Everything you need to know about mining and crafting in Andromeda, as well as where to find certain materials.

AT Mass Effect Andromeda there are a lot of shootings, but there is still time for research. This will allow you to create new armor, weapons, modifications, upgrades for " nomad" and much more.

Three types of research

AT Mass Effect Andromeda Research data is divided into three categories:

• Milky way research– data related to colonists and their equipment
• Chilius research- data relating to the races found in Andromeda
• Relic Research– data related to ancient abandoned technology

How to scan in Mass Effect Andromeda?

It's very simple. Activate your scanner by clicking on . An object or life form turns orange if it can be scanned.

When scanning a new lifeform or technology, you will receive research data for one of the categories, which can then be used to purchase blueprints.

Blueprinting and crafting in Mass Effect Andromeda

Approach the Research Center, which you can find on a ship or a friendly location, and select " Study". Here you can see which blueprints you have access to and which ones you can buy. Most items have tiers, so you will only get access to the next tier when you already have a lower tier item crafted.

After the drawing appears, go to the item " Development» to create weapons, armor and upgrade the car, if you have it necessary materials. Sometimes rare ingredients may be required.

If you have blueprints for modifications, then you can apply them to crafted items to add an improvement.

Extraction of materials

AT Mass Effect Andromeda there is various ways mining. Minerals are the easiest to mine and can be harvested with Ryder's Omni-Tool.

Mining zones are much richer, but require " nomad". When you set up a forward station, all mining zones appear on common map. Go there by car, turn on the search and follow the schedule. The longer the schedule before the appearance of the drone for mining, the more resources you will receive.

Orbital and system scan

When using the Galaxy Map in System/Orbit mode, you will often receive anomaly warnings from Suvi. After that, you must press / to turn on the scanner, then follow the arrow on the screen to find the anomaly. Once you have found the anomaly, press / to launch the probe. Usually there may be minerals or research data.

). Like an outlier, a “new object” is an object that differs in its properties from the objects of the (training) sample. But unlike an outlier, it is not yet in the sample itself (it will appear after a while, and the task is precisely to detect it when it appears). For example, if you analyze temperature measurements and discard abnormally large or small ones, then you are struggling with outliers. And if you create an algorithm that, for each new measurement, evaluates how similar it is to the previous ones, and throws out anomalous ones, you are “fighting novelty”.

Emissions are a consequence of:

• data errors (measurement inaccuracies, rounding off, incorrect entries, etc.)
• the presence of noise objects (incorrectly classified objects)
• the presence of objects of "other" samples (for example, the readings of a broken sensor).

Rice. 1. Model problem with two features

On fig. 1 shows that noise (noise) is an outlier “in a weak sense” (it can blur class/cluster boundaries a little). We are primarily interested in outliers "in the strong sense" that distort these boundaries.

Novelty, as a rule, appears as a result of a fundamentally new behavior of the object. Let's say, if our objects are descriptions of the system's operation, then after the virus penetrates into it, the objects become "novelty". Another example is the description of the operation of the engine after a breakdown. It is important to understand here that "novelty" is called novelty for the reason that such descriptions are completely new to us, and they are new because we cannot have information about all kinds of virus infections or all kinds of breakdowns in the training sample. The formation of such a training sample is labor-intensive and often does not make sense. On the other hand, it is possible to collect a sufficiently large sample of examples of normal (normal) operation of a system or mechanism.

Applications here are the sea:

• Detection of suspicious banking transactions (Credit-card Fraud)
• Intrusion Detection
• Detection of non-standard players on the exchange (insiders)
• Detection of malfunctions in mechanisms based on sensor readings
• Medical diagnostics (Medical Diagnosis)
• Seismology

It should be noted that there are also many possible problem statements. For example, the Positive-Unlabeled Classificatio n (PU learning) task is when some of the outliers are labeled (class 1), but the remaining learning objects (class 0) may also contain outliers. For example, an expert told us that the equipment failed at such and such moments, but he could not notice all the failures.

Even when anomaly detection problems are similar to regular classification problems, there are quirks, such as class imbalances (for example, equipment breakdowns are relatively rare).

Anomalies are not only in tabular data, they can be in graphs, time series, etc.

Rice. 2. An example of outliers in a time series.
Rice. 3. An example of outliers in graphs and sequences.

Quality functionals in anomaly detection tasks are used approximately the same as in classification tasks: PR AUC, AUROC , here everything is determined by the task context (customer).

Outlier detection methods

1. Statistical tests

As a rule, they are used for individual features and extreme values ​​​​are caught (Extreme-Value Analysis). For this, for example, Z-value or Kurtosis measure is used.

Rice. 4. An example of outliers.

Any practitioner has some proven way of finding extreme values ​​for certain data types. Many visualization methods, such as the whisker box, have built-in tools for detecting and displaying such extreme values.

It is important to understand that extreme value and anomaly are different concepts. For example, in a small sample

Rice. 5. An example of outliers in a problem with two features.

2. Model tests

The idea is very simple - we build a model that describes the data. Points that strongly deviate from the model (at which the model is very wrong) are anomalies (see Fig. 2). When choosing a model, we can take into account the nature of the problem, the quality functional, etc.

Such methods are good for determining novelty, but work worse when looking for outliers. Indeed, when setting up the model, we use data that has outliers (and it is “sharpened” for them).

Rice. 6. Using SVD to Find Outliers in a Matrix

On fig. 6 shows the application of the model approach. We have a matrix and we need to find outliers in it. We use an incomplete singular value decomposition (SVD) to find a matrix of small rank that is as similar as possible to ours (for clarity, all numbers are rounded). Elements that are very different from the corresponding elements of the low-rank matrix will be considered outliers.

3. Iterative methods

Methods that consist of iterations, each of which removes a group of "highly suspicious objects". For example, in an n-dimensional feature space, we can remove the convex hull of our points-objects, considering its representatives as outliers. As a rule, the methods of this group are quite laborious.

Rice. 7. Convex hulls of a set of points.

4. Metric methods

Judging by the number of publications, these are the most popular methods among researchers. They postulate the existence of some metric in the space of objects, which helps to find anomalies. It is intuitively clear that an outlier has few neighbors, while a typical point has many. Therefore, a good measure of anomaly can be, for example, "the distance to the kth neighbor" (see the Local Outlier Factor method). Specific metrics are used here, such as the Mahalanobis distance.

Rice. 8. Neighbors of several sample elements, the relationship with 5m is shown in red

5. Methods of task substitution

When a new problem arises, there is a great temptation to solve it with old methods (oriented to already known problems). For example, you can do clustering, then small clusters are likely to consist of anomalies. If we have partial information about anomalies (as in the PUC problem), then we can solve it as a classification problem with classes 1 (labeled anomalies) and 0 (all other objects). If class 0 consisted only of normal objects, then such a solution would be completely legal, otherwise it remains to be hoped that there are few undetected anomalies in it.

Rice. 9. An example of clustering into a small (red) and a large (blue) cluster.

6. Machine learning methods

But what if we perceive the problem of finding anomalies as a new machine learning problem (different from classification and clustering)?!

The most popular algorithms (there is an implementation even in scikit-learn) are here:

• Single Class Support Vector Machine (OneClassSVM)
• Isolation Forest (IsolationForest)
• Ellipsoidal Fit to Data (EllipticEnvelope)

Rice. 10. Visualization of the operation of various anomaly search algorithms.

• kernel– kernel (linear: linear, polynomial: poly, radial basis functions: rbf, sigmoidal: sigmoid, custom)
• nu– upper bound on % errors and lower bound on % support vectors (0.5 by default)
• degree is the degree for the polynomial kernel
• gamma- coefficient for the kernel feature (1/n_features by default)
• coef0– parameter in the polynomial or sigmoidal kernel function

• n_estimators- number of trees
• max_samples- sample size for building one tree (if a real number, then the percentage of the entire sample)
• contamination is the proportion of outliers in the sample (for choosing a threshold)
• max_features– the number (or %) of features that are used when building one tree (so far it only works with the value 1.0)
• bootstrap– enable bootstrap mode when subsampling

7. Ensembles of algorithms

The idea of ​​“one algorithm is good, but a hundred is better” has also penetrated into methods for solving anomaly detection problems, so many different algorithms are often built. Each of them gives an estimate of the anomaly and these estimates are then “averaged”.

Since the key point in real anomaly detection problems is the choice of features that characterize certain deviations from the norm, ensemble algorithms are built trying to guess good spaces. Here are popular:

• Feature Bagging(not a very good name) - for each algorithm, a random feature subspace is taken,
• Rotated Bagging– in the selected random feature subspace, a random turn is made.

By the way, here "averaging" does not necessarily mean the arithmetic mean of all estimates, it is intuitively clear that the maximum can often work (if some algorithm is sure of the anomalous object, then most likely it is).

History from practice

In anomaly search tasks, it is important to understand how search algorithms work and explain this to the customer. For example, when the author was last involved in solving a similar problem, the customer wanted a tool for detecting breakdowns, but due to the nature of the model, the algorithm turned out to detect "malfunctioning equipment", i.e. it gave a signal not only in case of breakdowns, but also in case of incorrect operation of the device, as well as when working in very rare modes. Some breakdowns (very frequent) he still missed, because. "they have already become the norm for the device." It is clear that if there was a large labeled sample, such problems would not arise, but in practice the equipment does not work for so long, there are also few breakdowns (and not all possible ones happen), and some breakdowns might not be noticed or noticed belatedly. In addition, some breakdowns do not affect the readings of the sensors in any way. Initially, the customer was very upset about the quality, but when they explained how the algorithm works, the customer checked the test data and made sure that the algorithm is very useful, even if it does not find any breakdowns: it can be used as a verifier “whether the device is working in normal mode” , and this is the most important thing.

P.S. The code for obtaining Fig. 10 can be taken.

(nuclear power plants, underground communications -, mines; landfills, underground communications, cemeteries);
- in the places of his residence (due to conflicts, as well as due to various objects that make changes to the energy structure of the room - paintings, figurines, etc.);
- in places of impact of UFOs and other unknown objects on the surface.
These abnormal zones are harmful to the mental and physical health person. It is not for nothing that among the people they are “dead” places. But they also have another, downside: in anomalous zones several times more often than in ordinary places, poltergeist phenomena, interactions with UFOs occur, and these places, according to parapsychologists, are a portal to parallel worlds.

Although anomalous zones are not yet known to science, it has been noticed that the zones are located near groundwater (they create a circular movement of energy) or river turns. The presence of powerful geopathic bands in such a place was also noted. In addition, anomalous zones are located in places of stone exits, faults.

In the study of anomalous zones, the accumulated information and intuition of the researcher are not of last importance. Conclusions about the nature of the zone can be drawn during measurements of energy fields, according to appearance terrain, animal behavior, vegetation. After the overall picture is obtained from these signs, it will be possible to draw conclusions. Having sufficient experience in the study of anomalous zones, one can form an idea of ​​the zone already when approaching it. And also to reveal the nature of the zone, or at least its influence on a person and the danger when visiting this place.

Therefore, a separate warning for those who are accustomed to act immediately: visiting anomalous zones is not safe for physical health, mental health, and even for the life of a researcher. Anomalous zones have a strong, usually negative impact on human energy structures. When measuring the energy fields of a person before and after visiting the zone and after, they showed the following: after visiting such a zone, the human energy has a “torn structure”. In addition, if the usual human biofield has an ovoid shape, only slightly flattened on the sides, then after visiting the anomalous zone, this one destroys the integrity of the cocoon, and in some cases it may not be visible at all. All this is fraught with health problems. As a result, it is necessary for the researcher to keep their energy structures in a balanced and harmonious state, for which it is necessary to perform special exercises (for example, techniques for the chakras).

There are a lot of signs of the presence of an anomalous zone. Here is some of them:
- when conducting biomeasurements, the pendulum or dowsing frame behave in a strange way;
- the gaze involuntarily clings to a certain area, is attracted to it;
- the absence of all living creatures in local places;
- Strange-looking vegetation that differs in some way from that located near the zone;
- inadequate behavior of animals;
- various kinds of sounds, the appearance of which defies logical explanation;
- emerging pictures and visions;
- the presence of strange objects;
- Unusual sensations in the body and psyche while in the zone.

Comment from AngryClown

Video posted: http://www.youtube.com/watch?v=CeBEGH4zTKc shows the location of the anomaly. My UI wouldn't show that yellow orb that you see in the video, but didn't stop me from being able to run to that exact location and bang out the quest in two seconds.

Simply follow the ramp up out of the horde area, and hug the right wall, past the stairs that lead up to the alliance area (don "t go up it). Maybe 20 yards past the stairs along the wall is where you"ll find it.

Comment from zazz999

For those who are having trouble finding the Anomaly for various reasons.
Go to 63, 26 in the mine and you should complete the quest.

Comment from jtbalogh

I found the orb at a different spot than my friend on the same quest and same day.
For example, see

55, 26 (end of tracks in the middle of the room)
or 63, 26 (against the wall)
or 58, 27.4 (another wall)
or 62.7, 21.6 (between tracks and stairs)

Follow the meter in case the coordinates change next time.
- The orb only appeared when the meter was 97% (mouse over the meter to see %)
- The orb takes a second to appear, so do not run too fast past it.
- The orb is on the same level as the spiritbound mobs in the Ruins of Ogudei area.

Comment from bratmonkey

for those of you who are wondering what the "tracking device" is that you are supposed to use: it is automatic. there isn't an item in your bag that you have to "use". simply walk to the area where the indicator says you are at 100% and it will automatically give you credit for completing the quest.

Comment from Chiengmai

I just wanted to add that I did this quest today in the anomaly was on the opposite side. It was in the same location in the tunnel but on the other side.

Comment from tacgnol

Quest name is likely a reference to an old Something Awful meme.

Comment from Thair

You don "t need any special addons or coords for this quest, just run in the part of the cave where the daily quest mobs are located and look at your meter (the orb-looking thinguie in the middle of the screen). If it "s getting bigger, you" re going in the right direction. Just keep running until it "s full and it will complete the quest.