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

Question: What is the rationality coefficient used in the observation model?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Incorporating Human Path Preferences in Robot Navigation with Minimal Interventions
Publish Date: 16 Mar 2023
Author List: Oriana Peltzer, Dylan Asmar, Mac Schwager, Mykel Kochenderfer

Figure

Hyperplane arrangement of a twodimensional space containing two obstacles (colored in gray) The robot is located inside the pink polytope, surrounded by three adjacent obstacle-free polytopes Each hyperplane on the boundary of the robot's polytope corresponds to one of the nonredundant constraints in eq (4) (b)Graph derived from the hyperplane arrangement The nodes on the graph designate polytopes, and edges designate transitions to adjacent polytopes To estimate the human's preference, the robot updates a posterior over the goal and over which of the graph transitions φ 1 , φ 2 and φ 3 is preferred by the human (c)Example preference defined over the graph The location of the goal is indicated in yellow in the lower right polytope For each node, the outgoing pink arrow designates the edge on the graph corresponding to the preferred transition between polytopes 
Simple, 10 × 10, 8 polytopes (b) Map 2: Office, 10 × 10, 56 polytopes (c) Map 3: Classroom, 20 × 20, 73 polytopes (d) Sampled observations and robot's executed trajectories 
Fig 5: Maps used for simulating the robot navigation problem with path preferences In (d), the heading angles observed are indicated with arrows The goal is indicated with a pink circle, and the orange robot corresponds to the starting location The blue robot follows a policy that accounts for path preference, while the green robot does not The opacity of the robots increases with time 
Map 1 problem setup and example realizations for goal-only (green) and path preference (blue) solution methods The robot starts at the lower left corner of the environment, and the goal of the task (pink circle) is in the upper left area The robot does not know which goal, among 10 options (shown in light blue squares), is the correct goal The human provides noisy observations, indicated by arrows, at each iteration The green robot selects actions according to the goal-only baseline, and the blue robot uses our proposed method to infer path preferences The polytopes composing G are drawn in blue Probability of correct goal WLPHVWHS +J (c) Entropy of goal distribution g 
Fig  6: Probability of the correct goal, fig 6b, and entropy of the goal belief distribution P (g), fig 6c, for the same problem setup, fig 6a In this problem instance, the human's preference is to go to the goal by passing on the right side of the obstacle Results are averaged over 50 runs and the area filled represents one standard deviation above and below the mean value The goal-only baseline shows an over-confident prediction (shown by the strong reduction in belief entropy) that the correct goal is less likely, making it more difficult to reach the correct goal compared to a method that accounts for path preference 
Success rates in the simple environment (Map 1) The results are averaged over 6 randomly sampled problem instances (start location, goal location, and goal possibilities), and over 50 runs per problem instance ∆T is the number of time steps separating two consecutive human inputs The robot's mission time is Tmax = 30 time steps We selected γ h = 1 5, corresponding to relatively noisy human inputs and making the problem more difficult to solve for the robot 
Computation times for Goal Only and Path Preference methods on Map 1 (fig 5a),Map 2 (fig 5b), and Map 3 (fig 5c),averaged over 100 runs with randomly sampled problem instances The 95 % confidence interval is provided with the mean We evaluate computation time at the first iteration of each run (where the search depth takes on its highest value Tmax) 

abstract

Robots that can effectively understand human intentions from actions are crucial for successful human-robot collaboration  In this work, we address the challenge of a robot navigating towards an unknown goal while also accounting for a human's preference for a particular path in the presence of obstacles 
This problem is particularly challenging when both the goal and path preference are unknown a priori  To overcome this challenge, we propose a method for encoding and inferring path preference online using a partitioning of the space into polytopes  Our approach enables joint inference over the goal and path preference using a stochastic observation model for the human 
We evaluate our method on an unknown-goal navigation problem with sparse human interventions, and find that it outperforms baseline approaches as the human's inputs become increasingly sparse  We find that the time required to update the robot's belief does not increase with the complexity of the environment, which makes our method suitable for online applications 

INTRODUCTION

Collaboration between humans and robots has become increasingly important and one key aspect of this collaboration is the ability for robots to adapt to human decisions  In many scenarios, such as a robot navigating through a busy room to deliver an item, it is important for the robot to take into account human preferences 
For instance, humans may prefer a specific path that would allow their colleagues to notice the item being delivered, but this preference may change dynamically based on various factors such as changes in the environment or unforeseen circumstances  While some preferences can be incorporated into the path-planning process, accommodating dynamic user preferences in real-time remains challenging 
In this paper, we propose a way to enable robots to adapt to human preferences dynamically by leveraging real-time feedback to inform decision-making  In this work, we tackle the problem of robot navigation in which the robot cannot observe the goal or the preferred path to the goal, but must make navigation decisions that are influenced by humans through recommended actions 
Prior work has explored how to adapt to a human's preference through feedback, but such approaches often require a high level of intervention, which can be time-consuming and impractical in real-world scenarios  To optimize the use of human input and quickly infer the human's preference, Fig  : An autonomous robot navigates in a simulated classroom towards a goal location (pink circle) 
At the start of its mission, it receives direction indications (arrows) from a human that indicate which path it should take to get to the goal  In this scenario, the human wants the robot to go around the desks on the right side of the classroom  A robot that does not reason over path preferences (green) will take the shortest path to the goal regardless of the human's input 
Our method (blue) infers the human's path preference from these indications and adapts to their recommendations  we propose an approach that leverages probabilistic representations of human preference and incorporates real-time feedback  Previous research by Bajcsy et al  considered an online adaptation problem in a manipulation task, where the person can apply forces to the robot to indicate their preferences 
By allowing the robot to continue its task while taking into account a probabilistic representation of human preference, their approach does not require frequent inputs  Building on this idea, we adopt a similar approach to adapt to a human's preference in the context of a robot autonomously navigating through a known environment, such as a cluttered office space 
Specifically, we focus on allowing the human to influence the robot's trajectory with respect to obstacles, by providing guidance on preferred routes or paths, while the robot continues to execute its task  Paths can be represented using homotopy classes   However, homotopies can pose computational challenges when used to encode and infer human preferences 
When the robot maintains a belief over homotopy classes, the inference problem can become exponentially complex with the number of obstacles in the space  Additionally, when the goal is unknown, the number of variables increases with the number of candidate destinations  This complexity can render the decision-making problem intractable 
Our solution is to encode path preference based on a partitioning of the environment into polytopes   This representation allows path preferences to be expressed as sets of preferred transitions between adjacent polytopes  Paths belonging to different homotopy classes correspond to different sequences of transitions 
By leveraging conditional independence assumptions, we can make the Bayesian inference problem tractable  These assumptions exploit the fact that human actions provide information about the path in a piece-wise manner  For example, indicating a preference for navigating around a particular obstacle only provides information about the local area and not the entire path 
Finally, after updating its belief representation over the human's preference, the robot can adapt to indications by replanning online  Our contributions are as follows  • We formulate the human-robot collaboration problem as a Partially Observable Markov Decision Process (POMDP) where both the goal of the task and the human's path preference are unknown random variables 
• We propose an encoding of a human's path preference using a partitioning of the environment into polytopes, along with conditional independence assumptions that make the Bayesian inference problem tractable to infer the task goal and path preference online  • Through simulations in two environments of different sizes and complexity, we show that our method is effective for solving problems where the robot must reach a goal that is unknown a-priori while simultaneously adapting to a human's indications 
Our method shows higher success rates compared to baseline approaches when the human inputs are sparse  Our approach enables a robot to make effective navigation decisions in collaboration with a human, even when the goal and path preference are not known in advance, and with minimal human input  In recent years, there has been a growing interest in shared autonomy and interactive systems, where humans and robots work together to accomplish tasks 
Several approaches have been proposed to address the challenge of enabling effective collaboration between human and robot agents while still achieving high task performance  Losey et al  and Jeon, Losey, and Sadigh propose a framework where a human operator is given control of a task-relevant latent action space while an autonomous system handles the rest 
Dragan and Srinivasa present a formalism for arbitrating between a user's input and a robot's policy when both human and robot share control of the same action space  Cognetti et al  [7] provide a method for real-time modifications of a path,       Fig  : We model the intent inference problem with the above diagram 
At each step in time, the robot receives an observation ot from the human conditioned on its current location st, the intended goal g, and the human's path preference θ  The robot updates its belief over g and θ and transitions to a next location st+1  while Hagenow et al  present a method that allows an outside agent to modify key robot state variables and blends the changes with the original control 
However, a common challenge of these approaches is the high level of intervention required from humans  Best and Fitch propose a method for predicting an agent's intended trajectory from observations  Rather than maintaining a belief over the agent's future path, they infer the agent's intended goal among a set of candidate locations at the boundary of the space 
This approach provides information on where the agent is heading and generates a distribution of candidate future trajectories for the agent  Inferring the goal of the task among a discrete set of candidates is also relevant to the area of shared autonomy  Javdani, Srinivasa, and Bagnell propose a formalism for shared control of a robotic arm, where the robot must assist the human in picking up an object but needs to infer which object the human has chosen from joystick inputs 
Planning with homotopy class constraints is useful in problems where the robot's requirements are given with respect to obstacles, and Yi, Goodrich, and Seppi consider topological constraints provided by human operators  Bhattacharya propose an efficient algorithm for solving pathplanning problems under homotopic constraints 
However, the number of homotopy classes for a given problem can be infinite, and as the robot changes location and updates its representation of the world, carrying out inference over homotopy classes in a dynamic environment requires recomputing the set of homotopies at every iteration, making the belief update challenging 
Prior work has addressed the challenge of shared autonomy by considering how robots can infer a human's intended goal, or how they can infer the preferred path to a goal  However, we argue that inferring the goal and the path as separate problems can lead to over-confidence in incorrect beliefs about the user's preferences 
To illustrate this point, consider the following scenario: a robot and a human are collaborating to move an object from one end of a room to Fig  : Using the hyperplanes composing the H-representation of each obstacle, we construct a hyperplane arrangement of the obstacle-free space (a)  We define the human's preference for the robot's one step action choices as the posterior distribution (given all human input up to that point) over transitions from the current to the neighboring polytopes, i e  edges on the graph 
Each time the robot transitions to a new polytope, the set of neighbor polytopes and the distribution over human preferences are updated  another, but there is an obstacle in the way  The human would like the robot to take a path around the obstacle on the left, even though the goal is on the right  If the robot only infers the goal from the human's inputs, it may incorrectly assume that the goal is on the right, and become over-confident in this belief 
On the other hand, if the robot only infers the preferred path, it may mistakenly assume that the goal is on the left, leading to a failure in completing the task  To overcome these challenges, our work proposes a joint inference approach that considers both the human's intended goal and their preferred path to that goal 
Specifically, we model the human's preference over different homotopy classes and leverage a conditional independence assumption to provide a tractable solution  In our approach, we assume that the human's inputs are noisily rational conditioned on both the goal and the preference  By jointly inferring the goal and path preference, we can avoid over-confidence in incorrect beliefs about the user's preferences, leading to improved system performance 
We consider the problem of robot navigation in a known environment to an unknown destination, where a human can intervene and provide a heading direction to the robot using a joystick or force cues  The human also has a preference on which path the robot should take with respect to obstacles, and our objective is for the robot to understand the human's intentions and execute the task with minimal interventions 
Let g be a discrete random variable denoting the goal of the task, belonging to a set of candidates Ω g , and let θ be a discrete-valued random variable representing the human's path preference, belonging to a set of possible preferences Θ  The physical location of the robot at time index t is denoted by s t ∈ R 2 , and the robot's action at time index t, belonging to some action space A, is denoted by a t  
The transition model T (s t+1 | s t , a t ) is deterministic, meaning the robot has full control over its future location  At any time step, the human may provide an observation to the robot  When the human intervenes, the robot receives a direction (heading angle) that can be mapped to a future location in space 
More specifically, we map the direction to an intended location, which is the resulting robot location after advancing in the indicated direction for one time step  For simplicity, we consider that the robot directly makes an observation o t of the location indicated by the human  We assume that the robot has a stochastic observation model for the human P (o t | s t , g, θ) that is conditioned on both the goal of the task g and the human's preferred path θ 
We further assume that having chosen a goal and path preference, the human takes actions to noisily minimize a cost function C g,θ that measures the cost of moving from the robot's current location to the goal along the preferred path  For example, C g,θ (s t , o t ) can be the length of the shortest path from location s t to the goal g after taking a first step to o t , and constrained by path preference θ 
We use C g,θ to induce a probability distribution over observations, given by: where γ h is a hyperparameter that designates the rationality coefficient  This model assumes the human will pick the lowest cost action with the highest probability and the likelihood of an action decreases exponentially with the increase in cost  
Our inclusion of the path preference θ sets our approach apart from   The model is shown in fig  represented as a Bayesian Network 

Inference

At each time step where the human provides an observation, the posterior P (g, θ) is given through the Bayesian update We note that the number of Bayesian updates required at each iteration to update the belief is equal to the cardinality of Ω g × Θ  In addition, each Bayesian update involves computing C g,θ (  
,   ) in eq  ( ), which involves solving an optimization problem (such as a shortest path problem)  In section IV, we propose a specific encoding of preference θ for resolving eq  ( ), while ensuring the number of computations of the cost C g,θ ( ,  ) per update does not grow exponentially with the number of obstacles 

Decision Making

We consider a navigation problem where the robot receives reward according to the model R(s t , g, θ, a t )  We wish to find the optimal policy π that maximizes the expected discounted sum of future rewards, with discount factor γ  The above problem is a Partially Observable Markov Decision Process (POMDP)  
In this section, we propose an encoding of human's path preference θ for computing the posterior in eq  ( )  Devifrom the concept of homotopy classes, we define the preference according to a partitioning of the environment into polytopes, as shown in fig  , creating a hyperplane arrangement of the space 
Hyperplane arrangements have been used by Vincent and Schwager in the context of Neural Network verification  In our setting, we leverage this representation to define path preferences as preferred transitions between adjacent regions of the space 

Hyperplane Arrangement

We assume a two-dimensional environment composed of m polytopic obstacles, each defined by their half-space representation (H-representation) where A i ∈ R di×2 and b i ∈ R di , and where d i is the number of edges (hyperplanes) composing polytope i  Let n = i d i be the total number of hyperplanes  We leverage each obstacle's H-representation to construct a hyperplane arrangement of the environment as shown in fig 
 e  a partitioning of the space into polytopes  More specifically, each location in space belongs to a polytope j for which we can write an H-representation of the form where α j i ∈ {−1, 1} di is a vector specific to polytope j and obstacle i corresponding to the relative position of any point in the set with respect to each hyperplane in O i  
Fig  : Intent inference model in a hyperplane arrangement of the obstacle free space  We spatially decompose the preference θ into a set of preferred neighboring polytopes per region of the space  Within each polytope j, the human preference pj is a discrete distribution over the preferred neighbor in N (j) 
We assume that for a location st belonging to polytope j, and given goal g and preference pj, the observation ot and any other preference p i,i =j are conditionally independent  Concatenating elements from each obstacle's Hrepresentation, we can write polytope j's H-representation as where Some of the constraints in eq  ( ) (corresponding to rows of A, b and α j ) are redundant, i e  the set P j does not change upon their removal 
We can further reduce the Hrepresentation of a polytope to include only non-redundant constraints  By removing the rows corresponding to redundant constraints, we obtain new matrices A j e , b j e and α j e such that we can write the polytope's reduced H-representation as The non-redundant constraints correspond to edges of the polytope 
In other words, as the robot continually moves in space, the first hyperplane that it will cross upon exiting the polytope will correspond to one of the polytope's nonredundant constraints  Vincent and Schwager outline an iterative method for removing redundant constraints by solving n linear programs 
We use this method in practice for computing α j e for each polytope  We can now characterize each polytope by a vector α j e ∈ {−1, 1} n j e , where n j e ≤ n is the number of essential constraints of the polytope  The polytopes P j partition the environment into a hyperplane arrangement 

Path Preference

In this section, we provide a definition of preference θ according to a graphical representation of the environment based on the hyperplane arrangement  Under this representation, a path preference corresponds to a set of preferred transitions  In other words, for each polytope in the space, the human will have a preference to which neighboring polytope they wish to transition 
Let G := (V, E) be an undirected graph, where vertices are obstacle-free polytopes, and edges connect two adjacent polytopes  Each polytope is described by a unique vector α j as defined in eq  ( )  Two polytopes are adjacent if they share non-redundant constraints (rows in eq  ( )) corresponding to the same hyperplane (i e  they are on opposite sides of the hyperplane) 
Let N (v) be the set of neighbors of a vertex v  For each vertex, we denote p v the discrete-valued random variable describing which edge in N (v) the human intends to transition to  Using this formalism, we define a path preference as the set of preferred transitions over all nodes in the graph, Let m θ = v∈V |N (v)| be the cardinality of Θ, and m g = |Ω g | the number of possible goals 
A priori, the number of Bayesian updates required to update the belief at every iteration should be m θ × m g   Now, let us assume the conditional independence relationships described by the new problem diagram in fig    More specifically, we introduce the assumption that conditioned on a robot location s t , the goal g, and the preference for the corresponding vertex p v in the graph, the observation o t and the preference for any other vertex are conditionally independent 
In other words, the observations the human provides can be defined conditioned only on the robot location, the goal, and the human's preference for its current vertex p v   By introducing this assumption, each update step only requires updating the joint (p v , g), reducing the number of cost computations to |N (v)| × m g  
We can notice that by introducing this assumption, we removed the direct relationship between the number of polytopes in the environment and the complexity of the Bayesian update in eq  ( )  In practice, components of θ are not mutually independent  For example, if the human preference at a vertex v 1 is
, it is unlikely that the human will also prefer p v2 = (v 2 , v 1 ) (turning back)  We can improve our model by assuming a dependent relationship between preferences for adjacent edges, which does not significantly increase the complexity of the inference problem  An interesting property of our encoding is that any two paths that belong to different homotopy classes will cross different sequences of polytopes, i e  they correspond to a different sequence of edges on G 
This can be proved by contradiction  Let us suppose that two continuous trajectories ξ 1 and ξ 2 , with the same start and end points and that do not intersect any obstacle, traverse the same regions in G in the same order  From the construction of the hyperplane arrangement, each polytope that the paths traverse through is obstacle-free 
Therefore, within each polytope, there is no obstacle in the area located in between the portions of ξ 1 and ξ 2 that belong to the region  A smooth transformation of ξ 1 into ξ 2 can be obtained by transforming each portion of ξ 1 belonging to the polytopes it intersects into the corresponding portion of ξ 2 for the same polytopes, where the extremities of the trajectory portions are connected to one another along the polytope's edges (where the same edge is crossed by both paths) 
Along this transformation, the paths do not intersect any obstacle, and therefore ξ 1 and ξ 2 belong to the same homotopy class 

EXPERIMENTS

We evaluate our model on a simulated navigation task where the robot must reach a goal that is unknown a priori while respecting the path preferences indicated by a human  The robot navigates in a grid world containing obstacles  The transition model is deterministic: the robot selects an adjacent location on the grid to reach at the next time step 
The robot is also allowed to take diagonal actions  Each location s t in the map can be mapped to a vertex v t ∈ G  Therefore, the actions leading to locations mapped to different vertices correspond to edges on the graph  We note f (s t , a t ) the edge crossed by taking action a t from location s t  
The robot is given a mission time limit T max for reaching the goal  In this problem, we assume that the human selects actions to noisily minimize a cost function C g,θ , where θ is defined as per eq  ( ), corresponding to the length of the shortest path to the goal constrained by the preference (where the robot is only allowed to make transitions on G along preferred edges) 
More specifically, where δ(s t , g | o t , p vt ) designates the length of the shortest path from s t to g passing by o t and constrained by preference p vt   This is a slight variant of the cost function proposed by Best and Fitch , where we add in a conditioning on the path preference  We compute costs by running the A path planning algorithm on the environment maps (grid worlds with diagonal actions) and impose preference constraints by pruning invalid transitions from the search tree 
Reward model  At each step in time, the robot receives a reward which is a sum of three components: a goal-specific reward a preference-specific reward or penalty We compute solutions to the POMDP defined in section III-B with the online solver POMCP , and with the particularity that within the rollouts, the robot does not expect to collect human inputs 
Each time a solution is computed, the robot takes an action and may receive an observation  If it does, it updates its belief distribution over the unknown problem variables and resolves the POMDP over a receding horizon 

Baselines

• Goal only  The robot solves the POMDP while ignoring the effects of path preference  Similarly to , we assume the human is taking action to minimize a goaldependent cost C g (s t , o t ) = δ(s t , g | o t ), where the conditioning on the preference is removed  We also omit the path preference's contribution to the reward R pref  
• Compliant  The robot complies with the human input, but does not take an initiative  If the user stops providing information, the robot continues in the last direction indicated for 5 time steps (conserving its momentum), then stops  • Blended  We designed an arbitration function to decide between our proposed policy (accounting for path preferences) and the user's recommendation when the robot receives inputs 
Our metric to evaluate confidence in the robot's prediction for the purpose of arbitration is the entropy of the intention distribution H(g, p i ), where p i denotes the preferred neighbor for the current region  Because our representation of the world is discrete, the arbitration is given by a step function 
Denoted by U , the action corresponding to the human's input, and P , the robot's prediction for the optimal action, we write the policy where we chose h = 1 6 as the confidence threshold 

Results

When evaluating the algorithm, we consider that a run is successful if the robot reached the goal within its allocated mission time T max and only made transitions between graph vertices corresponding to the human's preferences  We vary the time delay between human inputs, from constant guidance (∆ T = 1) to only a single observation (∆ T ≥ T max ) 
Success rates  Table I reports the success rates for experiments conducted over six randomly sampled problem instances and 50 runs per instance in Map 1 (fig  )  When the human provides inputs at every iteration, the compliant policy shows the highest success rates  However, as ∆ T increases, the compliant robot is not able to accomplish the task within the allotted time as it does not receive sufficient inputs to do so, and performance decreases compared to the autonomous baselines 
We find that in these runs, accounting for path preference consistently improves performance compared with the goal-only baseline  Results also show that blending the user's input with the robot's policy (Path Preference + Blend) when the human provides information leads to improved performance  Belief entropy 
Figure shows a challenging problem instance where the directions the human provides do not align directly with the shortest path to the goal  By ignoring the effects of preferences in the problem model (goal only), the robot quickly infers from observations that the upper left goal is less likely than others (P (g) drops) 
The strong decrease in entropy shows that the robot becomes overconfident in this prediction  Overconfidence in an incorrect goal will prevent the agent from finding the correct goal once the human's indications directly align with it, as it needs to correct for the wrong predictions, as shown in the path realization (fig 
)  In this realization, the goal-only method (green robot) fails to search the upper left area within the allotted time  By accounting for path preferences in its model, the blue robot's entropy over the goal distribution decreases more steadily, allowing for it to leverage the human's latest observations and reach the goal successfully 
shows an over-confident prediction (shown by the strong reduction in belief entropy) that the correct goal is less likely, making it more difficult to reach the correct goal compared to a method that accounts for path preference  Computation time  In table II we provide the time required to solve the POMDP, and the time required to update the robot's belief as it receives new observations 
We compute solutions on three maps: a simple 10 × 10 grid world with 8 polytopes (fig  ), a 10 × 10 grid world with 56 polytopes (fig  ), and a 20×20 grid world with 73 polytopes (fig  )  The latter environment being larger, we increase the mission time and the depth of the search tree in POMCP from T max = 30 (Map 1 and Map 2) to T max = 60 (Map 3) 
We do not notice an increase in the time required to update the robot's belief with an increase in problem complexity, which is consistent with our observation that the complexity of the Bayesian update should not increase with the number of obstacles or polytopes  On the contrary, the belief update time on Map 2 and Map 3, containing more obstacles, is reduced compared to the first map 
More obstacles result in fewer iterations when solving the constrained shortest path problem with A   Adding constraints due to the obstacles and polytopes reduces the size of the A search tree  C  Limitations Simulation environments  In our simulations, we hardcoded the preference policy over the maps (e g  in Map 1, go around the table counter-clockwise) 
We randomly sampled problem instances (start and goal locations, and goal options) to reduce the bias introduced by these preference choices  To best evaluate and compare the different approaches, it would be best to sample preferences among a distribution of preferences chosen by a human (for example, from benchmarks resulting from a collection of data) 
Creating such a benchmark is an interesting direction for future work  Hyperplane arrangement construction  The main limitation of our approach is that the size and geometry of each polytope depends strongly on the geometry of the obstacles, as seen in fig    Because of this, the robot can make predictions over preferences that are too refined compared with the topology of the environment 
A direct consequence is that when the size of the polytopes is small, the information provided by the human can be incorrectly interpreted as a preference on the robot's immediate action  Our method can be improved by changing the structure of the hyperplane arrangement so that it relies on the topology of the environment, but does not vary strongly with the geometry of the features in the environment 
For this purpose, topometric maps and region construction algorithms are promising directions  We presented an approach for encoding and inferring a human's path preference in an environment with obstacles  By leveraging a partitioning of the space into polytopes and a stochastic observation model, our method allows for joint inference over the goal and path preference even when both are unknown a-priori 
Our experiments on an unknown-goal navigation problem with sparse human interventions demonstrate the effectiveness of our approach and its suitability for online applications  The time required to update the robot's belief does not increase with the complexity of the environment, which further highlights the practicality of our method 
Context 2: Sir Simon William English  (born 30 December 1961) is a New Zealand former National Party politician who served as the 39th prime minister of New Zealand from 2016 to 2017  He had previously served as the 17th deputy prime minister of New Zealand and minister of finance from 2008 to 2016 under John Key and the Fifth National Government 

A farmer and public servant before entering politics, English was elected to the New Zealand Parliament in  as the National Party's candidate in the Wallace electorate  He was elevated to Cabinet in 1996 and in 1999 was made minister of finance, although he served for less than a year due to his party's loss at the 1999 general election  In October 2001, English replaced Jenny Shipley as the leader of the National Party (and consequently as Leader of the Opposition)  He led the party to its worst defeat at the 2002 general election, and as a consequence, in October 2003 he was replaced as leader by Don Brash 

In November 2006, after Brash's resignation, English became deputy leader under John Key  After National's victory at the 2008 general election, he became deputy prime minister and was also made minister of finance for the second time  Under English's direction New Zealand's economy maintained steady growth during National's three terms of government  He became a list-only MP after stepping down as an electorate MP at the 2014 general election 

John Key resigned as leader of the National Party and prime minister in December 2016  English won the resulting leadership election unopposed and was sworn in as prime minister on 12 December 2016  His tenure was only ten months, and included a three-month election campaign  In the 2017 general election, National won the largest number of seats but fell short of a majority  The parties holding the balance of power declined to support the existing government, and English was subsequently replaced as prime minister by Jacinda Ardern, leader of the Labour Party  English initially continued on as Leader of the Opposition, but resigned as leader of the National Party on 27 February 2018 and left parliament two weeks later 

Early life
English was born on 30 December 1961 at Lumsden Maternity Centre in Lumsden  He is the eleventh of twelve children of Mervyn English and Norah (née O'Brien) English  His parents purchased Rosedale, a mixed sheep and cropping farm in Dipton, Southland from Mervyn's uncle, Vincent English, a bachelor, in 1944  English was born in the maternity unit at Lumsden 

English attended St Thomas's School in Winton, then boarded at St  Patrick's College in Upper Hutt, where he became head boy  He played in the first XV of the school's rugby team  English went on to study commerce at the University of Otago, where he was a resident at Selwyn College, and then completed an honours degree in English literature at Victoria University of Wellington 

After finishing his studies, English returned to Dipton and farmed for a few years  From 1987 to 1989, he worked in Wellington as a policy analyst for the New Zealand Treasury, at a time when the free market policies favoured by Labour's finance minister Roger Douglas (known collectively as "Rogernomics") were being implemented 

English joined the National Party in 1980, while at Victoria University  He served for a period as chairman of the Southland branch of the Young Nationals, and became a member of the Wallace electorate committee  After moving to Wellington, he served for periods on the Island Bay and Miramar electorate committees, respectively 

Fourth National Government (1990–1999)

At the 1990 general election, English stood as the National candidate in Wallace, replacing the retiring Derek Angus, and was elected with a large majority  He would hold this seat, renamed Clutha-Southland in 1996, until 2014  He and three other newly elected National MPs (Tony Ryall, Nick Smith, and Roger Sowry) were soon identified as rising stars in New Zealand politics, and at various points were dubbed the "brat pack", the "gang of four", and the "young Turks"  In his first term in parliament, English chaired a select committee into social services  He was made a parliamentary under-secretary in 1993, serving under the Minister of Health 

First period in cabinet (1996–1999)
In early 1996, English was elevated to cabinet by Prime Minister Jim Bolger, becoming the Minister for Crown Health Enterprises and Associate Minister of Education (to Wyatt Creech)  He was 34 at the time, becoming the cabinet's youngest member  After the 1996 general election, the National Party was forced into a coalition with New Zealand First to retain government  In the resulting cabinet reshuffle, English emerged as Minister of Health  However, as a condition of the coalition agreement, NZ First's Neil Kirton (a first-term MP) was made Associate Minister of Health, effectively becoming English's deputy  This arrangement was described in the press as a "shotgun marriage", and there were frequent differences of opinion between the two ministers  After their relationship became unworkable, Kirton was sacked from the role in August 1997, with the agreement of NZ First leader Winston Peters 

As Minister of Health, English was responsible for continuing the reforms to the public health system that National had begun after the 1990 general election  The reforms were unpopular, and health was perceived as one of the government's weaknesses, with the health portfolio consequently being viewed as a challenge  English believed that the unpopularity of the reforms was in part due to a failure in messaging, and encouraged his National colleagues to avoid bureaucratic and money-focused language (such as references to "balance sheets" and "user charges") and instead talk about the improvements to services the government's reforms would bring  He also rejected the idea that public hospitals could be run as commercial enterprises, a view which some of his colleagues had previously promoted 

By early 1997, as dissatisfaction with Bolger's leadership began to grow, English was being touted as a potential successor, along with Jenny Shipley and Doug Graham  His age (35) was viewed as the main impediment to a successful leadership run  National's leadership troubles were resolved in December 1997, when Bolger resigned and Shipley was elected to the leadership unopposed  English had been a supporter of Bolger as leader, but Shipley reappointed him Minister of Health in her new cabinet 

English was promoted to Minister of Finance in a reshuffle in January 1999, a position which was at the time subordinate to the Treasurer, Bill Birch  After a few months, the pair switched positions as part of Birch's transition to retirement, with English assuming the senior portfolio  In early interviews, he emphasised his wish to be seen as a pragmatist rather than an ideologue, and said that the initiatives of some of his predecessors (Roger Douglas's "Rogernomics" and Ruth Richardson's "Ruthanasia") had focused on "fruitless, theoretical debates" when "people just want to see problems solved" 

Opposition (1999–2008)

After the National Party lost the 1999 election to Helen Clark's Labour Party, English continued on in the shadow cabinet as National's spokesperson for finance  He was elected deputy leader of the party in February 2001, following the resignation of Wyatt Creech, with Gerry Brownlee being his unsuccessful opponent 

Leader of the Opposition
In October 2001, after months of speculation, Jenny Shipley resigned as leader of the National Party after being told she no longer had the support of the party caucus  English was elected as her replacement unopposed (with Roger Sowry as his deputy), and consequently became Leader of the Opposition  However, he did not openly organise against Shipley, and according to The Southland Times "there was almost an element of 'aw, shucks, I'll do it then' about Mr English's ascension" 

Aged 39 when he was elected, English became the second-youngest leader in the National Party's history, after Jim McLay (who was 38 when elected in 1984)  He also became only the third Southlander to lead a major New Zealand political party, after Joseph Ward and Adam Hamilton  However, English failed to improve the party's performance  In the 2002 election, National suffered its worst electoral defeat ever, gaining barely more than twenty percent of the vote  English described it as "the worst day of my political life"  Both party insiders and the general public were split as to how much to blame him for the loss, but most of the party believed that English would be able to rebuild National's support 

By late 2003, however, National's performance in opinion polls remained poor  The party had briefly increased its popularity in the year following the election, but by October its support had fallen to levels only slightly better than what it achieved in the last ballot  English also appeared in a boxing match for a charity against entertainer Ted Clarke  This did not boost his polling or that of the National party either, with suggestions that it devalued his image as a serious politician  Don Brash, former governor of the Reserve Bank and a relative newcomer to politics, began to build up support to replace English  On 28 October, Brash gained sufficient backing in Caucus to defeat English in a leadership contest 

Shadow cabinet roles and deputy leader
On 2 November 2003, when Brash changed responsibilities for certain MPs, English became National's spokesman for education, ranked at fifth place in the party's parliamentary hierarchy  He remained in parliament after the 2005 election  In his new shadow education portfolio, English performed strongly, and remained a party favourite despite his election defeat as leader in 2002, eventually being returned to the finance portfolio in August 2004 as deputy spokesman (while still retaining responsibility for education) 

In November 2006, Brash resigned as leader  English was considered as a potential replacement leader (running against John Key) or deputy leader (against incumbent Gerry Brownlee) in the ensuing leadership election  However, a contest was avoided when the MPs agreed a Key/English ticket would run unopposed in a display of party unity  English took over the deputy leadership and the finance portfolio in the Key shadow cabinet 

Fifth National Government (2008–2017)

Deputy Prime Minister and Minister of Finance (2008–2016)

At the 2008 election, English was re-elected by his electorate, winning by a margin of about 15,500 votes  He became Deputy Prime Minister of New Zealand and Minister of Finance in the fifth National Government, being sworn into office on 19 November 2008 and continued to serve in those roles until becoming Prime Minister on 12 December 2014  He was also made Minister of Infrastructure in National's first term of government and Minister responsible for Housing New Zealand Corporation and minister responsible for the New Zealand flag consideration process in its third 

He was comfortably re-elected in Clutha-Southland in the 2011 election but opted to run as a party-list candidate in 2014  

The pairing of John Key as leader of the National Party and English as his deputy has been compared to that of Bob Hawke and Paul Keating (in Australia) and Tony Blair and Gordon Brown (in the UK)
Context 3: The Official 2006 NBA Draft Early-Entry List  30 International Players, 62 underclassmen and one lone 5th year high school player make up this year's list, for a total of 93 early-entrants  Extensive commentary and early draft status projections are included   For comparison, in 2005, 108 players declared (61 NCAA, 12 high school, 35 internationals), up from 94 in 2004, and 73 in 2003 
There were no major surprises on the early-entry list, besides a few mid-major, Division II and NAIA players that decided to enter, as well as 5th year high school player Clarence Holloway  Amongst the International players, Hrvoje Peric, Renaldas Seibutis, and Kyrylo Fesenko are considered mild surprises who could end up cracking the 2nd round  NCAA Lottery prospects Joakim Noah, Al Horford, Corey Brewer, Josh McRoberts, Brandon Rush and Tyler Hansbrough decided to sit this one out as expected, as did Marco Belinelli, Uros Tripkovic and Goran Dragic on the international front 
Akbar Abdul-Ahad 6-0, PG, Idaho State Junior No Undrafted Averaged under 6 points in 20 minutes per game playing in the Big Sky  Being the first player on the NBA Draft Early-Entry list will likely go down as the highlight of his basketball career 
Arron Afflalo, 6-5, SG, UCLA Sophomore No Undrafted Afflalo initially told the LA media hes returning to school, but after a deep run in the NCAA tournament-- more in spite of his play than because of it--Afflalo will be testing the waters  Afflalo has very average size, athleticism, perimeter shooting and ball-handling skills  Hes clearly receiving bad advice on where his stock lies 
LaMarcus Aldridge, 6-11, PF/C, Texas Sophomore Yes Top 5 pick Aldridge made his announcement official to enter the draft some weeks ago  He will hire an agent soon (Arn Tellem?) and is considered a lock for the top 5 and a strong candidate for #1 overall 
Morris Almond, 6-6, SG, Rice Junior No ??? Almond announced hell be entering the draft, without an agent  He might be the best scorer in the NCAA youve never heard about  His stats are terrific, despite being the sole focal point of opposing defenses, and hes capable of scoring in a variety of ways, particularly with his jumper  Hes hoping for an invite to Orlando 
Renaldo Balkman, 6-8, PF, South Carolina Junior No Undrafted After winning the NIT MVP award, Balkman has decided to see where he stands in the eyes of the NBA by testing the waters  Hes likely to find them downright freezing, as hes a skinny and undersized power forward with little to no skills who came off the bench for a very average team 
Larry Blair,6-1, SG, Liberty Junior No Undrafted The 22 point per game scorer Blair is attempting to get some exposure for himself by testing the waters 
Will Blalock, Iowa State, 5-11, PG, Junior No Second round pick? Declared for the draft together with Curtis_Stinson after Iowa States coach was fired  Size is a big question mark  Will likely hope to attend the pre-draft camp in Orlando and try to show scouts hes a 1st rounder  Likely returns for his senior year 
Jahsha Bluntt, 6-6, SG, Deleware State Junior No Undrafted Puts up fairly average numbers (14 6 ppg, 41% FG) in one of the worst conferences in America  Looking for exposure at the Orlando pre-draft camp but its highly unlikely to receive it 
Josh Boone, 6-10, PF/C, UConn Junior No First round pick? Boone announced hell be entering the draft without an agent  An up and down season has left his stock in the air, and will likely force him to prove himself at the Orlando pre-draft camp  Would greatly benefit from a productive senior season as an offensive focal point now that UConn has lost almost all of its firepower from last year 
Ronnie Brewer, 6-6, PG/SG, Arkansas Junior No Lottery pick? After initially wavering a bit on his decision, Brewer announced hell be entering the draft without an agent in a press conference  Brewer is considered a likely late lottery pick to mid-first rounder pick, as his physical attributes and array of versatile skills on both ends of the floor are highly sought after 
Bobby Brown, 6-1, PG, Cal-State Fullerton Junior No First round pick? DraftExpress exclusively reported that Brown will be testing the waters  Still considered a bit of a sleeper because of the school he plays for, he will not be hiring an agent at this point  Some scouts are very high on his quickness and perimeter shooting ability and feel he will help his stock tremendously in private workouts 
Shannon Brown, 6-4, SG, Michigan State Junior No First round pick? As exclusively reported by DraftExpress, Brown will be testing the waters  He will likely conduct a number of workouts and attend the Orlando pre-draft camp to attempt and gauge where his stock lies  Scouts compare him to Celtic guard Tony Allen, but with a better attitude  Hes a very borderline first rounder in a draft that is stacked with shooting guards 
Derek Burditt, 6-7, SG, Blinn Junior College Sophomore No Undrafted Unknown Junior College prospect  Not ranked as one of the top 25 JUCO players in the country, averaged around 17 points per game  Not burning his draft card as hes not yet an NCAA player, so really doesnt have much to lose, or gain 
Leroy Dawson, 6-2, SG, Emporia State Junior No Undrafted Anonymous Division II player from the MIAA conference  2nd team all conference, averaged 20 points per game  Like MANY on this list, only declaring because he can and has nothing to lose 
Travis DeGroot, 6-4, SG, Delta State Junior No Undrafted Plays in a strong Division II conference, but is at best only the 3rd best prospect on his own team after Jasper Johnson and Jeremy Richardson, and is therefore not a prospect at all 
Guillermo Diaz, 6-2, PG/SG, Miami Junior Yes First round pick? As reported by DraftExpress all year long, Diaz decided to forgo his senior year of college by hiring an agent, Miami based Jason Levien  One of the top athletes and shooters in the draft, which makes for an intriguing combination 
Cem Dinc, 6-10, SF/PF, Indiana Freshman No Undrafted As exclusively reported by DraftExpress, Dinc will be testing the waters  The coach that recruited him and then never played him, Mike Davis, resigned, so it would not shock anyone to see Dinc return to play in Europe and become automatically eligible next year after pulling out of this years draft 
Quincy Douby, 6-3, PG/SG, Rutgers Junior No First round pick As exclusively reported by DraftExpress, Douby sent out his paperwork to enter the draft  NBA scouts are all over the board on him, with some saying they consider him a 2nd round pick and others saying they would not be surprised if he ended up in the lottery  Terrific shooter and shot creator, averaged 28 ppg in the Big East conference  A real sleeper who will likely play in Orlando 
Mike Efevberha, 6-5, SG, Cal State Northridge Junior ??? Undrafted Ramona Shelburne of the LA Daily News reported that Efevberha will be testing the waters  Efevberha was the leading scorer in the country until he had a falling out with his coach and saw his playing time reduced significantly  Hell likely be looking for an invite to the Orlando pre-draft camp, and does not appear to be likely to head back to school 
Carl Elliot, 6-4, PG, George Washington Junior No Undrafted Elliot is using his use it or lose it draft card as a junior to get some exposure for himself through workouts and try to figure out where he stands in the eyes of the NBA  Elliot has excellent size for the PG position, but is still lacking plenty of all-around polish  His senior year will be essential to his development as a player  Reportedly has a family to support, which makes his decision tough considering how old he is already, despite only being a junior 
Jordan Farmar, 6-2, PG, UCLA Sophomore No First round pick? Farmar was the engine that led his team to the Finals of the NCAA tournament, and the only player that showed up once they got there  He is one of the top playmakers in the country, a Steve Nash type point guard, but his average athleticism, defense and outside shooting means hes only a bubble first-rounder  DraftExpress has been on his bandwagon since day one at UCLA, but is the NBA on it too?
Nick Fazekas, 6-11, PF, Nevada Junior No First round pick? Fazekas announced hell be entering the draft without an agent and will likely return to Nevada if it looks like hes not going to be a first round pick  If hes not a first rounder this year, its hard to imagine him ever being one since there isnt much left for him to accomplish individually in the NCAA  An interesting candidate for the pre-draft camp in Orlando 
Thomas Gardner, 6-5, SG, Missouri Junior No Second round pick? The St  Louis Post Dispatch reported that Gardner will enter the draft  Firing of underachieving Missouri coach Quin Snyder appeared to be the straw the broke the camels back  Gardner will have to hope to get invited to Orlando, but moving into the first round appears unlikely without an incredible performance there 
Rudy Gay, 6-8, SF, UConn Sophomore Yes Top 10 pick Gay announced hes leaving UConn at a press conference on campus, with Coach Calhoun by his side  He will hire an agent eventually  Size, length, incredible talent and athleticism means he might have the most upside of any player in this draft  Does he have the fire to capitalize on it though?
Reggie George, 6-10, PF, Robert Morris Chicago (NAIA) Junior No Undrafted Transfer from Iowa State had a nice season in the NAIA and is looking to capitalize on it by gaining some exposure for himself 
Daniel Gibson, 6-2, PG/SG, Texas Sophomore ??? Second round pick As exclusively reported by DraftExpress, Gibson will be entering the draft  There appears to be a conflict between Gibson and Texas regarding what his role will be next year, specifically whether or not hell be playing the point, meaning its unclear whether or not hell be returning  Gibson will likely go to Orlando to help him decide what his next step is  Showing off some PG skills will be essential there 
Aaron Gray, 7-0, Center, Pitt Junior No First round pick? After a disappointing end to his season, being outplayed by Patrick OBryant in the NCAA tournament, Gray has put that behind him and entered his name in the draft without an agent  Hes yet another underclassmen with huge questions marks about his pro potential that will likely have to go to the Orlando pre-draft camp to show he is worthy of a first round pick  Made some great strides this year, but still has a ways to go, especially conditioning-wise 
LeShawn Hammett 6-0, PG, St  Francis Junior No Undrafted Undersized combo guard played only 7 minutes in the mighty Northeast Conference before being suspended indefinitely for conduct detrimental to team  The NBA is clearly the only goal left for him to achieve 
Brandon Heath, 6-3, PG/SG, San Diego State Junior No Second round pick? Streaky shooting combo guard Heath announced the he will test the NBA draft process this summer, and is hoping for an invite to the Orlando pre-draft camp  MWC player of the year; has a lot of wrinkles to his game that need to be ironed out before he can legitimately think about the NBA 
Tedric Hill, 6-10, PF, Gulf Coast Community College Sophomore Yes Undrafted Ineligible to return to school after flunking out of college once again  Has bounced around over the past few years, and received some early hype from wannabe draftniks such as Gregg Doyel (CBS-Sportsline) and Sam Smith (Chicago Tribune) who compare him to Kevin Garnett  Very athletic we're told, but has absolutely no idea how to play the game  Has no chance of being drafted without an amazing showing at the Orlando pre-draft camp 
Clarence Holloway 7-0, Center, IMG Academy (Prep School) 5th year High School No Undrafted Lone high school player in this years age-limit depleted draft  Former Louisville commit never got eligible for college and was always considered too slow and heavy to make much of an impact anyway  Reportedly lost weight and improved his grades this past year at IMG and is currently being recruited by UConn, Kansas State and Oklahoma, amongst others 
Ekene Ibekwe, 6-9, PF, Maryland Junior No Undrafted Sources told DraftExpress exclusively that Ibekwe will be testing the waters  Likely only making this move because he can, as his chances of being drafted are very low  Athletic and long, but still lacking any type of polish 
Donald Jeffers, 6-8, PF, Roxbury Community College Sophomore No Undrafted Anonymous junior college player 
Alexander Johnson, 6-9, PF, Florida State Junior Yes First round pick? Sources told DraftExpress, that Johnson will be hiring an agent, mainly because he is already 23 years old  Hes considered intriguing because of his strength, raw offensive tools and freakish athleticism at the 4 position, and could work his way into the 1st round with strong workouts 
David Johnson, 6-7, PF, Clinton Junior College Sophomore No Undrafted 6-7 JUCO power forward who averaged 2 points and 3 rebounds per game 
Trey Johnson, 6-5, SG, Jackson State Junior No Undrafted Small school prolific scorer and one of the most accurate perimeter shooters in the country will attempt to draw some more attention to himself by testing the waters this summer  Johnson is hoping for a chance to prove himself in the Orlando pre-draft camp in June 
Coby Karl, 6-4, PG/SG, Boise State Junior No Undrafted Son of Denver Nuggets head Coach George Karl put up nice numbers (17 ppg, 5 rebs, 4 assists, 39 5% 3P) in the underrated WAC conference  Had surgery in March to remove a cancerous lump from his thyroid 
Mark Konecny, 6-10, Center, Lambuth (NAIA) Junior No Undrafted Transfer from Syracuse with mediocre production is looking for any type of exposure he can get before he graduates next season 
Kyle Lowry, 6-1, PG, Villanova Sophomore No First round pick NCAA tournament performance showed that he definitely needs another year, but regardless, Lowry is in  For now its without an agent  Considering the lack of quality point guard prospects in this draft, Lowry is likely a first round pick  Says he will attend the Orlando pre-draft camp if invited 
Aleks Maric, 6-11, Center, Nebraska Sophomore No Undrafted As exclusively reported by DraftExpress, Maric will be testing the waters  What may have played a role in this is the fact that the assistant coach that recruited him at Nebraska, Scott Spinelli, just moved on to Wichita State  Maric is considered a very average athlete who is still very raw and is therefore likely to go undrafted should he decide to stay in  Thanks to his Croatian passport, there is money waiting for him overseas if he chooses to take it 
Japhet McNeil, 5-10, PG, East Carolina Junior No Undrafted Severely undersized PG averaged 4 points and 5 6 assists in watered down Conference USA 
Paul Millsap, 6-8, PF, Louisiana Tech Junior Yes First round pick? As expected, Millsap has declared his intentions to enter the NBA draft, and according to sources hired an agent as well  Millsap has likely achieved just about everything he can in college at this point, and will land somewhere in the 20-40 part of the draft depending on workouts and measurements 
Matt Mitchell, 6-0, PG, Southern University-New Orleans Junior No Undrafted Anonymous NAIA player 
Adam Morrison, 6-8, SF, Gonzaga Junior Yes Top 5 pick As DraftExpress exclusively reported that Morrison will be declaring for the draft and hiring Chicago based agent Mark Bartelstein  Morrison, the top scorer in college basketball, is expected to be a top 5 pick and potentially the #1 pick overall  Questions linger about his athleticism and defense, but no one questions his passion, talent or feel for the game 
Patrick O'Bryant, 7-0, Center, Bradley Sophomore Likely First round pick NBA sources in Portsmouth told DraftExpress exclusively that OBryant will be testing the waters without an agent, but is likely to go all the way once he hears that hes a lock for the 1st round  His steady improvement, strong sophomore season, outstanding NCAA tournament and considerable upside means hes probably gone  O'Bryant since confirmed both DraftExpresss reports, particularly the one about hiring an agent in the Tri-State area (Andy Miller) should he decide to go all the way 
Evan Patterson, 6-7, SF, Texas Wesleyan Junior No Undrafted Mediocre numbers (11 ppg, 2 rebs) in a mediocre Southland conference 
Danilo Pinnock, 6-5, SG, George Washington Junior No Undrafted The extremely athletic Pinnock has told GWs student paper hell be testing the waters  Pinnock will attempt to capitalize on his teams success this year by potentially attending the NBA pre-draft camp in Orlando  Pinnock will have to show better ball-handling and perimeter shooting ability than he did during the regular season 
Leon Powe, 6-7, PF, Cal Sophomore No Second round pick Powe announced hell be testing the waters in a statement released by Cal  Where he ends up being projected depends heavily on how his knee checks out  Powe is already considered a serious tweener by NBA scouts, and had a hard time this season gaining back much of the explosiveness he had earlier in his career  Could realistically go undrafted should he decide to stay in 
Richard Roby, 6-5, SG, Colorado Sophomore Likely Second round pick As first indicated by DraftExpress Roby has decided to test the waters  Disappeared against any major competition he went up against, particularly towards the end of the season  Roby will likely have to put on weight in the next few months and show off his perimeter stroke in the Orlando pre-draft camp  Sources tell us that he is on the verge of making a huge mistake by hiring an agent



ground_truth: γh.

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: In which electorate was Simon English elected to the New Zealand Parliament?

Read the following text and answer briefly.

Context 2: 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods



ground_truth: The Wallace electorate.

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 underclassmen are on the NBA Draft Early-Entry List?

Read the following text and answer briefly.

Context 3: 
Rajon Rondo, 6-2, PG, Kentucky Sophomore Yes First round pick As expected, Rondo has decided to enter the NBA draft, and has also hired an agent, Bill Duffy  Despite an inconsistent sophomore season, most scouts weve spoken to still had him as at least the #2 point guard on their board because of his intriguing upside  Workouts will be huge for him 
Blake Schilb, 6-7, SG/SF, Loyola Chicago Junior No Undrafted Declared his intentions to enter the draft, without an agent, and is hoping for an invite to Orlando  Schlib is sorely lacking in the quickness and explosiveness departments that scouts demand from swingman prospects, but he makes up for it with his skill set to a certain extent  Regardless, sources tell us he wont be invited to Orlando, meaning he has to go back to school 
Mustafa Shakur, 6-4, PG, Arizona Junior No Second round pick? According to the Arizona Star, Shakur will likely enter his name in the draft, without an agent  Lute Olson confirmed it, saying he is not concerned about it  Shakur is hoping for an Orlando invite to show what he thinks he couldnt at Point Guard U 
Cedric Simmons, 6-9, PF/C, NC State Sophomore No First round pick? Simmons is reportedly "exploring his options," in regards to the 2006 NBA draft, but will do so without an agent  Nice size, frame, length, athleticism and defensive skills make him a very intriguing prospect 
Marcus Slaughter, 6-8, PF, San Diego State Junior Yes Second round pick? After burning his lone draft card a year early last June, despite being considered a marginal prospect, Slaughter has announced that he will be hiring agent Dan Fegan and forfeiting his remaining college eligibility  Slaughters father thinks that There was nothing else for Marcus to do at San Diego State  Many would disagree with that 
Curtis Stinson, 6-3, PG/SG, Iowa State Junior Yes Second round pick After swearing up and down last month that he has no intention on entering the draft, Stinson did just that  His coach Wayne Morgan, who he was very close to, was fired, resulting in him hiring agent Kevin Bradbury  The 23 year old combo guard will have to go to the Orlando pre-draft camp and impress if he wants to come close to being a 1st rounder 
Tyrus Thomas, 6-9, PF, LSU Freshman Yes Top 5 pick As DraftExpress exclusively reported Thomas called a press conference to announce his intentions to enter the 2006 NBA draft, as well as hire agents Brian Elfus and Mike Siegel  SEC Freshman of the year could be the most athletic player in the draft, as well as the player with the most overall upside 
PJ Tucker, 6-5, SF, Texas Junior No Second round pick As reported all year long by DraftExpress, Tucker will be entering the draft without an agent  Considering that hes a 6-5 combo forward with tremendous skills, his stock widely fluctuates depending on who is being asked  Phenomenal basketball player, but is severely lacking in 2-3 inches of height  Will likely need a strong showing at the Orlando pre-draft camp to have a legitimate shot at the 1st round  Some scouts compare him to Bonzi Wells 
Junior No Undrafted Undersized Division II post player has no chance of being drafted despite 20+8 averages 
Ian Vouyoukas, 6-10, Center, St  Louis Junior ??? Undrafted Vouyoukas declared his intentions to enter the draft, supposedly without an agent  Sources in Europe tell us he is likely to return to Greece to take a large contract offer from a first division team once he realizes he has no chance of being drafted  Vouyoukas is a nice mid-major big man who has improved somewhat in his junior season, but does not possess the necessary combination of athleticism and size required of an NBA center 
Darius Washington, 6-2, PG, Memphis Sophomore Likely First round pick? DraftExpress exclusively reported that Washington will be in the draft  It appears that hell be hiring an agent as well, despite not being anywhere near a lock for the first round 
Albert Weber, 6-3, SG, Connors State Sophomore No Undrafted Transfer from Alabama led his conference in scoring and is considered one of the top Junior College players in the country  Not officialy an NCAA player yet, and has not committed to any school yet, so really doesn't stand much to lose (or gain) from this move 
Marcus Williams, 6-3, PG, UConn Junior Yes Late Lottery-Mid-First As expected, Williams is expected to announce that hes hired Calvin Andrews of BDA Sports Management as his agent at a press conference next week  A strong junior season and outstanding NCAA tournament, establishing himself as one of the purest playmakers in the nation, means hes likely one of the first PGs taken 
Andriy Agafonov, 6-8, PF, Khimik 1986 Ukraine Undrafted Ukrainian power forward played 15 minutes and scored 6 points with 4 4 rebounds per game playing for FIBA EuroCup participants, and is declaring in hopes of getting his name out as he has one more draft card to burn after this before becoming automatically eligible 
Nemanja Aleksandrov, 7-0, SF/PF, KK Reflex 1987 Serbia & Montenegro ??? American agent has been telling us all year that hes likely to enter  Still hasnt played a game this year after taking slow recovery process from torn ACL  Once regarded as a prodigy and potential #1 overall pick, but injuries mean he hasnt played in nearly two years and is now considered damaged goods  Might just look for an attractive team to guarantee him in the 2nd round and develop him in the NBDL 
Pape-Philippe Amagou, 6-1, PG, Le Mans 1985 France ??? Amagous American agent has informed us that he will enter the NBA Draft this year, and participate in the Reebok Eurocamp in Treviso  Shares playmaking duties and spotlight with fellow early-entrant Yannick Bokolo 
Andrea Bargnani, 7-0, PF, Benetton Treviso 1985 Italy Top 5 pick Bargnani's Italian agent Stefano Meller told DraftExpress in Portsmouth that the Italian star power forward will definitely be entering the NBA draft  Bargnani is in the process of hiring an American agent and the only question is how long will it take for him to make it over to the US after Benetton finishes up in the Italian playoffs, which could last as far as mid-June  He is expected to be a top 5 pick with a shot at going #1 depending on how the lottery plays out  Considered a phenomenal talent thanks to his excellent size, perimeter skills and athleticism relative to height 
Yannick Bokolo, 6-3, PG/SG, Le Mans 1985 France ??? Terrific athlete who is still making the transition to playing the point full time 
Carlos Cedeno, 6-5, SG, Guaiqueries 1985 Venezuela Undrafted Relatively unknown Venezuelan player  Has some international experience at the junior levels 
Tadija Dragicevic, 6-8, PF, Red Star Belgrade 1986 Serbia & Montenegro Undrafted Undersized power forward barely played in the Adriatic League this past season 
Lior Eliyahu, 6-9, SF/PF, Galil Elyon 1985 Israel Second round pick? Prolific and athletic Israeli combo forward will be entering the NBA draft this year looking for certain guarantees from an NBA team in the 1st or 2nd round  Eliyahu is still in the Israeli army and will stay overseas for another year regardless of what happens  He'll be represented by the American agency Entersport in the United States  A midseason injury set him back from being the top Israeli player in the league despite his youth 
Rudy Fernández, 6-5, SG, DKV Joventut 1985 Spain First round pick? Has some minor buyout issues to deal with to make sure he can stay in the draft  Excellent season in Spain has him projected as a pretty solid first round pick  Improved outside shooting, and still the same excellent athlete, passer, defender and all-around player hes always been  Still very skinny too 
Kyrylo Fesenko, 6-11, PF, Azovmash 1986 Ukraine Second Round Pick More to come 
Rafael Hettsheimeir, 6-9, Center, Akasvayu Girona 1986 Brazil Undrafted Undersized Brazilian center did not overly impress at the Nike Hoop Summit, showing that he will likely lack mobility until he takes off some weight 
Marko Lekic, 6-11, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein, Lekic told us hell be putting his name in the draft this year once again  Still a bit of an unknown, numbers are fairly average in the Serbian YUBA league 
Damir Markota, 6-11, SF/PF, Cibona Zagreb 1985 Croatia Second round pick American agent Marc Cornstein told us Markota will definitely be putting his name in the draft once again  He had a breakout season in the Euroleague and Adriatic league before a groin injury slowed him down and eventually forced him to have minor surgery  Likely wont be able to come to the States until very late in the process  Does not have a buyout 
Mickael Mokongo, 5-11, PG, Chalon 1986 France ??? DraftExpress was exclusively informed hell be in the draft  Considered a talented athlete, but lack of size and the fact that he missed a large chunk of the season due to injury means his draft stock is very much up in the air still 
Brad Newley, 6-6, SG, 1985 Australia Second round pick Newely has told the Australian media that hes entering the draft  Hired Philadelphia based agent Leon Rose  Scouts who saw him play in Argentina last summer like his athleticism  Desperately lacking exposure, but agent appears to be unwilling to provide him with it 
Oleksiy Pecherov, 6-11, PF, Racing Basket 1985 Ukraine Second round pick DraftExpress received indication that Pecherov will be entering his name in the draft after a nice 2nd half regular season in France  Pecherov has his draft card in hand one year before he becomes automatically eligible, meaning he has nothing to lose  Has some nice skills facing the basket, but is still very soft and underdeveloped 
Hrvoje Peric, 6-8, SF, KK Split 1985 Croatia Second round pick? Good athlete who is still coming into his own as a basketball player  Did not play in the Adriatic League this season  Definitely needs at least another year in Europe, but could use the exposure that declaring for the draft provides 
Kosta Perovic, 7-2, Center, Partizan 1985 Serbia & Montenegro Undrafted? DraftExpress has been told that Partizan needs Perovic to be drafted this year to relieve them of his 500,000$ salary next year as well as help them financially with buyout money for their budget  Unfortunately this is happening about 3 years too late as weve seen little to no improvement from Perovic over that span 
Georgios Printezis, 6-9, PF, Olympiakos 1985 Greece Undrafted Greek power forward played 9 minutes and scored 4 points per game playing for a Euroleague team, and is declaring in hopes of getting his name out before he becomes automatically eligible next year 
Milovan Rakovic, 6-10, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein told us Rakovic will be putting his name in the draft  Still an unknown player, puts up nice numbers on occasion in the fairly weak Serbian YUBA league 
Alexandr Rindin, 7-5, Center, Gala Baku 1985 Azerbaijan Undrafted Huge body, complete unknown  5 points, 5 rebounds per game in FIBA Europe Cup 
Sergio Rodríguez, 6-3, PG, Estudiantes 1986 Spain First round pick Rodríguezs agent in the States told DraftExpress exclusively hell be in the draft, likely for good if he gets a commitment in the 1st round  A disappointing start to his season both in Spain and the ULEB cup made this European prodigy point guard fall on most teams draft boards, but Rodríguez picked things up substantially towards the end of the year and is now playing terrific basketball  Weak NCAA PG crop could put him in the lottery with good workouts 
Dusan Sakota, 6-10, SF/PF, Panathinaikos 1986 Greece Undrafted Fairly unathletic perimeter oriented big man was in the draft last year already  Plays for one of the best teams in Europe and rarely sees the floor for meaningful minutes 
Renaldas Seibutis 6-5, SG, Olympiakos 1985 Lithuania Undrafted One of the most productive players in Europe in his age group considering the level he plays at  Important cog on an excellent team, but lacks athleticism and isnt as good of a shooter as you would hope at this point in his career 
Saer Sene, 7-0, Center, Pepinster 1986? Senegal First round pick? Freakishly long and athletic African prospect who played extremely well at the Nike Hoop Summit  Many question his age and lack of productivity in the very average Belgian league A player teams will want to look at closely 
Sidiki Sidibe, 7-1, Center, Levallois 1985 France ??? 7-1, 265 pound volleyball player and former Kansas State commit will be in this years draft according to his American agent  Too raw to get any playing time whatsoever in French 2nd division 
Tiago Splitter, 7-0, PF/C, Tau Vitoria 1985 Brazil Lottery pick Splitters American agent Herb Rudoy told DraftExpress exclusively hes entering the draft Splitter is having a terrific season in both the ACB Spanish League and the Euroleague, but lack of buyout in his contract means he might not be able to stay in  CBA rules allow him to withdraw and become automatically eligible next season  Tau Vitorias president was quoted saying Splitter will be back in Spain next season 
Sun Yue, 6-9, PG/SF, Aoshen 1985 China Second round pick? Super talented tall point guard with decent athleticism and nice defensive skills  Lacks strength and outside shooting ability  Level of competition is mediocre in American semi-pro ABA league, which makes him an intriguing candidate for Orlando pre-draft camp 
Ali Traore, 6-9, PF, Roanne 1985 France ??? Puts up nice numbers in France  Will participate at the Reebok Eurocamp in Treviso 
Ejike Ugboaja, 6-8, PF, Union Bank Lagos 1985 Nigeria Undrafted Plays for Nigerian National Team 
Goran Dragic, 6-4, PG, Geoplin Slovan 1986 Agent initially notified us that Dragic will be entering the draft, but in the end decided to keep him out  His buyout was always a question mark 
Leigh Enobakhare, 6-10, Center, Oostende 1986 Agent Ugo Udezue from BDA Sports Management told us that Enobakhare will be entering the draft  In the end he must have heard that he is not considered a prospect at all, and decided to keep him out of the draft 
Cartier Martin, 6-8, SF/PF, Kansas State Junior Martin pondered entering his name in the draft, especially after the firing of Kansas State coach Jim Wooldridge 
Nick Young, 6-6, SG, USC Sophomore Young told the LA Daily News in February that hes staying at USC for another year 
D J  Strawberry, 6-5, SG/SF, Maryland Junior Strawberry initially intended to test the waters, but eventually ended up not doing so once he found out that his chances of being drafted are almost non-existent 
Al Thornton, 6-7, SF/PF, Florida State Sophomore Implied earlier on in the year that he might put his name in, but sources recently told us it appears that he will return for his senior year  Tallahassee media backs this up 
Marcus Williams (AZ), 6-8, SG/SF, Arizona Freshman After initially appearing to be gone after numerous definitive reports, Williams surprised everyone and thrilled Arizona fans by announcing in a press conference hell be returning for his sophomore year 
Josh McRoberts, 6-11, PF, Duke Freshman After being upset by LSU in the Sweet Sixteen, McRoberts was quoted saying Ill be at Duke next year   Duke issued a press release a month later confirming this 
Yi Jianlian, 7-0, PF, Guangdong 1987? International Jianlian announced in a press conference that hell be staying in China  A CBA official was also quoted on this matter, sounding as if they were the main factor for him staying put 
Acie Law, 6-3, PG, Texas A&M Junior After a fantastic showing in the NCAA tournament, Law helped his NBA draft stock considerably but will return for his senior year where A&M is expected to make a run at possibly winning the Big 12 
Joakim Noah, 6-11, PF/C, Florida Sophomore Huge 2nd half regular of the regular season and NCAA tournament boosted his stock into as high as the top 5  Noah came out and said afterwards hes staying regardless 
Al Horford, 6-9, PF, Florida Sophomore Horford indicated all season long that hes staying at least one more year, but playing extremely well in winning the national championship gave him a realistic chance at being a lottery pick  Regardless, Horford announced he'll return 
Corey Brewer, 6-8, SF, Florida Sophomore Brewer indicated all season long that hes staying at least one more year, but a terrific performance in the NCAA tournament gave him a realistic chance at being a top 20 pick  Regardless, Brewer announced he'll return 
Glen Davis, 6-8, Center, LSU Sophomore Davis announced hell be returning to LSU immediately after an absolutely horrendous showing in the Final Four which exposed all of his glaring weaknesses  Made it official as an LSU press conference alongside Tyrus Thomas 
Jason Smith, 7-0, PF/C, Colorado State Sophomore Smith announced that hes returning for his junior year, stating that "a little further down the road, it [the NBA] might be in my plans  I'm continuing to concentrate on my academics and see how I can help CSU as much as possible "
Jermareo Davidson, 6-10, PF, Alabama Junior > After burning his lone draft card a year early last June, Davidson considered entering the draft again, but eventually made the right decision in announcing hell be returning for his senior year 
Richard Hendrix, 6-8, PF, Alabama Freshman Told Alabama media after NCAA tournament loss that hell be back in Tuscaloosa next year 
Ja'Vance Coleman, 6-3, SG, Fresno State Junior Testing the waters according to the Fresno Bee  Whoops, no hes not 
Sean Singletary, 5-11, PG, Virginia Sophomore Singletary told The Daily Progress in early February that hes returning 



ground_truth: 62.