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 was the club known as before being officially renamed FC Urartu?

Read the following text and answer briefly.

Context 1: 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–)

References

External links
 Official website 
 Banants at Weltfussball de  

 
Urartu
Urartu
Urartu
Urartu
Context 2: Paper Info

Title: CONTOUR COMPLETION USING DEEP STRUCTURAL PRIORS
Publish Date: 9 Feb 2023
Author List: Ali Shiraee, Morteza Rezanejad, Mohammad Khodadad, Dirk Walther, Hamidreza Mahyar

Figure

Figure 1: Just by looking at subfigure (a), we, as humans, can easily perceive a shape like the one in subfigure (b) This is an extraordinary capability of our human brain and in this paper, we tried to see whether convolutional neural networks can show such capabilities 
Figure 2: The trajectory from random noise X N to the incomplete image X I in image space The network will pass a completed version of the image, X C , throughout this trajectory 
Figure 4: This figure shows our iterative process to complete the fragmented contours of an image given as input to our pipeline 
Figure 5: This example shows how different scores change throughout a single run All three scores change in the range of [0, 100] Our goal is to maximize reconstruction_score and minimize the overfit_score, but we should consider that the minimization lower bound is data dependent and is not zero 
Figure 6: Evolutionary process of the deep structure prior The right column shows the incomplete shapes given to the model and the rest of the columns show how the model is overfitting gradually to produce the incomplete shapes In each column, we are showing an intermediate iteration of this process The loss-term setup enables our pipeline to let the completed image appears during this iterative process 
Average MSE and IoU values between the incomplete (Raw) images, the output of DIP and DSP methods, and ground truth for each image are provided in this table 
For this experiment, we ran the model over a subset of complex dataset with 500 incomplete images at various levels of alpha for 250 iterations After the image completion is done, we compared the evaluation metrics between the completed image and the ground truth to examine the performance of the model for different values of alpha 
In this table, we show the effect of the receptive filter size on our algorithm's capability to fill in bigger gap sizes The numbers in this table are showing the percentage of the time that DIP was successful to complete shapes with each gap size and corresponding receptive field size As predicted, the bigger the filter size, the more successful the algorithm is in filling in the gaps 

abstract

Humans can easily perceive illusory contours and complete missing forms in fragmented shapes  This work investigates whether such capability can arise in convolutional neural networks (CNNs) using deep structural priors computed directly from images  In this work, we present a framework that completes disconnected contours and connects fragmented lines and curves 
In our framework, we propose a model that does not even need to know which regions of the contour are eliminated  We introduce an iterative process that completes an incomplete image and we propose novel measures that guide this to find regions it needs to complete  Our model trains on a single image and fills in the contours with no additional training data 
Our work builds a robust framework to achieve contour completion using deep structural priors and extensively investigate how such a model could be implemented 

Introduction

The human visual system is used to seeing incomplete outlines  Our brains can effortlessly group visual elements and fragmented contours that seem to be connected to each other  This power enables us to make shapes, organize disconnected visual features, and even properties of 3D surfaces when projected on 2D planes 
demonstrated how early vision may quickly complete partially-occluded objects using monocular signals  This capability of perceptual grouping has been studied in vision science for decades   Although there has been some work on perceptual grouping in the past couple of years, it has been less studied in the past decade due to the enormous progress of deep neural networks and their success in dealing with the pixel-by-pixel inference of images 
Different types of lines and curves have been studied to maximize the connectivity of two broken ends in the planer contour completion problem   Different types of lines and curves have been studied to maximize the connectivity of two broken ends in the planer contour completion problem  Geometry-based constraints can be utilized to address some challenges of contour completion problems, such as smoothness and curvature consistency  
However, such approaches only work for simple, smooth contours and usually fail in more complex settings  On the other hand, we currently have deep models that could easily take an incomplete image and complete the missing regions using enough training data   The amazing capability of such models especially those that are trained on different modalities with millions or billions of training data raises the question of whether we need such a large amount of training to perceive all the visual cues that are present in an image, which underlies visual perception by humans 
In human vision, Gestalt psychology suggests that our brain is designed to perceive structures and patterns that are grouped by some known rules  In this work, we show that some perceptual structures can also be learned from the image itself directly using architectures that enable such learning  Earlier work has shown  This is an extraordinary capability of our human brain and in this paper, we tried to see whether convolutional neural networks can show such capabilities 
that some forms of perceptual grouping can be achieved using computational models, such as stochastic completion fields   This type of learning resonates with some of the Gestalt perceptual grouping principles including "proximity", "good continuation" and "similarity"  In scenarios where color and/or texture are present, the cue of "similarity" helps us group regions with consistent patterns  
When color and texture are present, they provide a collection of rich information for such cues  In the present article, we probe convolutional neural networks in a scenario where both are absent, and the neural network is dealing with just forms and shapes  Specifically, we explore whether the convolutional neural network architecture itself can give rise to some of these grouping cues when they are fed just contours and shapes alone 
For years, neural networks have been treated as black boxes that can generalize images very well to multiple classes when there are enough training exemplars  One of the reasons that neural networks are trained on many exemplars is to avoid the problem of overfitting  On the other hand, we know that CNNs that generalize well to large classes of exemplars can easily overfit when those class labels are randomly permuted  
Inspired by this observation, suggest that image priors can be learned to a large extent through a generator network architecture that is solely trained on a single image  This encouraged us to take a deeper look at what structural information can be learned from a single-shape image and whether we can reconstruct some of those perceptual grouping capabilities using a generator network 
Inspired by , in this work, we adopt a novel training regime to complete shapes and contours where we use a UNet architecture with random initial weights and try to complete the contours within a single image without any training data  In our model, we imagine that the input image (i e , the only image used to update the model's weights) is an image of fragmented contours 
In this work, instead of training the model on multiple images fetched from a big image dataset, we imagine a random fixed tensor noise image as input to this model  At each iteration, the random noise tensor is inferred through our generative network and the network produces an outcome image  We introduce a novel loss function that enables this network to complete contours 
This process repeats, and the weights of our network are updated gradually based on this loss function, which is an energy term defined based on the input image and the output of the network  The model will reconstruct the missing structures i e , group fragmented contours that perceptually seem to be connected, before it fully overfits to the incomplete input image 
Contributions of our work are summarized as follows: 1  In our pipeline, we propose a novel algorithm that enables us to complete contours that appear to be connected to each other in an illusory form  2  Our model is trained on just one single query image and does not need any training data  3  Our model does not need to know which regions of the image are masked or occluded, i e , we remove the dependency of the algorithm on the guiding mask (a guiding mask is a mask that informs the model on where the missing regions are located at) 
We also introduce two metrics to produce a stopping criterion to know when to stop training before the model fully overfits to the incomplete image, i e , we guide the model to stop when the completed image is produced 

Methods

Our eyes are trained to predict a missing region of an occluded object within a scene  We can easily perceive or make guesses about parts of objects or shapes that we do not necessarily see  Even when we are looking at an image, we might guess about the shape, property, or other attributes of an unknown object within a scene 
Such capability extends beyond just known objects or shapes  We can look at a disconnected set of contours and guess what the connected form may look like  This capability is rooted in our prior knowledge about the world  (see Figure )  In this work, we aim to achieve a similar capability using deep generative networks 
Most neural networks that we work with these days are trained with a massive amount of data and one might think that this is the only way that a neural network can obtain prior information  Authors of Deep Image Prior (DIP) suggest that the convolutional architecture can capture a fair amount of information about image distribution 
They show that the hourglass architectures like UNet can show some good performances in some inverse problems such as image denoising, super-resolution, and inpainting  In this work, we focus on completing fragmented contours end-to-end just by using a single image  To be able to address this problem, we first look at a similar problem in image editing, known as image inpainting 
Image inpainting is the task of completing an image where some regions of that image are covered or filtered by a mask  In image inpainting, the generative model receives a masked image with the mask that guides the algorithm to fill in those missing regions  Although in the problem of contour completion, we have a very similar goal, the additional challenge that we suffer from is that we do not necessarily have a mask that covers the regions of interest for us 
For example, when we look at Figure (left), we are not provided that which regions of the image are incomplete by a guiding mask  Our brain figures this out just by looking at the form and predicting those missing regions  Inspired by the image inpainting work of DIP , we propose a novel algorithm for the contour completion problem (see Figure ), where, unlike DIP, we do not have a guiding mask to know where to fill in the missing regions of our disconnected contours 
Let us assume that we are given a degraded image x I containing a fragmented contour  We propose an iterative process (see Figure ) that can connect those discontinuities and glue those fragmented pieces together as follows  We propose an hour-glass model structure (f ) that is initially set up with completely random parameters (θ 0 ) at first 
Through an iterative process, we start feeding our network with a fixed random tensor noise z signal and obtain the inferred output (f (z)) from that network  We then back-propagate the difference between the inferred output and the incomplete image to the network  We then repeat this process until the difference between the generated outcome of the network (f θ (z)) and the incomplete image (x I ) gets smaller and smaller and finally overfits the incomplete image (x I ) 
In this work, we propose a novel error metric to backpropagate in the model and update its weights  we set the metric in a way that enables us to complete the incomplete image before it overfits the incomplete image  This is where the magic of our algorithm happens  We also propose a stopping criterion, so that when the image is complete, we no longer overfit the outcome of the model and instead produce a plausible connected set of fragmented contour pieces 
As illustrated in Figure , this trajectory will pass through a complete version of the image in image space, which is close to the actual connected ground truth x gt , which we do not have access to directly 

Energy Function

We can model this iterative process mathematically by maximizing a posterior distribution  Let us assume that the optimal image x * that we want to achieve is on a path that connects a random tensor noise z to the incomplete image x I   With this assumption, we can eventually overfit any random tensor noise to the incomplete image x I , and we can formulate the posterior distribution of our wanted optimal image x * as follows:
No Prior To better recapitulate what we want to achieve using our generative model, we solve an energy minimization problem on the parameter space of the model, rather than explicitly working with probability distributions and optimizing on x (image space)  Thus, we solve an energy minimization problem that incorporates the incomplete image (x I ) and model parameters (f θ (z)):
As shown in Figure , the pipeline starts from a random initialized set of parameters θ and updates those weights until it reaches a local minimum θ *   The only information provided for the network is the incomplete image x I   When we reach the optimal θ * , the completed image is obtained as x * = f θ * (z) where z is random tensor noise 
In this work, we use a U-Net architecture with skip connections as the generator model  " As we mentioned previously, in this work we were inspired by an earlier work known as Deep Image Prior (DIP)   In this work, the authors suggested a mean-squared-error loss term that enables the network to compare the output of the generator to the incomplete input image:
where x I is the incomplete image with missing pixels in correspondence of a binary mask m ∈ {0, 1} H×W and operator is for point-wise multiplication of two image matrices  In the inpainting tasks, the existence of a mask is essential as the algorithm needs to know where to fill in the missing area, whereas, in our work, we wanted to know whether the network can perform completion on its own without the need for the mask 
In other words, is it possible for the network to predict where to fill in at the same time that it is trying to reconstruct the incomplete image through the iterative process? To answer this question, we tried to solve a much harder problem in which the mask is not provided to the model and the model is agnostic to it 
To better understand how a solution could be hypothesized for this problem, we first imagine that we want to consider all the available regions in our image that could be potential places to fill in, i e , we set the mask in the previous formula 1 to be equal to the incomplete image x I   This is problematic as the model quickly tries to fill in all white space and quickly reconstructs the incomplete image by doing so 
On the other hand, we can take the inverse problem of the current problem, where the model tries to just fill in the regions that fragmented contour lives in  Taking these two at the same time, we came up with a novel loss term for energy minimization term that helps us remove the need for the mask in the case of the contour completion problem:
In this term, we introduce a linear combination of the two loss terms, where one focuses on reconstructing the missing regions in the foreground, and one focuses on avoiding inpainting regions in the background  The logic behind this is that, if we assume the original image to be representative of the mask, then the model tries to reconstruct in all white regions (the foreground), and in the inverse problem we just want to reconstruct the regions that are already part of the ground truth 

Stopping Criteria

As shown in Figure , knowing when to stop iterating to the over-fitted model is a key to obtaining a completed shape  Therefore, we equipped our model with a criterion that uses two individual novel terms to know when to stop and output the result of the network  These two metrics expand the capability of the generator network beyond what it does currently and achieve a full end-to-end contour completion model that trains and infers on a single image of divided contour fragments 
These new terms are: reconstruction_score (ρ) and overfit_score (ω) 

Reconstruction Score

The first score that this paper suggests is the reconstruction score, i  e , we have to make sure that the model is trained enough that it can reconstruct at least the entire set of fragmented contours within the image  This is a trivial score and to compute the reconstruction_score (ρ), we apply a k-dimensional tree (KDTree) nearest-neighbor lookup to find the ratio of points in the original incomplete image (x 0 ) 
This score ranges from [0 − 100] 

Overfit Score

It is evident that the model overfits the fragmented contours  This is due to the fact that the error in our loss term is minimized as the x overfits to x I , i  e , replacing x with x I in the loss term would give us zero  As we hypothesize iterative process also produces the complete image before it overfits to the incomplete image, we can imagine that at some point the image is complete (x C ) and does not need to be fine-tuned any more to overfit to x I  
We suggest a new score called overfit_score  overfit_score determines how much of the reconstructed outcome is over the number of pixels that are already in the incomplete image (x I )  To compute the overfit_score (ω), we apply a k-dimensional tree (KDTree) nearest-neighbor lookup of points in the outcome of the input image and see what portions of those points are novel and not already in the incomplete image (x I ) 
Similar to reconstruction_score, the overfit_score also ranges from [0 − 100]  Our goal is to maximize reconstruction_score and minimize the overfit_score, but we should consider that the minimization lower bound is data dependent and is not zero 

Combined Score

To be able to find the best possible set of completed contours, we combine the two and have a loop that tries to achieve close to full reconstruction and avoids over-fitting at the same time  This is what we call an "ideal" stopping point in the contour completion problem  In each run throughout all iterations, we pick an output of the network that minimizes a dissimilarity term:
where δ represents our dissimilarity score  The reconstruction_score and overfit_score are obtainable given network output and the incomplete image  Ideally, we want to achieve an output image that has a reconstruction_score equal to 100 and an overfit_score of γ which is a hyperparameter that is dependent on the image and complexity of the shape 
Empirically, we observed that this value is highly correlated with the distance between gaps that are observed in fragmented contours, i  e , the larger the gap results in a larger γ value  We will discuss this in more detail in the next section (see Section 3)  For one sample image, we computed the two metrics reconstruction_score and overfit_score and the combined value of dissimilarity (δ) and showed how these values change (see Figure ) 
Our initial observations show that the reconstruction_score will increase to 100 quickly for the incomplete image indicating that the already existing fragments of the contours have been reconstructed in the output  However, as mentioned previously, we cannot solely rely on this score since we also want to minimize the overfitting 
Remember that our goal is to produce an output that: a) preserves the original contours in the incomplete image and b) fills in the gaps between the fragmented contours  It is evident that overfit_score decreases throughout an iterative run of our process until it reaches zero  The dissimilarity will also decrease along with the overfit to a point, then it will increase, as the model tries to reproduce the incomplete image 
This is where an ideal γ value can be picked, i e , where to stop when the reconstruction is good but we have not done a full overfit to the incomplete image  Thus, one should pick the value of γ empirically in the scenario that the ground truth is not available, whereas, assuming that the ground truth is available, we can easily compute the best γ value 
In our experiments, we tried two datasets of images with different gap sizes  We observed that the best the γ for one set of samples is ∼ 5 (the set with shorter gaps) while it is ∼ 23 for samples from the other set, i  e, the set with longer gaps (see Figure for some completed examples) 

Experiments and Results

Performing unsupervised contour completion is a difficult task to benchmark as one can never know what fragments exactly are connected to each other in a real-world scenario  This makes the problem of contour completion a hard problem to solve  In this paper, we tried to create artificial shapes that are occluded by some masks and then tried to see if our model can regenerate the missing pieces and glue those divided contours together 
To demonstrate our model's behavior, we will conduct experiments on datasets created for this task and will report on them in this section  To compare network results in different settings, we will use pixel-wise Mean Squared Error (MSE) and Intersection over Union (IoU) between the produced result of the network and unmasked ground truth data and the reconstructed image on black pixels (where contours live) 

Data

We prepared two datasets, one labeled "Simple" and one "Complex", in accordance with the number of gaps in each shape  Both datasets contain nine different categories of shapes  In order to generate the Complex dataset, we used FlatShapeNet which is a dataset for the educational game Ariga  The dataset includes the following categories: Circle, Kite, Parallelogram, Rectangle, Rhombus, Square, Trapezoid, Triangle and Overlap 
The "overlap" category contains images that are made as a mixture of two shapes that are overlapping from the previous categories  These are some standard shapes with a few gaps in simple dataset, while the complex dataset has some hand-drawn shapes with fragmented lines and more gaps that produce more variety in general 
For each instance, a ground truth image is available for comparison  Most of our experiments have been conducted using the complex dataset in order to evaluate the generalization of our approach  For the analysis of how γ values should be set for each shape, we used the simple dataset as a reference 

Evaluation

In this section, we compare our model to the original Deep Image Prior (DIP) inpainting model  DIP's inpainting module accepts a degraded image and a binary mask corresponding to that image  In order to make a fair comparison, instead of providing a binary mask, we used the incomplete images both as input and as a mask in order to see whether it can produce a result similar to ours 
For DIP, we run the iterative process for a maximum number of 2500 iterations with the U-net backbone  We used the exact same architecture and setting in our model for a fair comparison  Using our ground truth dataset images, we calculate the MSE loss between the network output and ground truth during each iteration instead of relying on our stopping mechanism described in the previous section 
We then store the output with minimal loss throughout all the iterations  Finally, we select the best output among all iterations, report the MSE and IoU with the ground truth, and save the iteration number which resulted in the lowest MSE  Table compares the results that are obtained using the DIP method, the DSP method (ours), and the difference between raw images and the ground truth 
We have presented the average MSE-loss, average IoU, and the average number of iterations for the best output for different methods  As can be seen from the table, our model improves both MSE and IoU between the incomplete image and ground truth in fewer iterations  The DIP method can neither generate a better result than the raw image nor provide stopping criteria to prevent overfitting 
We provide a more detailed analysis of this result in Figure   As results show, our algorithm not only provides a much faster convergence but also consistently provides a better-completed image (consistently less MSE loss and better IoU), whereas it is challenging for the DIP method to accomplish better results without a guiding mask 
We compare MSE loss between the degraded raw images that these algorithms started with (shown in blue) (a) Mean Squared Error Loss: we clearly see that for almost all images, DSP (green) achieves a lower MSE than the incomplete images (blue) whereas, the DIP completed images either do not improve the MSE or even worsen that for the incomplete images 
Note that, the MSE is computed to an available ground truth image hidden from our methods (the lower is better)  (b) Intersection Over Union: here, we are looking at the IoU metric that specifies the amount of intersection over the union between the obtained images and the ground truth data  Again, we see that DSP produces images that are much closer to the ground truth (in most cases) whereas the DIP can not achieve a similar result 
While we see few DIP completed images produce a better IoU than the degraded images (in terms of IoU), most of them are worse than the starting image (the higher is better)  (c) The number of iterations that are needed for each algorithm to obtain its best results  Here, we see that DSP can quickly produce the best outcome with the least MSE loss whereas the DIP algorithm's best results are when we run the iterative process for more iterations (the lower is better) 

Correlation of γ with the Gap Size

To better understand the γ parameter of our combined score, we conducted the following experiment  A total of 12000 samples were obtained by merging all of our images from the two datasets, simple and complex  As we have access to the ground truth for each degraded image in the combined dataset, we can easily calculate reconstruction_score and overfit_score for each degraded-ground truth pair 
As expected, we obtain a reconstruction_score of 100 for all samples, but the overfit_score varies among them  Intuitively, we hypothesized that an optimal value of overfit score should be intertwined with the total area of gaps  To test this hypothesis, we did the following experiment  We first define a function φ(x) which takes a binary, black and white image x and returns the number of black pixels in it 
Then we define a gap term as follows: where x I is the incomplete image and x gt is the ground truth  In this case, gap indicates the total area of the gap with respect to the whole shape  We found out that this term and the best result have a correlation of 97 43%  This indicates that the value of γ is highly correlated with the gap size, that is something expected in a way 

Effect of α

We conducted additional experiments concerning how α affects the quality of reconstruction  In the previous section, we defined Equation 2 as the loss term that guides our iterative process  The term α specifies the amount of emphasis the model should place on reconstructing missing regions, rather than filling in fragmented contours 
A lower α indicates a better grouping quality, as shown in Equation   However, we will not achieve completion if we remove the first term completely from the loss by setting α = 0  Therefore, the first term should be kept, but its weight should be very low in order to achieve a good completion  On the other hand, if we set α = 1 and omit the second term, we lose the contour completion regularization term and obtain the same output as a vanilla deep image prior, which does not complete shapes 

Effect of Receptive Field Size

To better understand the effect of receptive field size on our algorithm, we test the following hypothesis: can models with bigger receptive field size complete shapes with bigger gaps? In Table , we report showing the results of this experiment  As we can see, the bigger the receptive field size, the more complete shapes we can reconstruct using DSP 

Implementation Details

As shown in Figure , we use a model with 5 layers and 128 channels for downsampling and upsampling convolutions, and 64 channels for skip convolutions  The upsampling and downsampling modules use 3 × 3 filters, while the skip module uses 1 × 1 filters  In the upsample part of the network, the nearest neighbor algorithm is used 
We used 256 × 256 images with three channels in all of our experiments  In training, we use the MSE loss between the degraded image and the output of the network, and we optimize the loss using the ADAM optimizer and a learning rate equal to 0 01   In our experiments, we also used α = 0 15 as an optimal proportion coefficient for reconstruction loss 

Conclusion

In this work, we introduced a novel framework for contour completion using deep structure priors (DSP)  This work offers a novel notion of a maskless grouping of fragmented contours  In our proposed framework, we introduced a novel loss metric that does not require a strict definition of the mask  Instead, it lets the model learn the perceivable illusory contours and connects those fragmented pieces using a generator network that is solely trained on just the single incomplete input image 
Our model does not require any pre-training which demonstrates that the convolutional architecture of the hour-glass model is able to connect disconnected contours  We present an extended set of experiments that show the capability of our algorithm  We investigate the effect of each parameter introduced in our algorithm separately and show how one could possibly achieve the best result for their problem using this model 
In future work, we plan to extend this model and try to see how it performs with real images  In particular, we want to determine whether we can inpaint real-world photographs while retaining perceptually aware scene structures  The importance of shape in perception by deep neural networks has been highlighted in many adversarial examples to appearance-based networks  
The outcome of this work has strong potential to impact the designing and implementation of models that are robust to such perturbations 
Context 3: Rokebye, Ralph of Yorks, arm  Gloucester Hall, matric  9 Nov , 1582, aged 15; student of Lincoln's Inn 1585  See Foster's Inns of Court Register 
Rokebye, Ralph of Herts (? Yorks), gent  Broadgates Hall, matric  entry 28 Feb , 1589-90, aged 14  See pedigree in Foster's Yorkshire Collection 
Rokeby, William (brother of Sir Richard, treasurer of Ireland, son of John, of Thundercliffe Grange, Yorks), fellow of King's Hall, Cambridge, D Can L ; rector of Sandal 1487, and of Halifax, Yorks, 1502, rector of Fakenham, Norfolk, 1496, chancellor of Ireland 1498, and 1515, bishop of Meath, and privy councillor 1507, archbishop of Dublin 1512, archdeacon of Surrey 1520, until his death 29 Nov , 1521  See Ath  ii  717; Cotton, i  25; & Lansdowne MS  979, ff  4, 6 
Rolfe, Augustine (Rolfus) M A  from Queen's Coll , Cambridge, 1595; incorporated 10 July, 1599 
Rolf, Richard B A  from Emanuel Coll , Cambridge, 1584-5 (incorporated 11 July, 1585); M A  1588  See Foster's Graduati Cantab 
Rolfe, William cler  fil  New Coll , matric  10 March, 1656-7, B A  1660, fellow, M A  14 Jan , 1663-4; rector of Brampton 1668, and of Stoke Bruern, Northants, 1676, until his death, buried (at Stoke Bruern) 6 Sept , 1693  See Baker's Northants, i  86 
Rolfe, William s  William, of Stoke-Bruern, Northants, cler  Brasenose Coll  7 July, 1688, aged 16; student of Inner Temple 1692, buried in the Temple church 1 March, 1692-3  See Foster's Inns of Court Reg 
Rolle, Denis youngest son John, of Steventon, Devon, equitis  Exeter Coll , matric  15 Feb , 1666-7, aged 17; brother of John same date 
Rolle, Denis s  D , of Heanton, Devon, arm  Exeter Coll  matric  24 Oct , 1687, aged 17; B A  1691, M A  1694 (as Denys), rector of Merton, Devon, 1696  See Samuel 1687, & Foster's Index Ecclesiasticus 
Rolle, (Sir) Henry of Devon, arm  fil  Broadgates Hall, matric  14 June, 1594, aged 18; student of Middle Temple 1597 (as son and heir of Henry, of Steventon, Devon, esq ), knighted 23 July, 1603, died in 1617  See Foster's Inns of Court Reg 
Rolle, Henry of Devon, arm  Exeter Coll , matric  20 March, 1606-7, aged 17; bar -at-law, Inner Temple, 1618, bencher 1633 (2s  Robert, of Heanton, Devon), M P  Callington 1621-2, 1624-5, Truro 1625-1626, 1628-9, serjeant-at-law 1640, recorder of Dorchester 1636, a judge of king's bench 1645, chief justice of upper bench 1648-55, died 30 July, 1656, buried in Shapwick church, Somerset; brother of Samuel 1605  See Ath  iii  416; & Foster's Judges and Barristers 
Rolle, Henry s  Alex , of Tavistock, Devon, gent  Christ Church, matric  23 March, 1696-7, aged 17 
Rolle, John of Devon, arm  Exeter Coll , matric  30 May, 1589, aged 15; B A  8 Feb , 1592-3, M A  25 May, 1596 
Rolle, John 1s  John, of Steventon, Devon, equitis  Exeter Coll , matric  15 Feb , 1666-7, aged 18; of Bicton, Devon; died in his father's lifetime, buried at Bicton 22 April, 1689; brother of Denis 1667, and father of Denis 1698 
Rolle, Richard s  Richard, of Cookeburye, Devon, gent  New Inn Hall, matric  26 Sept , 1634, aged 18; B A  from Jesus Coll , Cambridge, 1638, incorporated from Gloucester Hall 17 Dec , 1639, M A  2 July, 1642, rector of Sheviocke, Cornwall, 1656; father of the next-named  See Foster's Index Eccl 
Rolle, Richard s  R , of Sheviock, Cornwall, cler  St  Alban Hall, matric  3 July, 1674, aged 17; B A  1678 
Rolle, Robert (Rooles or Roales) fellow New Coll  1551-60 from Mark Lane, city of London, B A  26 June, 1555, M A  26 July, 1560, B D  22 Jan , 1572-3, D D  June, 1585, a teacher in Westminster school; perhaps canon of Combe (4) in Wells, 1574, and rector of Stoke Climsland, Devon, 1574  See O H S  i  345; & Foster's Index Eccl 
Rolle, Samuel s  Denis, of Great Torrington, Devon, arm  Exeter Coll , matric  16 July, 1687, aged 18, B A  1691; bar -at-law Middle Temple 1697; M P  Barnstaple 1705, died 1747; see Denis 1687  See Foster's Judges and Barristers 
Rolle, William B C L  14 July, 1528; perhaps vicar of Yarncombe, Devon, 1536  See Foster's Index Ecclesiasticus 
Rolles, Gabriel (Rooles) B A  from St  John's Coll , Cambridge, 1610-11, M A  1614; incorporated 13 July, 1619, rector of East Locking, Berks, 1620, as Rolle  See Foster's Graduati Cantab 
Rolles, Richard gent  Jesus Coll , matric  1 March, 1632-3, B A  next day, M A  15 Oct , 1635; perhaps created B D  20 Dec  1642, "ex regis gratia," rector of Wavendon, Bucks, and of Witham, Essex, 1646, by the Westminster assembly  See Add  MS  15,670, p  70 
Rolles, William s  Richard, of Lewknor, Oxon, gent  St  John's Coll , matrie  12 March, 1637-8, aged 17, B A  9 Nov , 1641, M A  6 July, 1644; B D  from Jesus Coll  12 Sept , 1661, rector of Wheatfield, Oxon, 1660, and of Chalfont St  Giles, Bucks, 1662  See Foster's Index Eccl 
Rolles, William created M A  from Exeter Coll  14 April, 1648 
Rolleston, Simon created M A  31 Aug , 1636 
Rolleston, Thomas of Devon, gent  Wadham Coll , matric  12 May, 1620, aged 16 
Rollinson, Francis 1584  See Rallinson 
Rollinson, William s  "Jose," of London, gent  St  John's Coll , matric  7 March, 1694-5, aged 15; perhaps brother of John Rawlinson, of New Coll  1692  See page 1236 
Rolt, Edward youngest son of Tho , of London, equitis  Merton Coll , matric  7 Nov , 1701, aged 15; of Sacomb, Herts, and Chippenham, Wilts, student of Lincoln's Inn, 1702, M P  St  Mawes 1713, Grantham 1715-22, Chippenham 1722; died 22 Dec , 1722; his father knighted 1 Oct , 1682, and died 9 Sept , 1710  See Foster's Parliamentary Dictionary 
Rolte, George s  Thomas, of St  Margarets par  Darenth, Kent, pleb  St  Alban Hall, matric  17 June, 1631, aged 18; B A  20 June, 1631, M A  29 April, 1634, incorporated at Cambridge 1639 
Romane, Edmund pleb  Balliol Coll , matric  20 Feb , 1627-8, aged 18; B A  next day, M A  3 June, 1630 
Romaine, Matthew pleb  Balliol Coll , matric  10 June, 1630, B A  same day, M A  14 May, 1633, vicar of Stoke Gaylard, Dorset, 1639; father of the next  See Foster's Index Eccl 
Romayne, Thomas s  Matth , of Stoke Gaylard, Dorset, minister  Wadham Coll , matric  17 July, 1669, aged 17; B A  from Hart Hall 1673, "the intruded" rector of Stoke Gaylard 1675  See Foster's Index Eccl 
Romayne, William (Ronayne) gent  Trinity Coll , matric  31 July, 1671, aged 16 
Rome, Harcourt s  William, of London, p p  Brasenose Coll , matric  13 Dec , 1672, aged 17 
Rome, William s  G  (? "Gul "), of Northampton (city), pleb  Brasenose Coll , matric  11 Dec



ground_truth: FC Banants.

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 does the receptive field size affect the completion of shapes?

Read the following text and answer briefly.

Context 3: , 1684, aged 16 
Romney, Joseph B A  from Emanuel Coll , Cambridge, 1610-11, M A  1614; incorporated 8 July, 1614, student of Inner Temple 1610, as of London, gent  See Foster's Inns of Court Reg 
Rone, John s  Randolph, of Hanmer, Flints, pleb  Brasenose Coll , matric  10 Oct , 1634, aged 18; D D  Trinity Coll , Dublin, 25 Jan , 1666 (as Roane), vicar of Hanmer, Flints, 1644, ejected same year, dean of Clogher 1667, bishop of Killaloe 1675, until his death 5 Sept , 1692  See Cotton's Fasti Ecc  Hib  i  467 
Rone, William of New Coll  1661  See Roane 
Roode, Edward (or Rode) B A  21 July, 1522, M A  26 Nov , 1534; perhaps canon of Southwell 1561-73 
Roode, Edward cler  fil  Merton Coll , matric  22 Nov , 1650; Eton postmaster 1649, fellow 1651, B A  2 March, 1651-2, M A  14 Dec , 1655; incorporated at Cambridge 1657, and LL D  1671; vicar of Gamlingay, co  Cambridge, rector of one moiety 1661, and of the other 1677; died at Cambridge 1689  See Burrows, 525; & O H S  iv  292 
Roode, Onesiphorus s  Edward, of Thame, Oxon, sacerd  New Inn Hall, matric  27 Oct , 1637, aged 16, B A  1 July, 1641; incorporated at Cambridge 1645; chaplain to the house of lords after the expulsion of the bishops; minister of New chapel, Tuttle-Fields, Westminster, 1648, until ejected in 1660  See Calamy, i  195 
Rood, Richard M A  from Pembroke Coll  5 Dec , 1634 
Rooke, John s  Tho , of Broadwell, co  Gloucester, pleb  Pembroke Coll , matric  1 March, 1683-4, aged 17; brother of Thomas 1693 
Rooke, John s  Tho , of Whitchurch, Wilts, gent  Balliol Coll , matric  14 Jan , 1713-14, aged 17 
Rooke, Nicholas s  Arthur, of Totnes, Devon, gent  Exeter Coll , matric  10 March, 1670-1, aged 16; B A  1674, M A  1677, rector of Dartington, Devon, 1679  See Foster's Index Eccl 
Rooke, Robert "ser " Oriel Coll , matric  1 April, 1656, B A  1659 
Rooke, Robert s  R , p p  St  Alban Hall, matric  30 March, 1677, aged 17 
Rooke, Thomas pleb  Christ Church, matric  3 May, 1659 
Rooke, William (Roock) of Dorset, pleb  Brasenose Coll , matric  entry under date 20 March, 1578-9, aged 19; B A  from St  Alban Hall 30 Jan , 1582-3, M A  9 May, 1586 
Rooke, William of Dorset, gent  New Coll , matric  12 July, 1605, aged 18; B A  21 Feb , 1608-9, chaplain, M A  16 Dec , 1611, rector of North Cheriton, Somerset, 1618  See Foster's Index Eccl 
Rooke, William s  J , of Workington, Cumberland, p p  Queen's Coll , matric  22 Oct , 1669, aged 17; B A  1674, M A  1677, B D  1690, vicar of Plumstead, Kent, 1691, and rector of Hadley, Hants, 1695  See Foster's Index Eccl 
Rookes, Christopher (Rokys or Rokkis) B A  8 July, 1522, M A  1 July, 1527, B D  supd  Oct , 1540; principal of Magdalen Hall 1529-32, vicar of Stanstead Abbots, Herts, 1534  See Foster's Index Eccl 
Rookes, Jonas B A  from Magdalen Hall 24 April, 1599, M A  11 Feb , 1601-2 (2s  William, of Roydes Hall); vicar of Penistone, Yorks, 1619, see Foster's Index Eccl ; styled fellow and bursar of University Coll  in Foster's Yorkshire Collection, possibly brother of the next-named 
Rookes, Robert of Yorks, pleb  Magdalen Hall, matric  14 May, 1602, aged 19; possibly brother of the last-named 
Ro(o)kes, William demy Magdalen Coll  1544, B A  supd  1551, fellow 1552-71, M A  27 April, 1556, B Med  supd  24 April, 1561  See Bloxam, iv  99 
Rookes, William s  William, of Rhodes Hall, Yorks, gent  University Coll , matric  30 June, 1665, aged 16; died at Oxford in 1667 
Roope, Ambrose s  A , of Dartmouth Parva, Devon, arm  Exeter Coll , matric  15 March, 1671-2, aged 16 
Roope, George s  Ant , of Bradford, Wilts, gent  Hart Hall, matric  10 Oct , 1702, aged 15 
Roope, John s  Nicholas, of Dartmouth, Devon, gent  Exeter Coll , matric  17 Nov , 1637, aged 15; student of Lincoln's Inn 1638  See Foster's Inns of Court Reg 
Roope, Nicholas of Devon, gent  Broadgates Hall, matric  6 Feb , 1606-7, aged 18; B A  6 Nov , 1610; probably father of the last-named 
Rooper, Thomas s  T , of London, gent  Trinity Coll , matric  9 July, 1699, aged 16; B A  1703, M A  19 Feb , 1705-6, as Roper 
Rooper, William of St  Alban Hall 1667  See Roper 
Roos, Brian D Can L  or doctor of decrees of the university of Valentia; incorporated 3 Feb , 1510-11; died 1529, buried in the church of Chelray  See Fasti, i  31 
Root, Isaac pleb  St  John's Coll , matric  2 July, 1658, admitted to Merchant Taylors' school 1649 (only son of Isaac, merchant taylor); born in Trinity parish 20 Aug , 1641  See Robinson, i  193 
Roots, Richard s  Tho , of Tunbridge, Kent, gent  St  John's Coll , matric  26 Dec , 1689, aged 15; demy Magdalen Coll  1690-1702, B A  1693, M A  1696, rector of Chilmarck, Wilts, 1702-27, canon of Sarum 1722, rector and vicar of Bishopstone, Wilts, 1728; brother of William 1699  See Rawl  iii  447, and xix  90; Bloxam, vi  111; & Foster's Index Eccl 
Roots, Thomas of Sussex, pleb  Magdalen Hall, matric  entry 17 Nov , 1581, aged 13; B A  supd  1 July, 1584, bar -at-law, Lincoln's Inn, 1594  See Foster's Judges and Barristers 
Rootes, Thomas s  William, of Tunbridge, Kent, pleb  St  John's Coll , matric  31 Jan , 1628-9, aged 23; B A  12 Feb , 1628-9, vicar of Long Stanton All Saints, co  Cambridge, 1630  See Add  MSS  15,669-70; & Foster's Index Eccl 
Rootes, Thomas pleb  St  John's Coll , matric  2 July, 1658; B A  1661, M A  1666; possibly father of Richard 1689, and William 1699 
Roots, William s  Tho , of Tunbridge, Kent, gent



ground_truth: Bigger receptive field size leads to more successful shape completion.

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: Who was Ralph Rokebye's brother?

Read the following text and answer briefly.

Context 3:  Christ Church, matric  16 March, 1698-9, aged 18; B A  1704; clerk Magdalen Coll  1705-11, M A  1707, rector of Little Berkhampstead, Herts, 1714; brother of Richard 1689  See Bloxam, ii  85; & Foster's Index Eccl 
Roper, Francis s  Robert, of Trimdon, co  Durham, gent  Corpus Christi Coll , matric  16 Dec , 1661, aged 18; probably identical with Francis, son of Robert, of Kelloe, co  Durham, farmer, was admitted sizar of St  John's Coll , Cambridge, 21 Sept , 1658, aged 16; fellow, B A  1662-3, M A  1666, B D  1673, vicar of Waterbeach, co  Cambridge, 1678, canon of Ely 1686-90, rector of Northwold, Norfolk, 1687, died 13 April, 1719  See Mayor, 138; Surtees' Durham, i  107; & Foster's Index Eccl 
Roper, John (or Rooper) demy Magdalen Coll , from Berks, M A  fellow, 1483, D D  disp  27 June, 1506, (first) Margaret professor of divinity, 1500, vice-chancellor of the university 1505, and 1511, principal of Salesurry and George Hall, rector of Witney, Oxon, 1493, vicar of St  Mary's church, Oxford, canon of Cardinal Coll  1532; died May, 1534  See Ath  i  76; & Landsowne MS  979, f  118 
Roper, John B A  disp  4 July, 1512 
Roper, Thomas of Trinity Coll  1699  See Rooper 
Roper, Philip of Kent, arm  Gloucester Hall, matric  7 Sept , 1588, aged 15 (subscribes Rooper) 
Roper, William (subscribes Rooper) of co  Hereford, militis fil  St  Alban Hall, matric  entry dated 5 June, 1607, aged 13; probably of Malmains, Kent, 2nd son of Sir Christopher Roper, afterwards 2nd baron Teynham  See Foster's Peerage 
Roscarrock, Henry of Cornwall, arm  Hart Hall, matric  entry under date 17 Dec , 1576, aged 21; probably son of Thomas, of Roscarrock, and brother of the next, and of Richard 1581 
Roscarrock, John B A  11 Feb , 1576-7; perhaps from Exeter Coll  (and 1s  Thomas, of Roscarrock, Cornwall); died 24 Nov , 1608; brother of Henry and Richard  See O H S  xii  65 
Roscarrock, Nicolas (Roiscariot) B A  supd  3 May, 1568, student Inner Temple 1571, as of Roscarrock, Cornwall  See Foster's Inns of Court Reg 
Roscarrock, Richard of Cornwall, arm  Broadgates Hall, matric  entry under date circa 1581, aged 19; student of Middle Temple 1583 (as 3s  Thomas, of Roscarrock, Cornwall, esq ), brother of Henry and John  See Foster's Inns of Court Reg 
Rosdell, Christopher of Yorks, pleb  St  Edmund Hall, matric  entry under date 22 Dec , 1576, aged 22, B A  4 July, 1576; rector of St  Bennet Sherehog, London, 1579, and vicar of Somerton, Somerset, 1582  See Foster's Index Eccl 
Rose, Christopher s  John, of Marlow, Bucks, gent  Christ Church, matric  13 Feb , 1622-3, aged 21, B A  same day; rector of Hutton, Essex, 1642  See Foster's Index Ecclesiasticus 
Rose, Christopher s  Giles, of Lynn Regis, Norfolk, gent  Lincoln Coll , matric  8 July, 1670, aged 15; student of Gray's Inn, 1673  See Foster's Gray's Inn Register 
Rose, Gilbert Augustinian Canon, B D  supd  22 May, 1512, and supd  12 Dec , 1519, for incorporation as D D 
Rose, Henry "ser " Lincoln Coll , matric  22 July, 1658, B A  16 Jan , 1660-1, fellow 1662 from Pirton, Oxon, M A  1663 (incorporated at Cambridge 1688), B D  1672; minister of All Saints, Oxford, but running much into debt, and marrying beneath himself, left his fellowship and church about 1674, retired to London, and at length to Ireland  See Ath  iv  561 
Rose, Hugh s  "Dav  Ni " (Nigg 4to ), of Ross, Scotland, p p  (subs  pleb )  Balliol Coll , matric  3 April, 1707, aged 20; B A  1709 
Rose, John B A  8 June, 1519, fellow Merton Coll  1523, M A  31 March, 1525; one of these names vicar of Shoreham, Kent, 1536  See Foster's Index Ecclesiasticus 
Rose, John of co  Leicester, pleb  Merton Coll , matric  24 Nov , 1581, aged 21 
Rose, John s  Jeremy, of Swell, co  Gloucester, pleb  Corpus Christi Coll , matric  12 Dec , 1623, aged 15; B A  4 July, 1626 
Rose, John s  Rich , of Halberton, Devon, gent  Exeter Coll , matric  14 May, 1688, aged 17 
Rose, John s  J , of West Derby, co  Lancaster, pleb  University Coll , matric  7 March, 1712-13, aged 18, B A  1716; rector of Bilborough, Notts, 1722  See Foster's Index Eccl 
Rose, Jonathan s  Th , of Mickleton, co  Gloucester, pleb  St  Alban Hall, matric  16 May, 1677, aged 18; B A  9 Feb , 1680-1 
Rose, Joseph s  Thomas, of Sturminster Newton, Dorset, pleb  Oriel Coll , matric  12 Dec , 1623, aged 19 
Rose, Richard B A  from Exeter Coll  14 June, 1621; perhaps student of Middle Temple 1622 (as son and heir of John, of Lyme, Dorset, gent ), and M P  Lyme Regis April-May, 1640, 1640 (l p ), till his death after 1648  See Foster's Inns of Court Reg  & Foster's Parliamentary Dictionary 
Rose, Richard arm  Exeter Coll , matric  29 March, 1656; student of Lincoln's Inn 1659, as 4s  Richard, of Wootton Fitzwarren, Dorset, esq  See Foster's Inns of Court Reg 
Rose, Richard s  Richard, of Monks Kirby, co  Warwick, pleb  Magdalen Coll , matric  3 May, 1672, aged 16 (as Rosse); chorister 1670-6  See Bloxam, i  95 
Rose, Richard s  R(ichard), of Wyng, Bucks, gent  Trinity Coll , matric  7 May, 1680, aged 16; bar -at-law, Inner Temple, 1699  See Foster's Judges and Barristers 
Rose, Stephen of co  Gloucester, pleb  Corpus Christi Coll , matric  21 Jan , 1619-20, aged 16; B A  13 Nov , 1621, M A  2 July, 1625, vicar of Aldermaston 1627, and rector of Barkham 1633, and of Arborfield, Berks, 1640, and perhaps of Hartley Mawditt, Hants, 1652  See Foster's Index Ecclesiasticus 
Rose, Stephen "ser " Lincoln Coll , matric  19 Nov , 1650 
Rose, Stephen "servi  fil " Magdalen Coll , matric  19 Nov , 1650 (subscribes "serv ") 
Rose, Stephen "ser " Magdalen Coll , subscribed 23 Nov , 1655; B A  from Wadham Coll  1659, vicar of Cold Overton, co  Leicester, 1662-3, and rector of Woolhampton, Berks, 1667-95, father of Temple  See Foster's Index Eccl 
Rose, Temple s  Step , of Woolhampton, Berks, cler  Trinity Coll , matric  29 March, 1693, aged 17, B A  1696 
Rose, Thomas Minorite, B D  22 June, 1509 
Rose, Thomas of Herts, pleb  Magdalen Hall, matric  10 Oct , 1589, aged 15 
Rose, Thomas s  Seth, of Telscombe, Sussex, sacerd  Oriel Coll , matric  5 June, 1640, aged 18; his father rector of Telscombe 1604, etc  See Foster's Index Eccl 
Rose, Thomas s  Edw , 



ground_truth: Sir Richard.