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: How many experiments were demonstrated to test the capabilities of the controller?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Force Feedback Control For Dexterous Robotic Hands Using Conditional Postural Synergies
Publish Date: Unkown
Author List: Dimitrios Dimou, José Santos-Victor, Plinio Moreno

Figure

Fig  1 Example of modeling the contacts and friction during manipulation 
Fig  2  Schematic representation of the proposed force controller The input is the state (GRASP or RELEASE) and the force readings Based on that the grasp size is adjusted by a value C and is given to the posture mapping function along with the desired grasp type A finger configuration is then generated and commanded to the robot 
Fig  3  Our control algorithm in Python-like pseudocode 
Fig  4  Our first experiment The robot picks up a bottle, transports it, and places down on the desk In the bottom part of the figure, you can see the control signals during this task 
Fig  5 The household objects used in our experiments 
Under the pictures of the execution you can see the signals recorded by the controller: the average normal force applied by all fingers (blue line), the thresholds f threshold high n  (purple dashed line) and f threshold low n (yellow dashed line), the average tangential force (green), and the grasp size used in each time-step (red) The task is divided four stages: 1) (red part) the initial grasp of the object, in this stage the force controller closes the grasp until the applied normal
Fig 6 In the upper row of images, you can see our second experiment The robot picks up the chips can, rotates it 90 degrees, and places back down In the middle row, for our third experiment, the robot picks up the chips can, rotates it 90 degrees, and hands it over to a person In the bottom row, for our forth experiment, the robot picks up a foam brick, rotates it 180 degrees, and hands it over to a person, using a pinch grasp 

abstract

We present a force feedback controller for a dexterous robotic hand equipped with force sensors on its fingertips  Our controller uses the conditional postural synergies framework to generate the grasp postures, i e  the finger configuration of the robot, at each time step based on forces measured on the robot's fingertips 
Using this framework we are able to control the hand during different grasp types using only one variable, the grasp size, which we define as the distance between the tip of the thumb and the index finger  Instead of controlling the finger limbs independently, our controller generates control signals for all the hand joints in a (lowdimensional) shared space (i e 
synergy space)  In addition, our approach is modular, which allows to execute various types of precision grips, by changing the synergy space according to the type of grasp  We show that our controller is able to lift objects of various weights and materials, adjust the grasp configuration during changes in the object's weight, and perform object placements and object handovers 

INTRODUCTION

To perform complex manipulation tasks in unstructured environments, humans use tactile feedback from their fingers  This feedback is provided by tactile afferents located in the skin of the hand  Particularly, for handling small objects with precise movements, the afferents located in the fingertips are used, which have high density and adapt fast to pressure changes  
These afferents provide information about the characteristics of the exerted contact forces, such as the magnitude and the direction  For anthropomorphic robots to be able to perform dexterous tasks similar force feedback signals must be used to alleviate problems arising from uncertainty in measurements, and handle external perturbations 
For example, using open-loop position control to lift a heavy object may fail due to slip without any feedback mechanism to provide tactile information  Previous works have used tactile sensors to design force controllers that use slip prediction to update the desired normal forces applied by the fingertips 
The slip predictors are based on machine learning models such as neural networks and random forests to classify multi-modal signals from a tactile sensor  In all previous works, each finger was separately controlled by an independent force controller  In addition, they required labeled data to train the slip predictors and because each finger is controlled independently is not obvious how to implement different anthropomorphic grasp types 
In this work we develop a force controller that takes as input the force readings of the fingertips and computes the grasp size which is then used along with a grasp type label to generate a grasp posture with the desired characteristics  To avoid slippage the desired normal contact force is calculated to be proportional to the tangential contact forces 
The applied normal force is then controlled using the size of the grasp as a control variable  Larger grasp sizes mean less force is applied to the object  So the grasp size is calculated from the error between the desired normal force and the actual measured normal force  The grasp size is then given to the posture sampler that generates a grasp posture, i e  the finger joint angles 
The posture sampler is modeled with a conditional Variational Auto-Encoder (cVAE) based on the framework proposed in   With this framework we abstract away the low-level control of the fingers and generate hand postures based on high-level properties such as the type and the size of the grasp  So it works as a mapping function that takes as input a low-dimensional vector and the grasp type and size as conditional variables and maps them to a set of joint angles 
We show that with our controller we can control a dexterous robotic hand to lift objects of different weights using three precision grasps  Our controller is also able to compensate and retain a stable grasp during changes in the objects' weight, for example when filling up a cup or emptying it  In addition we show how with the addition of the hand pose information we can use the controller to calculate if the tangential force is due to gravity or due to a support surface and use this information to perform handovers and place down objects on surfaces 
We perform several real-world experiments with a dexterous robotic hand to showcase the capabilities of our controller and support our design choices  To sum up our main contributions are • We develop a controller for a dexterous robotic hand that uses force feedback and the conditional synergies framework to perform dexterous manipulation tasks 
• We show that with our controller we can easily use different precision grasp types, by changing only the grasp type variable which is given to the grasp posture mapping function  • We demonstrate by incorporating information about the world pose of the hand we can use our controller to perform additional tasks such as placing down and handing over objects 
Roboticists have looked for inspiration in humans for developing methods for complex object manipulation   Neuroscientists have studied for a long time the processes that allow humans to use tactile feedback to perform complex manipulation tasks  Humans tend to adjust the grip force according to the object's weight, its friction and they use a safety margin to account for uncertainties  
To gather information about the tactile states they use multiple afferents that are located in the skin of the fingers   There are different afferents in different parts of the hand depending on their usage, e g  fast adapting afferents in the fingertips for precise manipulation  Based on signals from these afferents, humans encode simple contact events into action phases, such as grasping, lifting or releasing, which they combine in order to perform more complex and long-horizon manipulation tasks  
In robotics tactile sensors have been used for object stabilization and slip prediction in a variety of settings  For example, in , a compliant anthropomorphic prosthetic hand was controlled using force sensing to maintain object stability and avoid slip  In , they develop a control approach that uses integrated force and spatial tactile signals to avoid slip with unknown objects in real world settings 
In , , grasp quality metrics are computed based on the tactile feedback from the robots fingertips  In these works, simple two or three fingered grippers were considered for simple grasping tasks  Force control with anthropomorphic robotic hands has also been explored in more recent works  In , they employ three slip prediction methods to estimate when slip starts and based on the force signals at that moment they calculate the friction coefficient value 
Based on the calculated friction coefficient, they design a force controller that independently controls each finger to achieve a desired normal force  The desired normal contact force is set to be proportional to the tangential contact force and a safety margin based on the evidence found in   In , they train a random forest to classify the contact states into the classes: no contact, contact, slip 
Based on this classification signal, when slip is detected they increase the desired normal contact force to avoid it  In they train a recurrent neural network to estimate slip and the object material from the readings of a Biotac sensor  The force controller is increasing the desired normal contact force when slip is detected 
All these works , , use tactile feedback sensors to predict slip  They collect labeled data, on which they train their models  This approach is based on complex and expensive tactile sensors, and the process of collecting data is cumbersome  In addition, the data do not cover all possible hand poses, which would be impractical 
In contrast, in our work we do not rely on slip prediction, we avoid slip by defining a tangential force gain and a safety margin that work for a large number of objects  Furthermore, instead of independently controlling each finger we use a synergistic framework to generate grasp postures, that is conditioned on two variables: the grasp type and the grasp size 
This way, instead of controlling the values of each joint of each finger, we control only the two conditional variables greatly simplifying the control pipeline  This also, gives us the ability to use different grasp types in our manipulation tasks by changing only the grasp type variable  In also a synergistic framework was used to prevent an object from slipping from a humanoid hand, but they modeled only one synergy for a tripod grasp and they used the forces on the robotic arm as feedback, while we use force feedback from the fingertips 
Our control algorithm could also be applied to different hands as it does not depend on the hands configuration  Finally, in previous approaches only lifting tasks had been considered  In our work we demonstrate that our approach can be used to perform more complex tasks, such as placing objects on surfaces and performing handovers, which was not done in previous works 
Our goal in this work is to design a control algorithm for an anthropomorphic robotic hand to perform dexterous manipulation skills such as lifting and placing down objects  Our control algorithm will use tactile feedback from the force sensors on the fingertips of the hand to decide the forces that need to be applied to the object in each step of the task 
Given the desired forces to be applied, the size of the grasp will be computed  Given the grasp size and a desired grasp type, the posture generator will generate a grasp posture, i e  the hand configuration, such that the force constraints are satisfied  To model the contacts and friction we use Coulombs' law, which states that in order to avoid slip, the normal contact force f n to the contact surface of an object, times the fiction coefficient µ, has to be larger than the tangential force f t :
µf n ≥ f t You can see an example in Figure , where an object is pressed against a wall by an applied normal force f n , and we have the tangential force f t = mg due to gravity  In order for the object to remain stable we need to apply a normal force: where µ is the friction coefficient between the object and the wall 
In the case of a dexterous hand manipulating an object, we want the normal forces applied by all fingers to be greater than the tangential force divided by the friction coefficient of the materials of the object and the fingertip  Since it is hard to accurately compute the friction coefficient between all possible object materials previous works have used multi-modal tactile sensors like the BioTac sensor, which provides information about the pressure, skin deformation, and temperature, to predict slip and based on that signal to increase the applied normal force 
In our work we use the FTS3 sensors which is a low-cost sensor that measures the 3D force applied in each fingertip  In addition, previous works gathered labeled datasets in order to train their slip prediction models which is time-consuming and limits the possible orientations of the hand, because gathering labeled data for all possible orientations is impractical 
To overcome this we experimentally selected the parameters that determine the value of the applied normal force such that we avoid slip for all objects in our dataset, from the lightest to the heaviest  In order to guarantee contact between the fingertip and the object, in the beginning of the grasping phase, we use an offset f of f set n as the minimum normal force applied by each finger 
In they also suggest that humans use an additional safety margin which is proportional to the tangential force, f margin n ∝ f t   So the final desired normal contact force becomes: where G is the gain that includes the friction coefficient and the additional safety margin  To alleviate the effects of noise in the sensors, the running average of the measured normal force f n and tangential force f t is used, as a low pass filter 
So for each force measurement we have the following relation: where α ∈ (0, 1) is a parameter that determines how much new measurements affect the value, and is experimentally selected  Given the measured normal force f n from the fingertip sensors we can compute the error f err n = f des n − f n   We use this error signal to control the grasp size variable g size , that we use as a conditional variable in our posture mapping function 
The grasp size represents the distance between the thumb and the index finger in a grasp posture  So a smaller grasp size will result in a tighter grasp and greater normal force applied to the surface of the object  We use a linear controller for the grasp size variable that is implemented as follows: where K is a parameter that controls the rate of decrease of the grasp size, and is experimentally selected 
So when the error between the desired normal force and the actual normal force is large the grasp size decreases so tighter grasp postures are generated in order to apply more normal force  In practice, in order to avoid oscillations in the grasp size we use the desired normal force as a high threshold that we want the measured normal force to be below:
If the normal force is below that threshold the grasp size does not change even if there are small oscillations in the measured tangential and normal forces  Also, in order to avoid the hand applying too much force that damages the hardware or the object we use a low threshold, that is: where w threshold is the width of the threshold in mN  
If the measured normal force is below the grasp size increases in order to apply less force  So the final grasp size variable for grasping is calculated as follows: where This is similar to the deadband control method , where instead of having a fixed reference point, an operating range is set  If the response is in this range, the controller does not exert any correction 
In our case, the operating range changes according to the force signals from the robot's fingertips  The grasp posture mapping function is based on the conditional postural synergies model presented in   It uses a conditional Variational Auto-Encoder model to generate grasps postures conditioned on additional variables such as the grasp size 
In this work we augment this model to also generate grasp postures conditioned on the grasp type  The model is trained on a set of labeled grasp samples acquired by teleoperating a robotic hand using a data-glove  Using this model we are able to abstract away the low-level control of each joint of each finger and generate grasps based on more general characteristics such as the type and the size of the grasp 
In this way we can control all the fingers jointly by a single value, the grasp size, thus greatly reducing the control parameters  In addition we are able to use the same control algorithm for different precision grasp types, by changing the grasp type conditional variable  Finally, we can modify our controller to release objects instead of grasping them 
Given the pose of the hand in the world coordinate frame, which we can acquire from the robotic arm that is attached to, we can use the forward kinematics of the hand to compute the poses of each fingertip  Then using the force readings of each fingertip we can calculate the global direction of the net tangential force 
If the angle between the direction of the net tangential force and the direction of gravity is less than 90 degrees, i e  the net tangential force's direction is towards the ground, we assume that the tangential force is due to gravity pulling the object, so the force controller tries to grasp it  If the angle is more than 90 degrees, i e  the net tangential force's direction is upward, it means that something is pushing (or pulling) the object upward, in which case we assume that the object is touching on a support surface or someone is pulling the object so the controller increases the grasp size given to the posture mapping function proportionally to the normal force measured thus slowly releasing the object 
Opening the grasp is done by controlling the grasp size variable as follows: That way we can place objects on surfaces but also perform robot to human handovers, where the robot holds the object and the human grasps the object and slightly pushes or pulls it up, signaling to the robot that there is a support surface 
The robot then slowly releases the object by opening its grasp  We showcase these scenarios in the experiments' section  Based on these observations, we present our force controller in Figure   The hand starts in an open pre-grasp position, a latent point is sampled from the prior distribution of the posture mapping function, and given the desired grasp type and the grasp size a grasp posture, i e  the joint angles of the fingers, is sampled 
The initial grasp size is set to the maximum value, and when the force controller comes into effect and depending on the state of the system and the forces on the fingertips grasp size changes by some value C, according to equations 1,2, until the desired normal force is achieved  To choose between grasping or releasing an object we use a finite state machine formulation 
When the hand reaches the desired grasp pose, which we assume is provided, the GRASP state is activated, in which the controller tries to grasp the object  When the controller detects that the tangential force applied to the object is coming from a support surface the state changes to the RELEASE state, in which the controller releases the object by opening the grasp 
You can see the full algorithm in Python-like pseudocode in Figure   To summarize, the advantages of our controller compared with previous approaches are threefold: 1) instead of controlling each joint of each finger of the hand we use only two variables, the grasp size and the grasp type, which allows us to perform multiple grasp types by changing only one variable while the grasp size variable is common among all grasp types, that greatly reduces the complexity of the control process compared to independently controlling a 21 DoF hand to perform different grasp types, 2) we do not rely on slip prediction for controlling the desired normal force, which involves gathering labeled data and works only for the hand poses in the training dataset, and 3) we can use our controller to also release objects instead of only grasping them 

Experimental Set-up 

For our experiments we used the Seed Robotics RH8D Hand , which is a robotic hand with 7 DoFs  The hand is equipped with the FTS-3 force sensors in each fingertip, which are high resolution tactile sensors that provide the 3D force applied in each fingertip  The sensor provides data at a rate of 50Hz  For the experiments the hand was mounted on a Kinova Gen3 7DoF robot 
To train the posture mapping function we used the CyberGlove to teleoperate the hand and collect 468 grasps belonging to three precision grasp  types: tripod, pinch, lateral tripod  The architecture of the cVAE model was the same as in , with the addition of the grasp type as a conditional variable, which was one-hot encoded 
We used 10 household objects shown in Figure   With the heaviest object weighing 380g and the lightest 1g  During the experiments the trajectories of the arm were prerecorded, while the hand was controlled online by our control algorithm 

Parameter tuning 

To select the values of the parameters in our controllers we conducted preliminary experiments where we tested lifting and releasing several objects, with different physical properties  To select the value of the normal offset force f of f set n , we used an empty plastic cup as our test object, and we choose a value such that the fingers do not deform the cup 
The final value of the parameter was set to -50 mN  To select the values of the gain G and the rate of decrease K, of the grasp size, we experimented with the heaviest object in our dataset, which is the mustard bottle and weighs 380g  The gain G was set to 2 0 such that the desired normal force would be enough to hold the object 
The rate of change of the grasp size was set to 100 0, based on the operating frequency of the force sensor and the range of values of the tangential force  For the tangential force averaging process we used a parameter value of α t = 0 7, because we want the controller to be sensitive to fast changes in its value, that can arise for example during lifting an object 
For the normal force averaging process we used a parameter value of α n = 0 5, as we do not want it to be affected by noise that could make the controller overconfident 

Experiments 

To explore the capabilities of our controller, we demonstrate five experiments of increasing complexity: 1) we picked and placed a bottle using a tripod grasp, 2) we picked, rotated and placed a chips can on a box using a tripod grasp, 3) we picked, rotated and handed over the chips can to a person using a tripod grasp, 4) we picked, rotated and handed over a brown foam brick to a person using a pinch grasp, 5) a person handed over a plastic cup to the robot, filled it with coins to increase its weight, and the robot then handed it back to the person using a tripod grasp 
You can see the execution of the first experiment in  In the middle row, for our third experiment, the robot picks up the chips can, rotates it 90 degrees, and hands it over to a person  In the bottom row, for our forth experiment, the robot picks up a foam brick, rotates it 180 degrees, and hands it over to a person, using a pinch grasp 
Fig    In our fifth experiment, a person hands over an empty plastic cup to the robot, throws coins in it to increase its weight while the robot adjusts its grip to stabilize the object, and then hand overs the cup back to the person  force is below the offset f of f set n , 2) (green part) the robot lifts the object, as it tries to lift the tangential force increases, increasing the threshold, so the grasp size decreases to apply more normal force, 3) (orange part) the robot transports the object, you can see, in point A in the Figure, a perturbation in the tangential force when the robot begins to move, the controller responds by decreasing the grasp thus stabilizing the object, and 4) (blue part) the robot enters the releasing phase, where it lowers the arm until it detects that the tangential force is due to a support surface, then it stops lowering the arm and increases the grasp size slowly releasing the object 
In point B in the Figure, you can see that there is noise in the tangential force, due to the arm moving to place the object on the table, that is also reflected in the desired normal force  Because we use the desired normal force as a threshold and not as a reference signal this noise is not manifested in the control of the grasp size 
You can see the execution of the second experiment in the upper part of Figure   This experiment demonstrates the ability of the controller to handle arbitrary hand poses  The experiment is divided in four parts: 1) the robot enters the GRASP phase and the force controller generates grasps to achieve a normal contact force below the f of f set n threshold, 2) the robot lifts the object and adjusts the grasp size to avoid the object falling, 3) the hand rotates to place the chips can on the horizontal position, and 4) the robot enters the RELEASE phase, and the arm lowers until the object touches the box, when the hand detects the supporting surface, it starts to slowly release the object 
You can see the execution of the third experiment in the middle part of Figure   This experiment demonstrates the ability of the controller to perform robot to human handovers  The experiment is divided in four parts: 1) the robot enters the GRASP phase and the force controller generates grasps to achieve a normal contact force below the f of f set n threshold, 2) the robot lifts the object and adjusts the grasp size to avoid the object falling, 3) the hand rotates to place the chips can on the vertical position, and 4) the robot enters the RELEASE phase, the arm stays still, the human grasps the object from the bottom and slightly pushes it up, the hand then detects that there is a supporting surface and starts to slowly release the object 
You can see the execution of the fourth experiment in the bottom part of Figure   This experiment is similar to previous one, but the grasp type that the robot uses is a pinch grasp, that involves only the thumb and the index finger  To perform this we only had to alter the grasp type conditional variable that was given to the posture mapping function 
You can see the execution of the fifth experiment in the bottom part of Figure   In the first part (blue) of the experiment the robot closes its grasp, by reducing the grasp size, until the normal force is below the force offset  In the next three parts (pink, green, red) the person throws coins in the cup to increase its weight 
You can see in the signal plots that each time coins are added the tangential force decreases so the normal force threshold decreases too  The grasp sizes then decreases as well in order to apply more normal force  This experiment demonstrates the ability of the controller to handle perturbations in the weight of the object during grasping 

CONCLUSION

In summary, we presented a controller that uses force feedback integrated with conditional synergies to control a dexterous robotic hand to grasp and release objects  We demonstrated that our controller can lift objects of different weights and materials while avoiding slip, react online when the weight of the object changes, place them down on surfaces, and hand them over to humans 
In addition, the control architecture is modular, so the synergy grasp mapping component can be easily changed in order to control several precision grasp types  However, our experiments also revealed various limitations of our controller  For example our method fails to stabilize the object when rotational slip occurs 
In addition hardware limitations such as, slow update rates and noise in the force measurements can create problems that result in the object falling  In future work we plan to incorporate additional sensing modalities, such as vision to alleviate some of these issues 



ground_truth: 5.

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: When did the Tevatron Collider Run II start and when is it expected to end?

Read the following text and answer briefly.

Context 2: \section{INTRODUCTION}
The Tevatron Collider Run II started in March 2002 and is expected
to continue until the end of this decade  The Tevatron and the 
two detectors, CDF and D\O, have been performing  well in 2004,
each experiment is collecting data at the rate 
of $\approx$10 pb$^{-1}$ per week 
The total  luminosity accumulated by August 2004 is $\approx$500 pb$^{-1}$
per detector 
The rich physics program includes the
production and precision measurement of properties of  standard model (SM)
objects, as well as searches for phenomena beyond standard model 
In this brief review we focus on areas of most interest 
to the lattice community  We present
new results on the top quark mass
and their implication for the mass of the SM Higgs boson, 
on searches for the SM Higgs boson, on evidence for the $X(3872)$ state, 
on searches for pentaquarks, and on $b$ hadron properties 
All Run II results presented here are preliminary  

\section{TOP QUARK MASS}

The experiments CDF and D\O\ published several direct  measurements of
the top quark pole mass, $\ensuremath{M_{\mathrm{top}}}$, 
based on Run I data (1992-1996) 
The ``lepton $+$ jets'' channel yields the most precise determination of
$\ensuremath{M_{\mathrm{top}}}$  Recently, the
D\O\ collaboration published a new measurement~\cite{Mtop1-D0-l+j-new},
based on a powerful analysis technique yielding  greatly improved precision 
The differential probability 
that the measured variables in any event correspond to the signal
is calculated as a function of $\ensuremath{M_{\mathrm{top}}}$  
The maximum in the product of the individual event probabilities 
provides the best estimate of $\ensuremath{M_{\mathrm{top}}}$ 
The critical differences from previous analyses 
in the lepton $+$ jets decay channel lie in 
the assignment of more 
weight to events that are well measured or more likely to correspond to  
$t \bar t$ signal, 
and  the handling of the combinations of final-state objects
(lepton, jets, and imbalance in transverse momentum) 
and their identification with
top-quark decay products in an event  
The new combined value for the top-quark mass from Run I is 
$\ensuremath{M_{\mathrm{top}}}  =  178 0\pm4 3~\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ 

In Run II, both collaborations  have been exploring several different techniques 
for $\ensuremath{M_{\mathrm{top}}}$
measurements  The best single CDF result comes from a dynamic likelihood method
(DLM)  The method is similar to
the technique used in Ref ~\cite{Mtop1-D0-l+j-new} 
The result is $\ensuremath{M_{\mathrm{top}}} = 177 8^{+4 5}_{-5 0} (stat) \pm  6 2 (syst) ~\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ 
The joint likelihood of the selected events is shown in Fig  ~\ref{fig:cdf_tml}  
The Run II goal is a 1\% uncertainty on $\ensuremath{M_{\mathrm{top}}}$  




\begin{figure}[htb]
\vspace*{-5mm}
\includegraphics[height=5 8cm,width=8 1cm]  {data_22ev_likelihood eps}
\vspace*{-1 2cm}
\caption{The joint likelihood of top candidates(CDF) }
\label{fig:cdf_tml}
\end{figure}




\section{SEARCH FOR SM HIGGS BOSON}


The constraints on the SM Higgs ($H$)  boson  mass from
published  measurements, updated to include the new D\O\ top mass
measurement~\cite{Mtop1-D0-l+j-new}, are
$M_H = 117 ^{+67}_{-45}~\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$, $M_H < 251~\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ at 95\% C L 
The  new most likely  value of $M_H$
is above the experimentally excluded range,
and sufficiently low for $H$ to be observed at the Tevatron 


\begin{figure}[htb]
\vspace*{-5mm}
\includegraphics[height=7 5cm,width=7 8cm]  {d0_wbb_fig_3_err eps}
\vspace*{-1 1cm}
\caption{Distribution of the dijet
invariant mass for $W+2 b$-tagged jets  events,
compared to the expectation (D\O)  
}
\label{fig:d0_wbb_2tag}
\end{figure}



D\O\  has conducted a search for $H$ at $M_H < 140~\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ 
in the production channel  
$p \bar{p} \rightarrow WH \rightarrow  e \nu b \bar{b}$  
The experimental signature of  $WH \rightarrow e \nu b \bar{b}$
is a final state with 
one high $p_T$ electron, two  $b$ jets, and
large missing transverse energy  resulting from
the undetected neutrino 
The dominant backgrounds to $WH$ production
are  $W b \bar{b}$, $t \bar{t}$ and single-top production 
The distribution 
of the dijet mass for events with two $b$-tagged jets is shown in
Fig ~\ref{fig:d0_wbb_2tag}  
Also shown is the  expected contribution ($0 06$ events)  
from the $b \bar{b}$ decay of a
SM Higgs boson with $M_H =$ 115 $\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ 
No events are observed in the  dijet mass window of 85--135  $\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ 
D\O\ sets a limit on the cross section
for $\sigma( p\bar{p} \rightarrow WH) \times B(H \rightarrow b \bar{b}) $
of 9 0 pb at the 95\% C L ,  for a 115  $\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$ Higgs boson 
The results for mass points 105, 125, and 135 $\ensuremath{\mathrm{ Ge\kern -0 1em V }\kern -0 2em /c^2 }$
 are 11 0, 9 1 and 12 2 pb,  respectively 



\begin{figure}[htb]
\vspace*{-1 2cm}
\includegraphics[height=0 33\textheight,width=8 0cm]{whww_aps04_bw eps}

\vspace*{-1 2cm}
\caption{95\% limits on the $H$ production (CDF) }
\label{fig:cdf_whww}
\end{figure}


CDF  has done  a similar search, allowing either an  electron or a muon  
in the final state   Both groups have also searched for $H$ produced in
gluon-gluon fusion, with subsequent decay to a pair of $W$ bosons 
The CDF results for both channels  are shown in Fig ~\ref{fig:cdf_whww}  



\section{THE STATE X(3872)}


\begin{figure}[htb]

\includegraphics[height=8 0cm,width=7 5cm]  {X3872cdfPRL1FullM eps}
\vspace*{-1cm}
\caption{The $X(3872)$ signal (CDF) }
\label{fig:cdf_x}
\end{figure}




 The existence of the $X(3872)$ state discovered by 
the Belle Collaboration~\cite{Belle-X}
 has been confirmed 
 in $p \bar{p}$ collisions by  CDF~\cite{cdf-X} (see Fig ~\ref{fig:cdf_x})
and D\O~\cite{d0-X} 
 It is still unclear whether this particle is a $c\bar{c}$ state,
 or a more complex object   When the data are separated according to
production and decay variables, D\O\  finds no significant
differences between the $X(3872)$ and
the $c \bar{c}$ state $\psi(2S)$ 
CDF has analysed the ``lifetime'' distribution of the $X(3872)$ events in order to
quantify what fraction of this state arises from decay of $B$ hadrons, as opposed to
those produced promptly  The authors find that for the selected samples
28 3$\pm$1 0$(stat)\pm$0 7$(syst)$\% of $\psi(2S)$ candidates are from $b$ decays,
whereas 16 1$\pm$4 9$(stat)\pm$2 0$(syst)$\% of $X$ mesons arise from such decays 





\section{SEARCH FOR PENTAQUARKS}



\begin{figure}[htb]

\includegraphics[height=0 27\textheight,width=7 6cm]  {mpks_1stminbias eps}
\vspace*{-1 2cm}

\caption{Invariant mass distribution of an identified proton and a $K^0_s$ candidate  (CDF)
}
\label{fig:pqtheta}
\end{figure}



\begin{figure}[htb]

\vspace*{-0 9cm}
\includegraphics[height=0 25\textheight,width=8 0cm]  {CM_xicst_cc_1 eps}
\vspace*{-1 2cm}
\caption{Invariant mass distribution of the $(\Xi^-,\pi^+)$ system  (CDF) 
}
\label{fig:pqxi}
\end{figure}


\begin{figure}[htb]
\vspace*{-0 9cm}

\includegraphics[height=0 25\textheight,width=7 6cm]  {theta_note_dstp_dedx_pt eps}
\vspace*{-1 2cm}
\caption{Mass of the ($D^{*+}\bar p$) system  The arrow indicates the position of 
the $\Theta_c$ state (CDF) }
\label{fig:pqthetac}
\end{figure}



Following reports of evidence for exotic
baryons containing five quarks (pentaquarks), CDF has analysed 
its data for evidence of the following pentaquarks:
$\Theta^+$ ($uud\bar d \bar s$), doubly strange states 
$\Xi_{3/2}$, charmed states $\Theta_c$, and, most recently, 
a state $(udus\bar b)$, dubbed $R^+_s$, through its weak decay to $(J/\psi, p)$  
With its excellent particle indentification and mass resolution,
CDF has a unique capability to search for  pentaquark states 
The signals of known states: $\phi$, $\Lambda$,
$\Lambda(1520)$, $K^*$, $\Xi$, 
compare favorably with those provided
by the authors of  the pentaquark evidence 
The group finds no evidence for pentaquark states, see Figs 
~\ref{fig:pqtheta},{\ref{fig:pqxi},\ref{fig:pqthetac} 
This can be interpreted as an indication that the pentaquark production 
in $p \bar p$ collisions is heavily suppressed compared to the conventional
hadron production, or as an evidence against the existence of pentaquarks 

\clearpage

\section{RECENT B PHYSICS RESULTS}


\subsection{Spectroscopy}

CDF has measured the mass of $b$ hadrons in exclusive $J/\psi$ channels 
The measurements of the $B_s$ and $\Lambda_b$ (Fig  \ref{fig:masslb})
masses are the current world's best \\

$m(B^+)$ = 5279 10$\pm$0 41$(stat)\pm$0 36$(syst)$,

$m(B^0)$ = 5279 63$\pm$0 53$(stat)\pm$0 33$(syst)$,

$m(B_s)$ = 5366 01$\pm$0 73$(stat)\pm$0 33$(syst)$,

$m(\Lambda_b)$ = 5619 7$\pm$1 2$(stat)\pm$1 2$(syst)$ MeV/$c^2$ \\


\begin{figure}[htb]
\vspace*{-1mm}
\includegraphics[height=0 30\textheight,width=7 5cm]  {lambdav1c eps}
\vspace*{-1cm}

\caption{The mass spectrum of $\Lambda_b$ candidates (CDF) }
\label{fig:masslb}
\end{figure}


D\O\ reports the first observation of the excited $B$ mesons 
$B_1$ and $B^*_2$ as two separate states in fully reconstructed
decays to $B^{(*)}\pi$  The mass of $B_1$ is measured to be
5724$\pm$4$\pm$7 MeV/c$^2$, and the mass difference $\Delta M$ between
$B^*_2$ and $B_1$ is 23 6$\pm$7 7$\pm$3 9 MeV/c$^2$
(Fig   \ref{fig:d0_bexc}) 

D\O\ observes semileptonic $B$ decays to narrow $D^{**}$ states,
the orbitally excited states  of the $D$ meson
seen as resonances in the $D^{*+}\pi^-$ invariant mass spectrum 
The $D^*$ mesons are reconstructed through the decay sequence 
$D^{*+} \rightarrow D^0\pi^+$, $D^0\rightarrow K^-\pi^+$ 
The invariant mass  of oppositely charged $(D^*,\pi)$ pairs
is shown in Fig   \ref{fig:d0_dstst} 
The mass peak between 2 4 and 2 5 GeV/$c^2$ can be interpreted as two merged 
narrow $D^{**}$ states, $D^0_1(2420)$ and $D^0_2(2460)$ 
The combined branching fraction is 
$ {\cal B}(B\rightarrow D^0_1,D^0_2)\cdot {\cal B}(D^0_1,D^0_2\rightarrow D^{*+}\pi^-)=(0 280\pm0 021(stat)\pm0 088(syst)$\%  The systematic error includes the unknown phase between the
two resonances  Work is in progress on extracting the two Breit-Wigner
amplitudes 


\begin{figure}[htb]
\vspace*{-2mm}
\hspace*{-3mm}
\includegraphics[height=0 28\textheight,width=8 3cm]  {B08F02 eps}

\vspace*{-1cm}
\caption{Mass difference $\Delta M = M(B\pi)-M(B)$ for exclusive $B$ decays 
The background-subtracted signal is a sum of 
$B^*_1 \rightarrow B^* \pi$, $B^* \rightarrow B \gamma $ (open area)
and $B^*_2 \rightarrow B^*\pi$ $B^*\rightarrow B \gamma$ (lower peak in the shaded area)
and $B^*_2 \rightarrow B \pi$ (upper peak in the shaded area)  
(D\O) }
\label{fig:d0_bexc}
\end{figure}


\begin{figure}[htb]
\includegraphics[height=0 25\textheight,width=7 5cm]  {B05F03 eps}

\vspace*{-1cm}
\caption{The invariant mass distribution of
$(D^*,\pi)$ pairs, opposite sign (points) and same-sign (solid histogram) }
\label{fig:d0_dstst}
\end{figure}






\subsection{Lifetimes}


CDF and D\O\ have measured  lifetimes of $b$ hadrons through the exclusively
reconstructed decays $B^+ \rightarrow J/\psi K^+$, $B^0 \rightarrow J/\psi K^{*0}$,
$B_s \rightarrow J/\psi \phi$, 
and $\Lambda_b \rightarrow J/\psi \Lambda$
(Fig  \ref{fig:d0_lbctau}) 
The latest results are:  \\



 $\tau(B^+)$=1 65 $\pm$ 0 08 $^{+0 096}_{-0 123}$  ps ~(D\O\ 2003),

 $\tau(B^+)$=1 662 $\pm$ 0 033  $\pm$ 0 008  ps ~(CDF),

 $\tau(B^0_d)$=1 473  $^{+0 052}_{-0 050}$ $\pm$ 0 023    ps ~(D\O) 

 $\tau(B^0_d)$=1 539 $\pm$ 0 051  $\pm$ 0 008  ps ~(CDF),

 $\tau(B^0_s)$=1 444   $^{+0 098}_{-0 090}$ $\pm$ 0 020   ps ~(D\O),

 $\tau(B^0_s)$=1 369 $\pm$ 0 100 $\pm$ $^{+0 008}_{0 010}$  ps ~(CDF),


 $\tau(\Lambda_b)$=1 221 $^{+0 217}_{-0 179}$ $\pm$ 0 043  ps ~(D\O),


 $\tau(\Lambda_b)$=1 25 $\pm$ 0 26 $\pm$ 0 10  ps ~(CDF 2003) \\



The measured lifetimes correspond to the following lifetime ratios:\\

$\tau(B^+)/\tau(B^0_d)$   =  1 080$\pm$0 042     ~(CDF),
 
$\tau(B^0_s)/\tau(B^0_d)$ =  0 890$\pm$0 072    ~(CDF),

$\tau(B^0_s)/\tau(B^0_d)$ = 0 980$ ^{+0 075}_{-0 070}   \pm$0 003    ~(D\O),

$\tau(\Lambda_b)/\tau(B^0_d)$ =  0 874$ ^{+0 169}_{-0 142}   \pm$0 028    ~(D\O) \\



\begin{figure}[htb]
\includegraphics[height=0 3\textheight,width=8 2cm]  {d0_lbctau_B11F02 eps}
\vspace*{-1cm}

\caption{ Fit projection on  $c\tau$ for the $\Lambda_b$ candidates   (D\O)}
\label{fig:d0_lbctau}
\end{figure}


The $B_s$ lifetime measurements listed above are results of
a single-lifetime fit to data, integrated over the decay angles 
Because  of the presence of  final
states  common to ${B_s^0}$\ and its charge conjugate ${\overline{B}_s^0}$,
the two meson states   are expected
to mix in such a way that the two CP  eigenstates may have a relatively
large lifetime difference 
It is possible to
separate the two CP components of ${B_s^0 \rightarrow J/\psi \phi}$\ and thus to measure the
lifetime difference by studying the time evolution of the
polarization states of the vector mesons in the final state 
CDF has carried out a combined analysis of $B_s$ lifetimes 
and polarization amplitudes  The results for the lifetimes of the
low mass (CP even) and high mass (CP odd) eigenstates, and the relative 
width difference are:\\

 $\tau_L = 1 05 ^{+0 16}_{-0 13} \pm 0 02$ ~ps,
 
 $\tau_H = 2 07 ^{+0 58}_{-0 46} \pm 0 03$ ~ps,

 $\Delta \Gamma /\overline \Gamma   = 0 65 ^{+0 25}_{-0 33} \pm 0 01$ \\

Figure \ref{fig:cdf_dg} shows  the scan of the likelihood function 
for $\Delta \Gamma /\overline \Gamma$ 
Pseudoexperiments tossed with $\Delta \Gamma /\overline \Gamma =0$
yield the betting odds for observing the above results at
1/315  For $\Delta \Gamma /\overline \Gamma = 0 12$ (SM prediction,
which has recently been updated to 0 14$\pm$0 05~\cite{dg_un}) the betting odds are
1/84 

\begin{figure}[htb]
\vspace*{-1mm}
\includegraphics[height=0 3\textheight,width=8 2cm]  {cdf_scan-dg-un eps}

\vspace*{-1cm}
\caption{Scan of the likelihood function 
for $\Delta \Gamma /\overline \Gamma$ (CDF) 
}
\label{fig:cdf_dg}
\end{figure}




D\O\ has used a novel technique to  measure the lifetime ratio
of the charged and neutral $B$ mesons, exploiting the large
semileptonic sample  $B$ hadrons were reconstructed in the channels
$B\rightarrow \mu^+ \nu D^*(2010)^-X$, which are dominated by $B^0$ decays, 
and  $B\rightarrow \mu^+ \nu D^0X$, which are dominated by $B^+$ decays 
The lifetime ratio was
obtained from the variation of the ratio of the number of events in these two
processes at different decay lengths 
The result is \\


$\tau(B^+)/\tau(B^0_d)$   =  1 093$\pm$0 021$\pm$0 022       ~(D\O)




\subsection{Towards $B_s$ mixing}

Measurement of the $B_s$ oscillation frequency via ${B_s^0}$ -${\overline{B}_s^0}$ ~mixing
will provide an important constraint on the CKM matrix  The oscillation
frequency is proportional to the mass difference between the mass eigenstates,
$\Delta m_s$, and is related to the CKM matrix through 
$\Delta m_s \propto |V_{tb}V_{ts}|$  When combined with the
$B_d$ mass difference, $\Delta m_d$ it helps in extraction of $|V_{td}|$,
and thereby the CP violating phase  

As a benchmark for future $B_s$ oscillation measurement, both groups
study  $B_d$ mixing, gaining an understanding of the different components
of a $B$ mixing analysis (sample composition, flavor tagging, vertexing,
asymmetry fitting)  For a sample of partially reconstructed decays
$B\rightarrow D^*(2010)^+\mu^-X$, D\O\ obtains 
$\Delta m_d = 0 506 \pm 0 055 (stat) \pm  0 049 (syst))$ ps$^{-1}$ and
$\Delta m_d = 0 488 \pm 0 066 (stat) \pm  0 044 (syst))$ ps$^{-1}$
when employing  opposite side muon tagging and the same side tagging,
respectively 

The CDF result for semileptonic channels is
$\Delta m_d = 0 536 \pm 0 037 (stat) \pm  0 009 (s c ) \pm 0 015 (syst)$ ps$^{-1}$ 
CDF also reports a result on $B$ oscillations using fully reconstructed
decays:
$\Delta m_d = 0 526 \pm 0 056 (stat) \pm  0 005 (syst))$ ps$^{-1}$ 

Reconstructing $B_s$ decays into different final states is another
important
 step in the ${B_s^0}$ -${\overline{B}_s^0}$ ~mixing analysis 
Thanks to the  large muon and tracking coverage,   D\O\ is accumulating
a  high statistics sample of semileptonic $B_s$ decays 
D\O\ reconstructs the $B_s \rightarrow D^+_s \mu^- X$ decays, with
$D^+_s \rightarrow \phi \pi^+ $ and
$D^+_s \rightarrow K^* K^+ $,
at a rate of $\approx$ 40(25) events per pb$^{-1}$,  respectively 
Figure \ref{fig:d0_bsdsphipi} shows the mass distribution of the
$D^+_s \rightarrow \phi \pi$ candidates 


\begin{figure}[htb]
\vspace*{-5mm}
\includegraphics[height=0 3\textheight,width=8 0cm]  {blds-250 eps}
\vspace*{-1 2cm}
\caption{  $D^+_s \rightarrow \phi \pi^+$  signal  (D\O)}
\label{fig:d0_bsdsphipi}
\end{figure}


\begin{figure}[htb]
\vspace*{-10mm}
\hspace*{-4mm}
\includegraphics[height=0 35\textheight,width=7 9cm]  {cdf_Bs-DsPi-PhiPi eps}

\vspace*{-1 0cm}
\caption{ $B_s \rightarrow D_s \pi$, $D_s \rightarrow \phi \pi$  signal   (CDF)}
\label{fig:cdf_bsdsphipi}
\end{figure}


CDF has clean signals for fully hadronic, flavor-specific  $B_s$ decays,
providing the best sensitivity to $B_s$ oscillations at high
$\Delta m_s$  Figure \ref{fig:cdf_bsdsphipi} shows the signal for
the best channel, $B_s \rightarrow D_s \pi$, $D_s \rightarrow \phi \pi$ 

\clearpage


\subsection{Rare decays}

The purely leptonic decays $B_{d,s}^0 \rightarrow \mu^+
\mu^-$ are flavor-changing neutral current (FCNC) processes 
In the standard model, these decays are forbidden at the tree level and
proceed at a very low rate through higher-order diagrams 
The latest SM prediction~\cite{sm_ref3}
is ${\cal B}(B^0_s \rightarrow \mu^+ \mu^-)=(3 42\pm 0 54)\times
10^{-9}$, where the error is dominated by non-perturbative uncertainties  The
leptonic branching fraction of the $B_d^0$ decay is suppressed by CKM matrix elements $|V_{td}/V_{ts}|^2$
leading to a predicted SM branching fraction of $(1 00\pm0 14)\times 10^{-10}$ 
The best published experimental bound (Fig ~\ref{fig:cdf_bsmumu})
 for the branching fraction
of $B^0_s$ $(B^0_d)$ is presently
${\cal B}(B^0_s \, (B^0_d) \rightarrow \mu^+\mu^-)<7 5\times 10^{-7}\, 
(1 9\times 10^{-7})$ at the 95\% C L ~\cite{cdfII} 
The decay amplitude of $B^0_{d,s} \rightarrow \mu^+ \mu^-$ can be
significantly enhanced in some extensions of the SM  

\begin{figure}[htb]
\includegraphics[height=8 3cm,width=7 9cm]  {cdfbsmumu_results_prl eps}

\vspace*{-1cm}
\caption{Invariant mass for the events passing all requirements  (CDF)}
\label{fig:cdf_bsmumu}
\end{figure}


Assuming no contributions 
from the decay $B^0_d\rightarrow \mu^+\mu^-$ in the signal region,
D\O\  finds the conservative upper limit on the branching fraction 
to be ${\cal B}(B^0_s \rightarrow \mu^+ \mu^-) \leq 4 6\times 10^{-7}$ 
at the 95\% C L  (Fig ~\ref{fig:d0_bsmumu}) 






\begin{figure}[htb]
\includegraphics[height=5 0cm,width=8 0cm]  {B06F03 eps}
\vspace*{-1cm}
\caption{Invariant mass for the events  passing all requirements  (D\O)}
\label{fig:d0_bsmumu}
\end{figure}





ground_truth: The Tevatron Collider Run II started in March 2002 and is expected to continue until the end of this decade.

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 3: 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  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.