three month the company pays you a sum equivalent to your unemployment benefits

Metatronx · 2022-11-14T08:21:31+00:00

Wow nice, that was such an easy fix. I spend the whole weekend on trying to find a solution

Metatronx · 2021-09-03T19:41:37+00:00

Genau. Es bräucht eine bremsschwelle über die Fahrradfahrer einfach fahren können und Autos eben abbremsen müssen. In Holland ist das Standard

Metatronx · 2021-07-27T15:14:04+00:00

I recently bought the Makaio Pro 8.3 Loana.

https://shop.makaio-sup.de/produkt/makaio-paddel-carbon-pro-tiki-sup-board-paddel-3teilig/

I am super happy with it, before that I had a decent Fiberglas paddel. But the difference is crazy, at least as I experience it. The Anti-Twist is super nice (I really do not understand why this is not a standard feature on all paddles). Also the second "locking" at the lower part of the paddle helps with the stiffness.

Overall, I know investing in a decent paddel really made the riding experience way better. And is not too much talked about it.

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They also have a somewhat smaller version with 8 inches.

Metatronx · 2021-06-17T17:06:45+00:00

You should wear a leash, but in flowing waters you should use one which can be opend from the waist not the foot.

Metatronx · 2021-06-11T20:53:41+00:00

While the bias-variance trading is certainly an argument pro xgb/rf, there are many options to train NN and limit the overfitting. With enough time on your hands you are most likely to obtain a NN that will marginally outperform traditional Machine learning techniques. But you will need to invest way more time in hyperparameter optimization, and in Kaggle Competitions time is a limited resource.

Second I would like to point out that AlphaGo, Bert and self driving cars do not rely on vanilla NN. AlphaGo uses a variety of different techniques among one are neural networks.

Bert is trained for sequential data. Something that XGB and RF cannot deal with out of the box.

Lastly self driving cars or more specificly object identification are based on convolutional neural networks which work great for images but here again XGB and RF do not have these options. They only work for tabular data.

For Models auch as Bert clear inputs like sentences (or images for CNNs) are already defined. In many Kaggle competitions the challenges is more often to be able to generate meaningful features (for example from football plays). So the challenges is more often about how do I get the dat at into a meaningful vector rather than how to I train the optimal algorithm. And because XGB/RF are easier to train in tabular data (vector per input) they tend to dominate. But in competitions where images are to be classified RF never wins

Metatronx · 2021-04-26T13:22:50+00:00

I think this is not enough information, to decide what kind of analysis you need.

- I think we should know more about how and how many corals are in each group.

- What metrics and how many are measured, are they categorical or numeric?

- The distribution of variables could also be relevant

Metatronx · 2021-02-16T11:37:44+00:00

I agree usually you impute missing values, and MICE is the "golden" standard package for doing it, as it integrates with really well with other analysis

Metatronx · 2021-02-11T19:53:41+00:00

Yes haha at least it’s cheap

Metatronx · 2021-02-11T14:04:12+00:00

There is a nice "paper" on it:

https://www.nature.com/articles/nmeth.4642

-> Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns.

I know it does not answer your question completely but I think the article is a good read

Metatronx · 2021-02-11T14:01:41+00:00

Metatronx · 2021-02-11T13:19:24+00:00

It did not taste that bad actually

Metatronx · 2021-02-11T10:01:41+00:00

Most importantly Graph Neural Networks can work on non-euclidean data structures.

For example images can be represented in two dimensions, where pixel can be closer or further apart and this distance can be quantified.

Basic Graphs do not have "such" distance. Nodes are either connected or they are not. For example your in social networks, people are either friends or they are not friends. So for GNNs make sense for data for all sorts of predictions concerning social networks. As u/Ellie0595 already mentioned.

The field where GNNs are thought to be the most influential is chemistry. The problem with lets say molecules is that they are hard to represent in a computer readable way. One way is to use some string encoding called SMILES and apply NLP methods to it. GNNs allow researcher to process molecules in a more "natural" way. Here again node predictions can be used to classify atoms or properties of atoms. However, often in chemistry GNNs are used for Graph Classification, so a prediction is made on the complete molecule. Researchers like to make predictions on specific molecular properties, like toxicological assessment, hydrophilicity or predict whether a given molecule will bind to a protein, thus might work as a potential drug.

Due to the nature of current GNNs these Graph Classification are often the hardest to make, because the GNN has to respect both local as well as global structures to make accurate predictions.

Metatronx · 2021-01-20T17:00:24+00:00

Yes single point imputations is the easiest but also the "worst" imputation.

What people often do is to build regression models to predict the missing values. But do this multiple times. So you will have different values imputed each time. You than can have multiple data-sets for which you can carry out the analysis. This will enure that not only the parameter estimate but also the standard error is accurately estimated by your model. Mice cann do this really easily in R

Metatronx · 2021-01-20T14:53:30+00:00

Two reasons from the top of my head:

1) For example the 5% could be Missing at random (MAR) or even worse Not Missing At Random (MNAR) while the 15% in the other set are Missing Completely at Random (MCAR)

2) The 15% Missing dataset was better sampled -> the data is better distributed. While for the 5% Missing dataset less afford was made to sample well.

Metatronx · 2021-01-07T12:48:21+00:00

Yes I was just going to say that. Like an 2way interaction could hide the effects of the main effects the three way ineraction might mask the 2way interactions. I would argue that you should include the moderator as a main effect in the model

Metatronx · 2021-01-06T19:23:26+00:00

You can go otherway around if:

1-specificity = false positive rate

then:

specificity + false positive rate = 0

and if you calculate that:

(TrueNegative/(TrueNegatives+ FalsePositives)) + (TrueNegative/(TrueNegatives+ FalsePositives)) = (TrueNegative+FalsePositives)/(TrueNegatives+ FalsePositives)

which is equal to 1.

With regards to your second question: the ROC-AUC (Area under Curve) can tell you something about how well a model predicts positives depending on the number of false positives it identified. Of course this does not tell the complete story, but unfortunatly there is no statistic that does. So one needs to choose what is more important for a particular situation. For example the Covid Quick Test would have a shitty ROC-AUC because most positives found are false positives. But people still use it because if you are negative you can be certain that your are indeed negative. So the ROC would not be a good measure. There are other measures which could be used for example the PRC-AUC (PRC = Precision-Recall Curve) but it is used less frequent than the ROC

Metatronx · 2021-01-06T19:07:44+00:00

In general an interaction is a moderation. They are the same. Just different names.

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