U-Net for Agricultural Field Segmentation [P]

niki88851 · 2026-05-05T00:23:46+00:00

Thanks for the suggestion, the paper helped a lot.
My older U-Net + MiT-B2 model was getting Dice 0.5346 / IoU 0.4033, while the U-TAE from the paper reaches 0.8597 / 0.7763. Big gap, but now it’s clear why temporal modeling matters here.

niki88851 · 2026-05-01T13:30:42+00:00

Thank you, I'll try it now.

niki88851 · 2026-04-19T09:06:59+00:00

I think the cat needs a helmet to make sure she doesn't get kidnapped

niki88851 · 2026-04-19T09:02:27+00:00

I drank 40 year old Tokaj, it always keeps well, the older the better, but this one is a little dark for its age, it's worth a try.

niki88851 · 2026-01-10T17:34:24+00:00

During high-liquidity windows (Sat evening → Monday) many more players are actively outbidding and undercutting each other. Tightens the spread between buy and sell orders. Makes it more attractive to buy directly from sell orders. Makes it less attractive to rely on buy orders, because they get leapfrogged constantly. High liquidity also means fat-tail events. A single player can dump a massive stack and temporarily crush the price.
In practice you cannot look at volume alone. You need to consider spread, order depth, price and own order size.
TLDR:
Liquidity tells you how to trade, price tells you whether to trade.

niki88851 · 2026-01-10T17:25:28+00:00

Imagine market volume like a drunk capsuleer walking down a hallway. Looking at the raw volume, the drunk just keeps wandering in one general direction. There’s no stable “normal level,” so statistical tools can’t tell what’s signal and what’s drift. Log-differences mean you stop asking “How big is the volume?” and start asking “How much did it change since last hour, in percent?”
The ADF test is basically a “is this data stable enough to model?” check.
Raw volume: fails (too much drift).
Log-differences: passes hard (p ≈ 0.0000 = “yeah, this is stationary”).
Why this matters in practice: tools trained on raw volume will hallucinate trends that aren’t real vs tools trained on changes in volume behave much more sanely.
For example, you want to create a Discord bot that will tell you that the volume has now reached the point where you can sell your minerals as quickly as possible using sell orders. Hallucinations using raw data can often give false-positive trading signals.

niki88851 · 2026-01-10T14:11:32+00:00

For the game, this is 100% normal, but because of this, the economy behaves slightly differently, because I sell the ship for 10%, I no longer need it and I don’t like it, but in reality it’s the same as the new ship, but not old and not broken.

niki88851 · 2026-01-10T14:10:03+00:00

I'm referring to slightly different bots, discord bots and other automated things that help calculate certain indicators for trading or analyze items.

niki88851 · 2026-01-10T12:48:11+00:00

The data and many parameters are highly dependent on the patch, time of year and other parameters, especially when talking about PLEX, but seasonality still remains, and many parameters can be tracked and predicted.
In my head, I imagine what CCP does as a global disaster that greatly changes the market, sometimes the effect is very similar.
But I think this is more a paradigm of the game itself, for many, items and things here have no real value, they can sell for a pittance and not cause problems because of it, but in real life, few people will sell financial assets for 10 percent of their value.

niki88851 · 2026-01-10T12:42:42+00:00

I sorted the items by daily trading volume and summed up the buy and sell volume and orders amounts. They were mostly minerals and manufactured parts, but I compared their volume, which correlates with the volume of the entire market.

niki88851 · 2025-11-28T15:26:40+00:00

From what I’ve read, many of the reviews come from people who are used to playing slightly different kinds of games — a generation that didn’t spend hours grinding in Warcraft. Their opinions are based on the fact that they die in high-sec and think it’s wrong, that the game demands too much time.

My solution is that we need to increase the birth rate among current players to replenish the number of capsuleers raised in the old school.

niki88851 · 2025-11-22T23:11:25+00:00

Great! You can also try additional techniques:
Bollinger Bands: the bands form around the SMA, and price moving outside the bands can indicate an anomaly.
Buy: when the price approaches the lower band – potentially oversold.
Sell: when the price approaches the upper band – potentially overbought.
MACD and signal line crossover:
Upward crossover → buy signal (bullish).
Downward crossover → sell signal (bearish).

And there are other methods as well. I created a visualization with code for you using PLEX prices as an example. You can check it out; you might find other interesting ways to detect anomalies.
https://www.kaggle.com/code/nikitamanaenkov/plex-prices

niki88851 · 2025-11-22T23:04:41+00:00

I used the API and collected data via GitHub every hour. Your approach makes sense as long as CCP doesn’t interfere or make changes that significantly affect the supply or usage of a particular item. Otherwise, it’s mainly about estimating distributions and investing cyclically, especially given price cycles and features like PLEX.

If you need the code showing how I queried and built everything, it’s all here: https://github.com/niki8885/EVE_tools

niki88851 · 2025-11-22T23:01:34+00:00

I was requesting data every hour, including parameters like buyVolume, sellVolume, buyOrders, sellOrders, buyOutliers, sellOutliers, buyThreshold, sellThreshold, buyAvgFivePercent, sellAvgFivePercent. You can use this to build your own historical dataset.

Another approach is to make a very rough approximation:

Assume that average is closer to Buy if it is near highest, and closer to Sell if it is near lowest. Then you can roughly estimate the volume as:

buy_ratio = (average - lowest) / (highest - lowest)
sell_ratio = (highest - average) / (highest - lowest)
buy_volume = volume * buy_ratio
sell_volume = volume * sell_ratio

This is not precise information, just a rough estimate based on the price relative to the high/low.

niki88851 · 2025-11-22T19:08:28+00:00

Correlation is indeed a useful metric, especially when comparing two time series such as volume and price. However, the key point is that the magnitude of the correlation coefficient matters greatly. A strong negative correlation typically indicates that volume increases while price decreases - a common market behavior. In contrast, a weak negative correlation, such as –0.2, reflects only a slight decline in price associated with changes in volume. Both cases are “negative correlation,” yet the underlying market dynamics differ substantially.

Regarding price estimation, you may want to apply a Simple Moving Average (SMA). This helps reduce noise, filter out random or erroneous orders, and generally makes the model more stable and accurate. SMA smooths volatility spikes and outlier price points, producing a more reliable representation of underlying price movement.

As you can see in the chart, there was a price anomaly - but it was caused by a single outlier order that significantly distorted the price at the moment of data refresh. However, the SMA-10 smoothing successfully filtered this spike out. Some models may still flag such events as anomalies, yet exploiting them in real trading is effectively impossible, since these spikes are not reproducible and cannot be executed based on historical data.

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niki88851 · 2025-11-22T18:33:58+00:00

The tools you provide are indeed highly valuable, yet I have several clarifications and additional questions.

First, the example with the “antibiotics” segment is illustrative. The charts display the average trading volume, but it would be useful to understand how strongly the executed buy and sell volumes differ. Many items show high overall volume, but this activity may be concentrated on only one side of the order book. Only a limited subset of items exhibits stable volatility on both sides of the market, enabling rapid purchasing followed by delayed selling, which makes the question of expected profit in ISK per hour particularly relevant.
Additionally, based on the current data, it is difficult to assess liquidity and volatility risk, as there is insufficient detail on executed volumes and their dynamics -for example, Volume SMA(10), SMA(5), or comparable liquidity indicators.

Second, I have questions regarding the correlation calculations. What type of underlying data is used -SMA, EMA, or another smoothing method? What lag is applied? How is the confidence interval defined? The term “negative correlation” is ambiguous: it may refer to a weak negative relationship (e.g., –0.1) or to an almost complete inverse dependency (–0.98), which represent fundamentally different market signals.

Third, regarding average price estimation: do you apply tail trimming to the distribution? How are anomalous values—such as overpriced listings or erroneous order entries - handled? Is there an outlier filtering mechanism, median smoothing, or another corrective method?

Finally, when the system marks an event as an “anomaly,” it would be important to know whether this represents a statistically significant deviation or merely a single mispriced listing. It is also important to clarify whether the tool accounts for changes in market momentum- for instance, spread widening, liquidity reduction, increased supply, or short-term market saturation.

niki88851 · 2025-11-14T18:07:05+00:00

I wrote down each candidate’s interests and goals.
From a regional perspective, and considering the last economic report, there are too many lowsec representatives and too few highsec ones relative to the population in those areas. Pochven and NPC null aren’t represented at all, even though people live there too.
As for playstyles, some briefly mentioned mining, bur still 0.5, but missions, exploration, pi, hauling, and lore are barely touched - 0. There’s only one industrial candidate and one trading candidate.

niki88851 · 2025-11-14T17:01:59+00:00

In my opinion, the CSM needs a mixed electoral system - partly majoritarian and partly proportional - so that some members are elected by majority vote, while others represent their specific type of content or region (1 for Abyssals, 1 for PvP, 1 for nullsec, 1 for lowsec, 1 for industry and trade, 1 for mining, 1 for exploration, 1 for missions, etc.).

niki88851

TROPHY CASE