prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the score achieved by the authors for Track-2?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Two-stage Pipeline for Multilingual Dialect Detection
Publish Date: Unkown
Author List: Ankit Vaidya (from Pune Institute of Computer Technology), Aditya Kane (from Pune Institute of Computer Technology)

Figure

Figure 1: Class distribution of dialects
Figure 2: System diagram for dialect classification The LID classifies the input into one of 3 languages The sample is then further classified into dialects by language specific models 
Figure 3: Confusion matrix of 9-way classification Note that rows are normalized according to the number of samples is that class 
Our complete results for Track-1 using the two-stage dialect detection pipeline Model-* denotes the language of the models used for the experiments 
Performance on Track-1 validation dataset of individual models used in the two-stage pipeline "Lg" stands for language of the model used 
Comparative results of two-way classification using the finetuned (F T ) predictions and predictions adapted from three-way classification models 

abstract

Dialect Identification is a crucial task for localizing various Large Language Models  This paper outlines our approach to the VarDial 2023 DSL-TL shared task  Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively 
Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain  We achieve a score of 58 54% for Track-1 and 85 61% for Track-2  Our codebase is available publicly 1  

Introduction

Language has been the primary mode of communication for humans since the pre-historic ages  Studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language   Inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region 
This gave rise to multiple national dialects of the same language  The VarDial workshop (colocated with EACL 2023) explores various dialects and variations of the same language  We participated in the Discriminating Between Similar Languages -True Labels (DSL-TL) shared task  In this task, the participants were provided with data from three languages, with each language having three varieties 
This shared task consisted of two tracks -Track-1 featuring nine-way classification and Track-2 featuring six-way classification  The second track included two particular national dialects of each language (eg  American English and British English), and the first track had one general  We ranked 1 st in both of the tracks 
Moreover, we beat the next best submission by a margin of 4 5% in the first task and 5 6% in the second task We were the only team to surpass the organizer baseline scores  We present our winning solution in this paper  We used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language 
We converged upon the best combination by doing an elaborate analysis of various models available  Furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study  Lastly, we provide some future directions in this area of research 

Related Work

The present literature encompasses various aspects of dialect identification  We study this from three perspectives: large language models, language identification and dialect classification problems 

Large Language Models

The success of transformers and BERT based models was inevitable since the initial boom of the transformer  2017) model  In recent years, many other architectures like RoBERTa and ELECTRA have further pushed the state-of-the-art in this domain  Moreover, autoregressive models like GPT and GPT-2 have also shown their prowess 
Multilingual versions of RoBERTA, namely XLM-RoBERTa are also available  Lastly, language specific models like Spanish BERT (la Rosa y Eduardo G  Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury, 2022) and Portuguese BERT are available as well  Our winning solution makes use of these large language models trained on specific languages 

Language Identification Models

Many multilingual language identification models have been developed in order to classify the language of the input sentence beforehand  Even though the initial works used n-gram models and generative mixture models or even conditional random fields and other classical machine learning methods like naive bayes , modern methods have shifted to the use of deep learning for language identification  
Recent works have mainly focused on deep learning based language identification, where handling codemixed data is a big challenge in the domain  For our experiments, we use a version of XLM-RoBERTa finetuned on a language identification dataset 2   This model has near-perfect test accuracy of 99 6% 

Dialect Classification

Dialect classification has been previously solved using statistical methods like Gaussian Mixture Models and Frame Selection Decoding or Support Vector Machines (SVM)   It has been explored relatively sparsely, mostly in the case for local languages   Deep learning approaches have been explored in previous editions of the VarDial workshop shared tasks and otherwise  
Dialect classification was also explored previously as a part of other shared tasks   We want to stress that given the multilingual nature of the dataset, using the present methods directly was not an option  In our work, although we take inspiration from the previous works, we propose a novel system that surpasses the performance of the previous systems by a large margin 

Data

The dataset  We observed that the class PT-BR had the most number of samples (2,724) and the class EN had the least number of samples (349), and thus the imbalance ratio was almost 1:8  We have illustrated the data distribution in Figure   We tried to mitigate this imbalance using over-sampling and weighted sampling methods 
However, the improved data sampling method did not affect the performance 

System Description

This was a problem of multi-class classification having 9 classes for Track-1 and 6 classes for Track-2  The samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages  The Language Identification (LID) model which is the first stage classifies the sentence into 3 languages: English (EN), Spanish (ES) and Portuguese (PT) 
The LID is a pretrained XLM-RoBERTa that is fine-tuned for the task of language identification  It is able to classify the input sentence into 20 languages  We classify and separate the samples according to their language  The samples corresponding to the specific languages are then fed into the language specific models for dialect identification 
For dialect identification we have used models like BERT and RoBERTa with a linear layer connected to the pooler output of the models  Then fine-tuning is done on the models for dialect identification   using the samples corresponding to the specific languages  For the task of dialect identification we experimented with several pretrained models like XLM-RoBERTa, BERT, ELECTRA, GPT-2 and RoBERTa 
All models were fine-tuned for 20 epochs with a learning rate of 1e-6 and weight decay 1e-6 with a batch size of 8  The best performing model checkpoint was chosen according to the epoch-wise validation macro-F1 score  5 Experiments and Results

Experiments using Large Language Models

For the task of Dialect Identification we have tried various language specific models like XLM-RoBERTa, BERT, ELECTRA, RoBERTa and GPT- 2  The base variant of all these models were used and all the models were used through the Hugging-Face library  The pooler output of these models was passed through a linear layer and the models were fine-tuned 
First, we experimented with different models for Track-1  All the models were trained for 20 epochs with learning rate 1e-6, weight decay 1e-6 and a batch size of 8  We used XLM-RoBERTa as the baseline for all 3 languages  The best performing models for the English language were RoBERTa and BERT whereas GPT-2 was the worst performing 
Similarly the language specific versions of RoBERTa and BERT performed well for the Spanish and Portuguese respectively  Overall the worst performing model was GPT-2 across all 3 languages  The validation F1 scores are present in Table   The two best-performing models for every language were chosen for Track-2 
The same procedure as specified above was used and the F1 scores are present in Table   The train and validation F1 scores for 2-class classification are higher for all models as compared to the F1 score of the same models for 3-class classification  This was mainly due to the poor representation and accuracy of classification of the third class 
We observed symptoms of overfitting in all models after 12-15 epochs and the best validation F1 score was obtained in the range of 4-8 epochs 

LID experiments

The pipeline for dialect identification is divided into two parts as the sentences in the dataset belong to different languages  The stages are described in Section 4  The XLM-RoBERTa we have used for language classification has a test accuracy of 99 6% meaning it correctly classifies all input sentences and hence, can be considered as a perfect classifier 
For the final pipeline we experimented using the two best performing models for each language in Track-1 and Track-2  For both the tracks we experimented with all 8 (2 3 ) possible combinations of models and calculated the validation F1 score for the combined validation dataset which had sentences belonging to all languages 
The validation scores for Track-1 and Track-2 are shown in Table and Table respectively  For both the tracks, the three pipelines with the best validation F1 scores were chosen for submission 

Using 3-way classifier as a 2-way classifier

In Track-1, participants are expected to train a classifier which classifies amongst 9 classes, and in Track-2, participants are expected to train a classifier which classifies amongst 6 classes  These 6 classes are a proper subset of the 9 classes from Track-1  Thus, an intuitive baseline for Track-2 is to use the model finetuned for Track-1, whilst considering only the relevant classes for the latter task 
The classes EN , ES and P T , i e  the classes without any national dialect associated with them are not included in Track-2 as compared to Track-1  Thus, we calculate the predictions for the Track-2 validation dataset using the models for Track-1 and exclude the metrics for Track-1 specific classes to get the metrics for this "adapted" 2-way classification 
We show the results of this experiment in Table and observe that, as expected, the adapted 2-way classification performs worse compared to the explicitly finetuned variant 

Results for Track-1 and Track-2

We now present our experiments and their performance for both tracks  Our experiments for Track-1 are described in Table and our experiments for Track-2 are described in Table   The participants were allowed three submissions for evaluation on the test set, so we submitted predictions using the three systems which performed the best on the validation set 
As mentioned in Section 5 2, we performed 2 3 , i e  a total of 8 experiments using the two best models for each language  We observed that RoBERTa base on English, Spanish BERT base on Spanish and Portuguese BERT base performed the best on the testing set for Track-1  The same combination, with RoBERTa base for English, worked best for Track-2 
All of our submissions were the top submissions for each track, which surpassed the next best competitors by a margin of 4 5% and 5 6% for Track-1 and Track-2 respectively 

Ablation of best submissions

We hereby make some observations of our submissions and other experiments  To assist this, we plot the confusion matrices of our best submissions for Track-1 and Track-2 in Figures respectively  Note that these confusion matrices have their rows (i e  true labels axes) normalized according to the number of samples in the class 
Here are observations from our experiments: 1  BERT-based models outperform other models across all languages: We observe that BERT-based models outperform ELECTRA-based and GPT-2-based models, as shown in Table   We speculate this is because of the inherent architecture of BERT, which combines semantic learning with knowledge retention 
This combination of traits is particularly useful for this task  2  Common labels perform the worst across all languages: We observe that the common labels EN , ES and P T perform the worst, both in the individual as well as the two-stage setup  We hypothesize this is because of the absence of dialect specific words, or words that are specific to the geographical origin of the national dialect (for example, "Yankees" for EN-US and "Oxford" for EN-GB) 
3  English models work better than models of other languages: It can be noted from Figures 4 and 3 that the English models have the best performance across all classes  This can be attributed to two reasons: absence of national dialect specific words and lesser pretraining data in the case of Portuguese 
4  British English is most correctly classified class: We can observe that the Spanish or Portuguese models make equal number of mistakes in the case of either national dialect, in the case of Track-2 (see Figure )  However, in the case of English, the label EN − GB is correctly classified for more than 95% of the cases 
We speculate this is because British English involves slightly distinctive grammar and semantics, which help the model separate it from other classes  5  The proposed 2-step method is scalable for multiple language dialect classification: We can strongly assert that the novel 2-step deep learning method for multilingual dialect classification is a scalable method for the task due to two specific reasons: firstly, the multilingual models (like XLM-RoBERTa) might not have the vocabulary as well as the learning capabilities to learn the minute differences between individual dialects 
Secondly, this system can be quickly expanded for a new language by simply adding a language specific dialect classifier, provided the language identification model supports that particular language 

Conclusion

In this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora  We conduct thorough ablations on this setup and provide valuable insights  We foresee multiple future directions for this work  The first is to expand this work to many languages and dialects 
Secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads  The obvious limitation of this system is the excessive memory consumption due to the usage of language specific models  For low resource languages this system is difficult to train and scale 
We hope that these problems will be addressed by researchers in future works 
Context 2: Football Club Urartu (, translated Futbolayin Akumb Urartu), commonly known as Urartu, is an Armenian professional football team based in the capital Yerevan that currently plays in the Armenian Premier League  The club won the Armenian Cup three times, in 1992, 2007 and 2016  In 2013–2014, they won the Armenian Premier League for the first time in their history 

In early 2016, the Russia-based Armenian businessman Dzhevan Cheloyants became a co-owner of the club after purchasing the major part of the club shares  The club was known as FC Banants until 1 August 2019, when it was officially renamed FC Urartu 

History

Kotayk
Urartu FC were founded as FC Banants by Sarkis Israelyan on 21 January 1992 in the village of Kotayk, representing the Kotayk Province  He named the club after his native village of Banants (currently known as Bayan)  Between 1992 and 1995, the club was commonly referred to as Banants Kotayk  During the 1992 season, the club won the first Armenian Cup  At the end of the 1995 transitional season, Banants suffered a financial crisis  The club owners decided that it was better to merge the club with FC Kotayk of Abovyan, rather than disband it  In 2001, Banants demerged from FC Kotayk, and was moved from Abovyan to the capital Yerevan 

Yerevan

FC Banants was relocated to Yerevan in 2001  At the beginning of 2003, Banants merged with FC Spartak Yerevan, but was able to limit the name of the new merger to FC Banants  Spartak became Banants's youth academy and later changed the name to Banants-2  Because of the merger, Banants acquired many players from Spartak Yerevan, including Samvel Melkonyan  After the merger, Banants took a more serious approach and have finished highly in the league table ever since  The club managed to lift the Armenian Cup in 2007 
Experience is making way for youth for the 2008 and 2009 seasons
Context 3: Paper Info

Title: Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring
Publish Date: 25 Mar 2023
Author List: Loïc Crombez (from LIMOS, Université Clermont Auvergne), Guilherme Da Fonseca (from LIS, Aix-Marseille Université), Florian Fontan (from Independent Researcher), Yan Gerard (from LIMOS, Université Clermont Auvergne), Aldo Gonzalez-Lorenzo (from LIS, Aix-Marseille Université), Pascal Lafourcade (from LIMOS, Université Clermont Auvergne), Luc Libralesso (from LIMOS, Université Clermont Auvergne), Benjamin Momège (from Independent Researcher), Jack Spalding-Jamieson (from David R  Cheriton School of Computer Science, University of Waterloo), Brandon Zhang (from Independent Researcher), Da Zheng (from Department of Computer Science, University of Illinois at Urbana-Champaign)

Figure

Figure 1: A partition of the input graph of the CG:SHOP2022 instance vispecn2518 into 57 plane graphs It is the smallest instance of the challenge with 2518 segments On top left, you see all 57 colors together On top right, you see a clique of size 57, hence the solution is optimal Each of the 57 colors is then presented in small figures 
Figure 2: Number of colors over time for the instance vispecn13806 using different values p The algorithm uses σ = 0 15, easy vertices, q max = 59022, but does not use the BDFS nor any clique 
Figure 3: Number of colors over time with different values of q max obtained on the instance vispecn13806 Parameters are σ = 0 15, p = 1 2, no clique knowledge, and no BDFS 
Figure 4: Number of colors over time with and without clique knowledge and BDFS obtained on the instance vispecn13806 Parameters are σ = 0 15, p = 1 2, and q max = 1500000 
Figure 5: Number of colors over time for the instance vispecn13806 for different values of σ In both figures the algorithm uses p = 1 2, easy vertices, q max = 59022, but does not use the BDFS nor any clique For σ ≥ 0 25, no solution better than 248 colors is found 
Figure 6: Number of colors over time (in hours) for the instance vispecn13806 
Several CG:SHOP 2022 results We compare the size of the largest known clique to the smallest coloring found by each team on a selection of 14 CG:SHOP 2022 instances 
[20][21][22][23][24][25] with state-of-the-art graph coloring algorithms The conflict optimizer underperforms except on the geometric graphs r* and dsjr* CE39-0007), SEVERITAS (ANR-20-CE39-0005) and by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP[20][21][22][23][24][25] The work of Luc Libralesso is supported by the French ANR PRC grant DECRYPT (ANR-18-CE39-0007) 

abstract

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments  Surprisingly, the top three teams used the same technique, called conflict optimization  This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning problem 
In this paper, we present the technique in the more general framework of binary constraint satisfaction problems (binary CSP)  Then, the top three teams describe their different implementations of the same underlying strategy  We evaluate the performance of those implementations to vertex color not only geometric graphs, but also other types of graphs 

Introduction

The CG:SHOP challenge (Computational Geometry: Solving Hard Optimization Problems) is an annual geometric optimization competition, whose first edition took place in 2019  The 2022 edition proposed a problem called minimum partition into plane subgraphs  The input is a graph G embedded in the plane with edges drawn as straight line segments, and the goal is to partition the set of edges into a small number of plane graphs (Fig  )  
This goal can be formulated as a vertex coloring problem on a graph G defined as follows  The vertices of G are the segments defining the edges of G, and the edges of G correspond to pairs of crossing segments (segments that intersect only at a common endpoint are not considered crossing)  The three top-ranking teams (Lasa, Gitastrophe, and Shadoks) on the CG:SHOP 2022 challenge all used a common approach called conflict optimization while the fourth team used a SAT-Boosted Tabu Search  
Conflict optimization is a technique used by Shadoks to obtain the first place in the CG:SHOP 2021 challenge for low-makespan coordinated motion planning , and the main ideas of the technique lent themselves well to the 2022 challenge  Next, we describe the conflict optimizer as a metaheuristic to solve constraint satisfaction problems (CSP)  
We start by describing a CSP  A CSP is a triple of • variables X = (x 1 ,       , x n ), Each of the 57 colors is then presented in small figures  • domains D = (D 1 ,       , D n ), and • constraints R  Each variable x i must be assigned a value in the corresponding domain D i such that all constraints are satisfied 
In general, the constraints may forbid arbitrary subsets of values  We restrict our attention to a particular type of constraints (binary CSP ), which only involve pairs of assignments  A partial evaluation is an assignment of a subset of the variables, called evaluated, with the remaining variables called non-evaluated 
All constraints involving a non-evaluated variable are satisfied by default  We only consider assignments and partial assignments that satisfy all constraints  The conflict optimizer iteratively modifies a partial evaluation with the goal of emptying the set S of non-evaluated variables, at which point it stops 
At each step, a variable x i is removed from S  If there exists a value x ∈ D i that satisfies all constraints, then we assign the value x to the variable x i   Otherwise, we proceed as follows  For each possible value x ∈ D i , we consider the set K(i, x) of variables (other than x i ) that are part of constraints violated by the assignment x i = x 
We assign to x i the value x that minimizes where w(j) is a weight function to be described later  The variables x j ∈ K(i, x) become non-evaluated and added to S  The weight function should be such that w(j) increases each time x j is added to S, in order to avoid loops that keep moving the same variables back and forth from S  Let q(j) be the number of times x j became non-evaluated 
A possible weight function is w(j) = q(j)  More generally, we can have w(j) = q(j) p for some exponent p (typically between 1 and 2)  Of course, several details of the conflict optimizer are left open  For example, which element to choose from S, whether some random noise should be added to w, and the decision to restart the procedure from scratch after a certain time 
The CSP as is, does not apply to optimization problems  However, we can, impose a maximum value k of the objective function in order to obtain a CSP  The conflict optimizer was introduced in a low makespan coordinated motion planning setting  In that setting, the variables are the robots, the domains are their paths (of length at most k) and the constraints forbid collisions between two paths 
In the graph coloring setting, the domains are the k colors of the vertices and the constraints forbid adjacent vertices from having the same color  The conflict optimizer can be adapted to non-binary CSP, but in that case multiple variables may be unassigned for a single violated constraint  The strategy has some resemblance to the similarly named min-conflicts algorithm , but notable differences are that a partial evaluation is kept instead of an invalid evaluation and the weight function that changes over time 
While the conflict optimization strategy is simple, there are different ways to apply it to the graph coloring problem  The goal of the paper is to present how the top three teams applied it or complemented it with additional strategies  We compare the relative benefits of each variant on the instances given in the CG:SHOP 2022 challenge 
We also compare them to baselines on some instances issued from graph coloring benchmarks  The paper is organized as follows  Section 2 presents the details of the conflict optimization strategy applied to graph coloring  In the three sections that follow, the three teams Lasa, Gitastrophe, and Shadoks present the different parameters and modified strategies that they used to make the algorithm more efficient for the CG:SHOP 2022 challenge 
The last section is devoted to the experimental results 

Literature Review

The study of graph coloring goes back to the 4-color problem (1852) and it has been intensively studied since the 1970s (see for surveys)  Many heuristics have been proposed , as well as exact algorithms   We briefly present two classes of algorithms: greedy algorithms and exact algorithms  Greedy algorithms 
These algorithms are used to find good quality initial solutions in a short amount of time  The classic greedy heuristic considers the vertices in arbitrary order and colors each vertex with the smallest non-conflicting color  The two most famous modern greedy heuristics are DSATUR and Recursive Largest First (RLF )  
At each step (until all vertices are colored), DSATUR selects the vertex v that has the largest number of different colors in its neighbourhood  Ties are broken by selecting a vertex with maximum degree  The vertex v is colored with the smallest non-conflicting color  RLF searches for a large independent set I, assigns the vertices I the same color, removes I from G , and repeats until all vertices are colored 
Exact algorithms  Some exact methods use a branch-and-bound strategy, for example extending the DSATUR heuristic by allowing it to backtrack   Another type of exact method (branch-and-cut-and-price) decomposes the vertex coloring problem into an iterative resolution of two sub-problems   The "master problem" maintains a small set of valid colors using a set-covering formulation 
The "pricing problem" finds a new valid coloring that is promising by solving a maximum weight independent set problem  Exact algorithms are usually able to find the optimal coloring for graphs with a few hundred vertices  However, even the smallest CG:SHOP 2022 competition instances involve at least a few thousands vertices 

Conflict Optimization for Graph Coloring

Henceforth, we will only refer to the intersection conflict graph G induced by the instance  Vertices will refer to the vertices V (G ), and edges will refer to the edges E(G )  Our goal is to partition the vertices using a minimum set of k color classes C = {C 1 ,       , C k }, where no two vertices in the same color class C i are incident to a common edge 

Conflict Optimization

TABUCOL inspired neighbourhood One classical approach for the vertex coloring involves allowing solutions with conflicting vertices (two adjacent vertices with the same color)  It was introduced in 1987 and called TABUCOL  It starts with an initial solution, removes a color (usually the one with the least number of vertices), and assigns uncolored vertices with a new color among the remaining ones 
This is likely to lead to some conflicts (i e  two adjacent vertices sharing a same color)  The local search scheme selects a conflicting vertex, and tries to swap its color, choosing the new coloring that minimises the number of conflicts  If it reaches a state with no conflict, it provides a solution with one color less than the initial solution 
The process is repeated until the stopping criterion is met



ground_truth: 85.61%.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Where is the club's headquarters located?

Read the following text and answer briefly.

Context 2:  The departures of most of the experienced players have left the club's future to the youth  Along with two Ukrainian players, Ugandan international, Noah Kasule, has been signed 

The club headquarters are located on Jivani Street 2 of the Malatia-Sebastia District, Yerevan 

Domestic

European

Stadium

The construction of the Banants Stadium was launched in 2006 in the Malatia-Sebastia District of Yerevan, with the assistance of the FIFA goal programme  It was officially opened in 2008 with a capacity of 3,600 seats  Further developments were implemented later in 2011, when the playing pitch was modernized and the capacity of the stadium was increased up to 4,860 seats (2,760 at the northern stand, 1,500 at the southern stand and 600 at the western stand) 

Training centre/academy
Banants Training Centre is the club's academy base located in the Malatia-Sebastia District of Yerevan  In addition to the main stadium, the centre houses 3 full-size training pitches, mini football pitches as well as an indoor facility  The current technical director of the academy is the former Russian footballer Ilshat Faizulin 

Fans
The most active group of fans is the South West Ultras fan club, mainly composed of residents from several neighbourhoods within the Malatia-Sebastia District of Yerevan, since the club is a de facto representer of the district  Members of the fan club benefit from events organized by the club and many facilities of the Banants training centre, such as the mini football pitch, the club store and other entertainments 

Achievements
 Armenian Premier League
 Winner (1): 2013–14 
 Runner-up (5): 2003, 2006, 2007, 2010, 2018 

 Armenian Cup
 Winner (3): 1992, 2007, 2016 
 Runner-up (6): 2003, 2004, 2008, 2009, 2010, 2021–22

 Armenian Supercup
 Winner (1): 2014 
 Runner-up (5): 2004, 2007, 2009, 2010, 2016 

Current squad

Out on loan

Personnel

Technical staff

Management

Urartu-2

FC Banants' reserve squad play as FC Banants-2 in the Armenian First League  They play their home games at the training field with artificial turf of the Urartu Training Centre 

Managerial history
 Varuzhan Sukiasyan (1992–94)
 Poghos Galstyan (July 1, 1996 – June 30, 1998)
 Oganes Zanazanyan (2001–05)
 Ashot Barseghyan (2005–06)
 Nikolay Kiselyov (2006–07)
 Jan Poštulka (2007)
 Nikolay Kostov (July 1, 2007 – April 8, 2008)
 Nedelcho Matushev (April 8, 2008 – June 30, 2008)
 Kim Splidsboel (2008)
 Armen Gyulbudaghyants (Jan 1, 2009 – Dec 1, 2009)
 Ashot Barseghyan (interim) (2009)
 Stevica Kuzmanovski (Jan 1, 2010 – Dec 31, 2010)
 Rafael Nazaryan (Jan 1, 2011 – Jan 15, 2012)
 Volodymyr Pyatenko (Jan 17, 2013 – June 30, 2013)
 Zsolt Hornyák (July 1, 2013 – May 30, 2015)
 Aram Voskanyan (July 1, 2015 – Oct 11, 2015)
 Tito Ramallo (Oct 12, 2015 – Oct 3, 2016)
 Artur Voskanyan (Oct 3, 2016 – Aug 11, 2018)
 Ilshat Faizulin (Aug 12, 2018 –Nov 24, 2019)
 Aleksandr Grigoryan (Nov 25, 2019 –Mar 10, 2021)
 Robert Arzumanyan (10 March 2021–24 June 2022)
 Dmitri Gunko (27 June 2022–)



ground_truth: The club's headquarters are located on Jivani Street 2 of the Malatia-Sebastia District, Yerevan.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are the three teams that used conflict optimization in the challenge?

Read the following text and answer briefly.

Context 3:  While the original TABUCOL algorithm includes a "tabu-list" mechanism to avoid cycling, it is not always sufficient, and requires some hyper-parameter tuning in order to obtain a good performance on a large variety of instances  To overcome this issue, we use a neighbourhood, but replace the "tabu-list" by the conflict optimizer scheme presented above 
PARTIALCOL inspired neighbourhood PARTIALCOL another local search algorithm solving the vertex coloring problem was introduced in 2008  This algorithm proposes a new local search scheme that allows partial coloring (thus allowing uncolored vertices)  The goal is to minimize the number of uncolored vertices 
Similarly to TABUCOL, PARTIALCOL starts with an initial solution, removes one color (unassigning its vertices), and performs local search iterations until no vertex is left uncolored  When coloring a vertex, the adjacent conflicting vertices are uncolored  Then, the algorithm repeats the process until all vertices are colored, or the stopping criterion is met 
This neighbourhood was also introduced alongside a tabu-search procedure  The tabu-search scheme is also replaced by a conflict-optimization scheme  Note that this neighbourhood was predominantly used by the other teams 

Finding Initial Solutions

Lasa team used two approaches to find initial solutions: 1  DSATUR is the classical graph coloring algorithm presented in Section 1  2  Orientation greedy is almost the only algorithm where the geometry of the segments is used  If segments are almost parallel, it is likely that they do not intersect (thus forming an independent set) 
This greedy algorithm first sorts the segments by orientation, ranging from − π 2 to π 2   For each segment in this order, the algorithm tries to color it using the first available color  If no color has been found, a new color is created for coloring the considered segment  This algorithm is efficient, produces interesting initial solutions and takes into account the specificities of the competition 

Solution Initialization

The gitastrophe team uses the traditional greedy algorithm of Welsh and Powell to obtain initial solutions: order the vertices in decreasing order of degree, and assign each vertex the minimum-label color not used by its neighbors  During the challenge Gitastrophe attempted to use different orderings for the greedy algorithm, such as sorting by the slope of the line segment associated with each vertex (as the orientation greedy initialization presented in Section 3), and also tried numerous other strategies 
Ultimately, after running the solution optimizer for approximately the same amount of time, all initializations resulted in an equal number of colors 

Modifications to the Conflict Optimizer

Taking inspiration from memetic algorithms, which alternate between an intensification and a diversification stage, the algorithm continually switched between a phase using the above conflict score, and one minimizing only the number of conflicts  Thus during the conflict-minimization phase, the random variables f (C j ) and w(u) are both fixed equal to 1 leading to a conflict score
Each phase lasted for 10 5 iterations  Adding the conflict-minimization phase gave minor improvements to some of the challenge instances 

Shadoks

In this section, we describe the choices used by the Shadoks team for the options described in Section 2 1  The Shadoks generally chose to eliminate the color with the smallest number of elements  However, if the multistart option is toggled on, then a random color is used each time  The conflict set S is stored in a queue 
The Shadoks tried other strategies, but found that the queue gives the best results  The weight function used is w(u) = 1 + q(u) p , mostly with p = 1 2  The effect of the parameter p is shown in Fig    Notice that in all figures, the number of colors shown is the average of ten executions of the code using different random seeds 
The algorithm uses σ = 0 15, easy vertices, q max = 59022, but does not use the BDFS nor any clique  If q(u) is larger than a threshold q max , the Shadoks set w(u) = ∞ so that the vertex u never reenters S  If at some point an uncolored vertex v is adjacent to some vertex u of infinite weight in every color class, then the conflict optimizer is restarted 
When restarting, the initial coloring is shuffled by moving some vertices from their initial color class to a new one  Looking at Fig  , the value of q max does not seem to have much influence as long as it is not too small  Throughout the challenge the Shadoks almost exclusively used q max = 2000 • (75000/m) 2 , where m is the number of vertices 
This value roughly ensures a restart every few hours  q max =0 5k q max =5k q max =50k q max =100k q max =250k The Shadoks use the function f as a Gaussian random variable of mean 1 and variance σ  A good default value is σ = 0 15  The effect of the variance is shown in Fig    Notice that setting σ = 0 gives much worse results 
Option (e) The goal of BDFS is to further optimize very good solutions that the conflict optimizer is not able to improve otherwise  Fig  shows the influence of BDFS  While on this figure, the advantages of BDFS cannot be noticed, its use near the end of the challenge improved about 30 solutions  The bounded depth-first search (BDFS) algorithm tries to improve the dequeuing process 
The goal is to prevent a vertex in conflict with some adjacent colored vertices from entering in the conflict set  At the first level, the algorithm searches for a recoloring of some adjacent vertices which allows us to directly recolor the conflict vertex  If no solution is found, the algorithm  In both figures the algorithm uses p = 1 2, easy vertices, q max = 59022, but does not use the BDFS nor any clique 
For σ ≥ 0 25, no solution better than 248 colors is found  could recolor some vertices at larger distances from the conflict vertex  To do so, a local search is performed by trying to recolor vertices at a bounded distance from the conflict vertex in the current partial solution  The BDFS algorithm has two parameters: adjacency bound a max and depth d 
In order to recolor a vertex v, BDFS gets the set C of color classes with at most a max neighbors of v  If a class in C has no neighbor of v, v is assigned to C  Otherwise, for each class C ∈ C, BDFS tries to recolor the vertices in C which are adjacent to v by recursively calling itself with depth d − 1 
At depth d = 0 the algorithm stops trying to color the vertices  During the challenge the Shadoks used BDFS with parameters a max = 3 and d = 3  The depth was increased to 5 (resp  7) when the number of vertices in the queue was 2 (resp  1)  Degeneracy order Given a target number of colors k, we call easy vertices a set of vertices Y such that, if the remainder of the vertices of G are colored using k colors, then we are guaranteed to be able to color all vertices of G with k colors 
This is obtained using the degeneracy order Y   To obtain Y we iteratively remove from the graph a vertex v that has at most k − 1 neighbors, appending v to the end of Y   We repeat until no other vertex can be added to Y   Notice that, once we color the remainder of the graph with at least k colors, we can use a greedy coloring for Y in order from last to first without increasing the number of colors used 
Removing the easy vertices reduces the total number of vertices, making the conflict optimizer more effective  The Shadoks always toggle this option on (the challenge instances contain from 0 to 23% easy vertices) 

Results

We provide the results of the experiments performed with the code from the three teams on two classes of instances  First, we present the results on some selected CG:SHOP 2022 instances  These instances are intersection graphs of line segments  Second, we execute the code on graphs that are not intersection graphs, namely the classic DIMACS graphs , comparing the results of our conflict optimizer implementations to previous solutions 
The source code for the three teams is available at: • Lasa: https://github com/librallu/dogs-color • Gitastrophe: https://github com/jacketsj/cgshop2022-gitastrophe • Shadoks: https://github com/gfonsecabr/shadoks-CGSHOP2022

CG:SHOP 2022 Instances

We selected 14 instances (out of 225) covering the different types of instances given in the CG:SHOP 2022 challenge  The results are presented in Table   For comparison, we executed the HEAD code on some instances using the default parameters  The table shows the smallest number of colors for which HEAD found a solution 
We ran HEAD for 1 hour of repetitions for each target number of colors on a single CPU core (the HEAD solver takes the target number of colors as a parameter and we increased this parameter one by one)  At the end of the challenge, 8 colorings computed by Lasa, 11 colorings computed by Gitastrophe, and 23 colorings computed by Shadoks over 225 instances have been proved optimal (their number of colors is equal to the size of a clique) 
In order to compare the efficiency of the algorithms, we executed the different implementations on the CG:SHOP instance vispecn13806  The edge density of this graph is 19%, the largest clique that we found has 177 vertices and the best coloring found during the challenge uses 218 colors  Notice that vispecn13806 is the same instance used in other Shadoks experiments in Section 5  Notice also that HEAD algorithm provides 283 colors after one hour compared to less than 240 colors for the conflict optimizers 
We ran the three implementations on three different servers and compared the results shown in Figure   For each implementation, the x coordinate is the running time in hours, while the y coordinate is the smallest number of colors found at that time 

Results on DIMACS Graphs

We tested the implementation of each team on the DIMACS instances to gauge the performance of the conflict optimizer on other classes of graphs  We compared our results to the best known bounds and to the state of the art coloring algorithms HEAD and QACOL   The time limit for Lasa's algorithms is 1 hour 
CWLS is Lasa's conflict optimizer with the neighbourhood presented in TABUCOL , while PWLS is the optimizer with the neighbourhood presented in PARTIALCOL   Gitastrophe algorithm ran 10 minutes after which the number of colors no longer decreases  Shadoks algorithm ran for 1 hour without the BDFS option (results with BDFS are worse) 
Results are presented in Table   We only kept the difficult DIMACS instances  For the other instances, all the results match the best known bounds  The DIMACS instances had comparatively few edges (on the order of thousands or millions); the largest intersection graphs considered in the CG:SHOP challenge had over 1 5 billion edges 
We notice that the conflict optimizer works extremely poorly on random graphs, but it is fast and appears to perform well on geometric graphs (r250 5, r1000 1c, r1000 5, dsjr500 1c and dsjr500 5), matching the best-known results   Interestingly, these geometric graphs are not intersection graphs as in the CG:SHOP challenge, but are generated based on a distance threshold 
On the DIMACS graphs, Lasa implementation shows better performance than the other implementations 



ground_truth: Lasa, Gitastrophe, and Shadoks.