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Dallas Regionals 2020 – My Team

Hi everyone! I am going to discuss the team I used for Dallas Regionals 2020, I know it has been a couple weeks since but I spent a long time designing this team (about 500 hours of gameplay). This team got me 266th out of 549 masters.

The team mainly relies on setting trick room, but was also effective if trick room wasn’t able to be activated.

Imp the Grimmsnarl! One of the best screen setters in the game! Light clay as item to extend the screens. Prankster ability is the best ability for Grimmsnarl, grants priority for non-damaging moves. Taunt came in handy against other set-up mons to force them to attack. The last slot was up for debate for a while. Originally I had play rough to maximize damage from its fairy type, it then switched to its signature move Spirit Break which lowers the foe’s special attack stat by one stage. This wasn’t working out too well, so I ended up going with Foul Play which power depends on the foe’s attack stat. This came in handy against belly drum opponents and physical based Dragapult. Max HP was for surviving some hits.

Image result for grimmsnarl

Imp (Grimmsnarl) (M) @ Light Clay  
Ability: Prankster  
Level: 50  
EVs: 252 HP / 252 Atk / 4 SpD  
Adamant Nature  
IVs: 0 Atk  
- Taunt  
- Reflect  
- Foul Play  
- Light Screen  

THICCC the Mudsdale! The most important member on my team! Assault vest for that extra special defense buff. Stamina over OwnTempo since I tend to play the long game and it increases defense by one stage every time it’s hit with a move. This is also my go to dynamax. Max HP for bulk, max attack for damage output. 0 speed IV for trick room. High horsepower for STAB, heavy slam for fairies, closecombat for snorlax and rockslide for rock on Togekiss and flinch chances.
THICCC (Mudsdale) (M) @ Assault Vest  
Ability: Stamina  
Level: 50  
EVs: 252 HP / 252 Atk / 4 SpD  
Brave Nature  
IVs: 0 Spe  
- High Horsepower  
- Heavy Slam  
- Close Combat  
- Rock Slide  

Hat Lady the Hatterene! The best of special attackers in game. 136 base special attack stat maxed out at 206 at level 50. Quiet nature to lower speed and boost special attack. HP for bulk, which at this point you would notice my team is very much on the bulky side. 0IV in speed for trick room and 0 attack to mitigate foul play damage. Focus sash for surviving a hit and having a better chance of setting up trick room. One of two trick potential trick room setters on the team. Dazzling Gleam and Psychic for STAB. The last slot I had originally thougth about mystical fire, but I have another Pokémon on my team that takes care of fire type moves, I have decided with Giga drain as a good counter to gastrodon and rotom-wash.
Image result for hatterene
Hat Lady (Hatterene) (F) @ Focus Band  
Ability: Magic Bounce  
Level: 50  
EVs: 252 HP / 252 SpA / 4 SpD  
Quiet Nature  
IVs: 0 Atk / 0 Spe  
- Trick Room  
- Dazzling Gleam  
- Psychic  
- Giga Drain  

Smoke the Torkoal! The secret late game sweeper. Torkoal has been slept on by a lot, its massive 140 base special attack, with max spa, charcoal, hasrh sunlight (Drought ability) and STAB from Eruption deals a lot of damage including to resisting types. 0atk and 0speed iv’s for the same reason as Hatterene and max HP IV’s for the bulk. Eruption for STAB and full health and Heat Wave for when HP is hindered with a chance of burn. Drought setting up the sun for a one turn Solar Beam to counter ground/rock and water types and earth power for coverage.
Smoke (Torkoal) (M) @ Charcoal  
Ability: Drought  
Level: 50  
EVs: 252 HP / 252 SpA / 4 SpD  
Quiet Nature  
IVs: 0 Atk / 0 Spe  
- Eruption  
- Solar Beam  
- Heat Wave  
- Earth Power  

ArmStrong the Conkeldurr! Another pokemon with a ridiculous stat, 140 base attack stat and max attack with positive nature reaches 211 at level 50. Life orb for an added boost in attack power. Iron fist ability boost the power of punching moves, which all four of its moves are. Mach Punch for priority, ice punch and thunder punch for coverage, drain punch for STAB and HP regeneration. Max HP for bulk. Base 45 speed is slow enough for TR, so brave nature for slower speed, 0IV for the same reason.
ArmStrong (Conkeldurr) (M) @ Life Orb  
Ability: Iron Fist  
Level: 50  
EVs: 252 HP / 252 Atk / 4 Def  
Brave Nature  
IVs: 0 Spe  
- Mach Punch  
- Ice Punch  
- Drain Punch  
- Thunder Punch  

Ring the Bronzong! Last but not least, a very important support to the team. Bronzong has been a great teammate for best of three games where I won’t reveal its complete move-set right away. This is the second trick room setter in the team. Gyro ball for stab and damage. Safeguard came in handy to protect Mudsdale from will-o-wisp or scald burns. Max HP and Defense as it is my wall. Leftovers for HP recovery every turn. Ally Switch was the most useful in the third game as it would through them off and could get me a turn one knockout with my mudsdale.
Ring (Bronzong) @ Leftovers  
Ability: Levitate  
Level: 50  
EVs: 252 HP / 252 Def / 4 SpD  
Relaxed Nature  
IVs: 0 Spe  
- Trick Room  
- Gyro Ball  
- Ally Switch  
- Safeguard

 

 

 

Image

Here is a the Rental Code if you want to try my team out!
rental_dallas_team

Graph Mining for HPC Analytics – Neo4j and Property Graphs

Hi everyone, and welcome back to another blog post. I will explain the basics of what make a property graph and how to use a tool called NEO4J to create a property graph based database.

What is a property graph? They have the same definition as most graphs. They are composed of vertices edges (G = {V, E}. In property graphs the terminology for these is nodes and relationships. Along with having nodes and relationships, what makes a property graph unique are whats called the properties, which is the information that can either be attached to the nodes or the relationships.

Let’s discuss how nodes are defined. They are considered entities. They can hold any amount of data as key-value pairs (properties). Nodes can also be labeled, to specify what domain in the graph they belong to (sub-graphs).

Relationships are similar to edges in a traditional graph except they have more minimum restrictions in a property graph. Relationships in a property graph must include a start node, end node, direction and a type. Just as nodes have properties, relationships can also hold properties. Moreover, even though relationships have a direction, they can be easily traversed in any direction.

Where does neo4j come into play? Neo4j is a native graph database that implements the data structure for the property graph mentioned above. Neo4j is a open-source, NoSQL that provides a ACID compliant backend seen in many other databases. In the next blog we will dive into more specifics of Neo4j, and how to use a declarative query language called Cypher that is similar in many ways to SQL but is optimized to work with graph databases.

Graph Mining for HPC Analytics – Introduction

Hello, my name is Luis Bobadilla. I will be writing my findings as I conduct my research for my masters thesis. In this post I will set the background and related work that my thesis is based on.

As a graduate research assistant for the laboratory for knowledge discovery in databases (KDD Lab), a machine learning research lab, I am tackling a problem for an ongoing project that many students are on. The project is named the HPC Analytics project.

At Kansas State University we have a high performance computing cluster called beocat (https://beocat.ksu.edu/). Our goal is attempt to have the most efficient system utilization and resource allocation cluster. Beocat users span from all disciplines at the university, from biology, statistics, and a range of engineering departments. When users submit their jobs to beocat they specify the number of nodes, cpus, memory and time limit. This sometimes leads to an issue for less experienced users. They can either underestimate or overestimate some of those parameters leading their job to fail or use up unnecessary system resources.

As a team, we have done a few approaches to predicting the memory and cpu needed for a given job. These approaches can be read in the following two papers.

1. http://kdd.cs.ksu.edu/Publications/Conference/andresen2018predictive.pdf
2. http://kdd.cs.ksu.edu/Publications/Conference/tanash2019improving.pdf

What I’m working specifically is adding a few more features to the dataset involving role extraction. In the next post we will discuss the set-up for the graph database used (NEO4J).