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Laurent Leksell and Kullervo Hynynen Will be Featured Speakers at 2nd International MR-guided FUS Symposium
Two pioneers in the field of noninvasive image-guided treatments – one an internationally-known entrepreneur and business executive, the other a physicist and world-class academic researcher – will be among the featured speakers at the 2nd International Symposium on MR-guided Focused Ultrasound in Washington, D.C. from October 17-20, 2010.
The Focused Ultrasound Surgery Foundation has announced that Laurent Leksell, co-founder and long-time President and CEO of Gamma Knife-maker Elekta AB, will deliver the symposium's keynote Batten Family Lecture. Based in Sweden, he currently serves as an executive member of Elekta's board and is a member of its executive committee for the Asia Pacific Region.
Leksell founded Elekta in 1972 with his father, the late Professor Lars Leksell. Over a 30-year period, he led the company's transformation from a small privately-held firm into an international medical technology enterprise that now has 2,500 employees and subsidiaries in 18 countries. In 2007, Leksell was named Swedish Entrepreneur of the Year. He holds a PhD in Economics from Handelshogskolan i Stockholm as well as an MBA.
In addition to his role as symposium president, Kullervo H. Hynynen, PhD, will present the David and Diane Heller Lecture covering the past, present and future of focused ultrasound technology. Hynynen is a distinguished physicist and a pioneer in the surgical and therapeutic use of focused ultrasound. He holds appointments as Canada Research Chair in Imaging Systems and Image-Guided Therapy (Tier 1), as Professor, Department of Medical Biophysics, University of Toronto and as Director and Senior Scientist, Discipline of Imaging Research and Director of the Centre for Research in Image-Guided Therapeutics, Sunnybrook Health Sciences Centre, Toronto.
One of the most published and cited researchers in the field, Hynynen has investigated the use of focused ultrasound for noninvasive surgery, vascular surgery, targeted drug delivery and gene therapy. Currently, he is exploring new ultrasound imaging methods for therapy delivery and tumor diagnosis.
Written by Ellen C., McKenna
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\section{Introduction}
Game theory provides a universal framework for modeling interactions among cooperative and competitive players~\cite{shoham2008multiagent}. The canonical solution concept is Nash equilibrium (NE), describing a situation when no player increases their utility by unilaterally deviating. However, computing NE in two-player or multi-player general-sum games is PPAD-complete~\cite{daskalakis2006complexity,chen2006settling}, which makes solving games both exactly and approximately difficult. The situation remains non-trivial even in two-player zero-sum games, no matter whether the players may perceive the state of the game perfectly (e.g., in Go~\cite{silver2016mastering}) or imperfectly (e.g., in poker~\cite{brown2018superhuman} or StarCraft II~\cite{vinyals2019grandmaster}).
In recent years, learning algorithms have demonstrated their superiority in
solving large-scale games over traditional optimization methods, including linear or nonlinear programs. The most successful learning algorithms belong either to the line of research on counterfactual regret minimization (CFR)~\cite{brown2018superhuman}, or policy space response oracle (PSRO)~\cite{lanctot2017unified}. CFR is an iterative algorithm approximating Nash equilibrium using repeated self-play.
Several sampling-based CFR variants~\cite{lanctot2009monte,gibson2012generalized} were proposed to solve large games efficiently. To scale up to even larger games, CFR could be embedded with neural network function approximation~\cite{brown2019deep,steinberger2019single,li2019double}. The other algorithm, PSRO, generalizes the double oracle method~\cite{mcmahan2003planning,bosansky2014exact} by incorporating (deep) reinforcement learning (RL) methods as a best-response oracle~\cite{lanctot2017unified,muller2019generalized}. The neural fictitious self-play can be seen as a special case of PSRO~\cite{heinrich2015fictitious}. Both CFR and PSRO achieved great performance in solving large-scale imperfect-information games, e.g., poker~\cite{brown2018superhuman,heinrich2015fictitious}.

One of the critical components of these successes is the existence of \emph{efficient and accurate simulators}. A simulator serves as an environment that allows the agent to collect millions to billions of trajectories for the training process.
The simulator may be encoded using rules as in different poker variants~\cite{lanctot2019openspiel}, or a video-game suite like StarCraft II~\cite{vinyals2017starcraft}.
However, in many real-world games such as football~\cite{kurach2020google,tuyls2021game} or table tennis~\cite{ji2021model}, constructing a sufficiently accurate simulator may not be feasible because of a plethora of complex factors affecting the game-play. These factors include the relevant laws of physics, environmental circumstances (e.g., a wind speed), or physiological limits of (human) bodies rendering certain actions unattainable. Therefore, the football teams or the table tennis players may resort to watching previous matches to alter their strategies, which semantically corresponds to \emph{offline equilibrium finding} (OEF).
Recent years have witnessed several (often domain-specific) attempts to formalize offline learning in the context of games. For example, the StarCraft II Unplugged~\cite{mathieu2021starcraft} offers a dataset of human game-plays in this two-player zero-sum symmetric game. A concurrent work~\cite{cui2022offline} investigates the necessary properties of offline datasets of two-player zero-sum games to successfully infer their NEs. However, neither work considers the significantly more challenging field of multi-player games. In this paper, we provide benchmark algorithms that can solve two-player and multi-player games based on the model-based and the behavior cloning methods.

Because of the lack of a common ground in terms of datasets and basic algorithmic benchmarks, the contemporary research on OEF remains fragmented and limited. Our intention in this work is to fill the gap by introducing carefully curated data and a simple benchmark corroborating with prior conceptions about game-theoretic offline reinforcement learning. More specifically, our main contributions include: i) constructing OEF datasets collected from widely accepted game domains and established behavior-generating methods; ii) generalizing two most successful online equilibrium finding algorithms, Deep CFR and PSRO, to the offline setting by incorporating model-based methods; iii) providing a comprehensive OEF benchmark combining a behavior cloning algorithm and a model-based algorithm; and iv) evaluating the benchmark method on the constructed offline datasets. The experimental results substantiate our claims that the benchmark method is able to attain reasonable results on a wide spectrum of multi-player games.

\section{Rationale Behind Offline Equilibrium Finding}
We begin by providing a motivating scenario of OEF and comparing it to two related lines of research -- opponent modeling and empirical game theoretic analysis -- in order to highlight the rationale behind the introduction of OEF. We then justify the choice of model-based methods for OEF. We provide a more detailed overview of related works in Appendix~A.

\textbf{Motivating scenario.}  Assume that a table tennis player $A$ will play against player $B$ whom they never faced before. What may $A$ do to prepare for the match? In this case, even though $A$ knows the rules of table tennis, they remain unaware of specific circumstances of playing against $B$, such as which moves or actions $B$ prefers or their subjective payoff function.
Without these detailed game information, self-play or other equilibrium finding algorithms cannot work. Therefore, $A$ has to \emph{watch the matches that player $B$ played against other players} to learn their style and compute the equilibrium strategy, which minimizes exploitation. This process corresponds to the proposed OEF methodology.

\begin{wrapfigure}{l}{0.385\textwidth}
\centering
\includegraphics[width=0.385\textwidth]{figures/Play_Ping_Pong.png}
\caption{The game of table tennis.}
\label{fig:motivation}
\end{wrapfigure}

\textbf{Why offline equilibrium finding?} In games with complex dynamics like table tennis games or football games~\cite{kurach2020google}, it is difficult to build a realistic simulator or learn the policy during playing the game. A remedy is to learn the policy from the historical game data. Therefore, we propose OEF as a process of \emph{finding the NE strategy of the underlying game, given a fixed dataset collected by an unknown exploration strategy}. The OEF is similar to Offline RL but poses several unique challenges: i) the canonical solution concept is the game-theoretic mixed Nash equilibrium, which requires an iterative procedure of computing responses; ii) the games have at least two players who play against each other, which increases sensitivity to distribution shift and other uncertainties when compared with traditional Offline RL; and iii) the distribution shifts of the opponents' actions and the game are coupled, which brings difficulties to distinguish and address them.

\textbf{Why Opponent Modeling is not enough?} Opponent modeling is used to predict the opponents' behavior strategies, in both single and multi-agent reinforcement learning~\cite{he2016opponent}. However, just predicting the opponents' behaviors is not enough because the opponents in OEF are not fixed but always best responding to the agent's strategy. In this work, we apply imitation learning, which is also used in opponent modeling, to learn the behavior cloning policy, and the experimental results show that simply applying the behavior cloning policy is not enough.

\textbf{Why Empirical Game Theoretic Analysis (EGTA) cannot work?} The EGTA was proposed to reduce the complexity of large economic systems in electronic commerce~\cite{wellman2006methods}. It evolved into two main directions: strategic reasoning for simulation-based games~\cite{wellman2006methods} and evolutionary dynamical analysis of agent behavior inspired by evolutionary game theory~\cite{tuyls2018generalised}. The basic idea of EGTA is to use a sampled strategy to play the game and estimate the empirical game from the simulation's results. This should consequently provide insights into the structure of the original game. PSRO~\cite{lanctot2017unified} is thus a specific case of EGTA. The primary difference between EGTA's setting and OEF is that in OEF the strategy used to generate the offline data remains unknown, precluding EGTA from being used for OEF.

\textbf{Why model-based methods?}
In traditional Offline RL, the data of two actions may be used to determine which action is better~\cite{levine2020offline}. In contrast, the data of two action tuples in OEF are not enough to decide which tuple is closer to an NE strategy as NE identification requires other action tuples to serve as references \cite{cui2022offline}. For computing NE, we hence need to be able to evaluate any action tuple, and the data of some may be missing from the dataset. It prevents us from learning using model-free methods from the dataset directly. Therefore, we have to resort to simulating the game dynamics by a model to give the agents their utilities.
In this context, \emph{model-based methods, rather than other model-free methods}, are arguably the most straightforward choices for OEF. Unfortunately, most known model-based methods for single-agent reinforcement learning cannot be directly applied to games for equilibrium finding. The main reason is their inherent reliance on the existence of no strategic opponents in the environment. Though MuZero~\cite{schrittwieser2020mastering} can be applied to symmetric two-player zero-sum games, e.g., Go, and solve the game with self-play, for heterogeneous players, it requires either a model of each agent separately or a model modelling all agents. Training these agents' models is challenging in OEF due to the limited game data. Therefore, in this work, we adapt two widely-used equilibrium finding algorithms -- Deep CFR and PSRO -- to the context of offline learning using an environment model trained from the dataset.

\section{Preliminaries}

\subsection{Imperfect-Information Games}
An imperfect-information game (IIG) ~\cite{shoham2008multiagent} is a tuple ($N, H, A, P, \mathcal{I}, u$), where $N = \{1,...,n\}$ is a set of players and $H$ is a set of histories (i.e., the possible action sequences). The empty sequence $\emptyset$ corresponds to a unique root node of game tree included in $H$, and every prefix of a sequence in $H$ is also in $H$. $Z \subset H$ is the set of the terminal histories. $A(h) = \{a:(h,a) \in H\}$ is the set of available actions at a non-terminal history $h \in H$. $P$ is the player function. $P(h)$ is the player who takes an action at the history $h$, i.e., $P(h) \mapsto N \cup \{c\}$. $c$ denotes the ``chance player'', which represents stochastic events outside of the players' control. If $P(h) = c$ then chance determines the action taken at history $h$. Information sets $\mathcal{I}_i$ form a partition over histories $h$ where player $i \in N$ takes action. Therefore, every information set $I_i \in \mathcal{I}_i$ corresponds to one decision point of player $i$ which means that $P(h_1) = P(h_2)$ and $A(h_1) = A(h_2)$ for any $h_1, h_2 \in I_i$. For convenience, we use $A(I_i)$ to represent the action set $A(h)$ and $P(I_i)$ to represent the player $P(h)$ for any $h \in I_i$. For $i \in N$, a utility function $u_i: Z \rightarrow \mathbb{R}$ specifies the payoff of player $i$ for every terminal history.

A player's behavioral strategy $\sigma_i$ is a function mapping every information set of player $i$ to a probability distribution over $A(I_{i})$ and $\Sigma_{i}$ is the set of strategies for player $i$. A strategy profile $\sigma$ is a tuple of strategies, one for each player, ($\sigma_1, \sigma_2, ..., \sigma_n$), with $\sigma_{-i}$ referring to all the strategies in $\sigma$ except $\sigma_i$. Let $\pi^{\sigma}(h) = \prod_{i \in N \cup \{c\}}\pi^{\sigma}_i(h)$ be the probability of history $h$ occuring if players choose actions according to $\sigma$. $\pi_i^{\sigma}(h)$ is the contribution of $i$ to this probability. Given a strategy profile $\sigma$, the value to player $i$ is the expected payoff of the resulting terminal node, $u_i(\sigma) = \sum_{z \in Z}\pi^\sigma(z)u_i(z)$.

The canonical solution concept is Nash Equilibrium (NE). The strategy profile $\sigma$ forms an NE
if $u_{i}(\sigma) \geq \max_{\sigma_{i}'\in\Sigma_i}u_{i}(\sigma_{i}', \sigma_{-i}), \forall i\in N$.
To measure of the distance between $\sigma$ and NE, we define $\textsc{NashConv}_{i}(\sigma)=\max_{\sigma_{i}'} u_{i}(\sigma_{i}', \sigma_{-i}) - u_{i}(\sigma)$ for each player and $\textsc{NashConv}(\sigma)=\sum_{i\in N}\textsc{NashConv}_{i}(\sigma)$. Other solution concepts such as (coarse) correlated equilibrium require different measures~\cite{marris2021multi}.
In this work, we only focus on NE solution concept.

\subsection{Equilibrium Finding Algorithms}
\textbf{PSRO.} PSRO is initialized with a set of randomly-generated policies $\hat{\Sigma}_{i}$ for each player $i$. At each iteration of PSRO, a meta-game $M$ is built with all existing policies of players and then a meta-solver computes a meta-strategy, i.e., distribution, over polices of each player (i.e., Nash, $\alpha$-rank or uniform distributions). The joint meta-strategy for all players is denoted as $\bm{\alpha}$, where $\alpha_{i}(\sigma)$ is the probability that player $i$ takes $\sigma$ as their strategy. After that, an oracle computes at least one policy for each player, which is added into $\hat{\Sigma}_{i}$. We note when computing the new policy for one player, all other players' policies and the meta-strategy are fixed, which corresponds to a single-player optimization problem and can be solved by DQN \cite{mnih2015human} or policy gradient reinforcement learning algorithms. NFSP can be seen as a special case of PSRO with uniform distributions as meta-strategies.

\textbf{CFR.} CFR is a family of iterative algorithms for approximately solving large imperfect-information games. Let $\sigma^t_i$ be the strategy used by player $i$ in round $t$. We define $u_i(\sigma, h)$ as the expected utility of player $i$ given that the history $h$ is reached and then all players play according to strategy $\sigma$ from that point on. Let us define $u_i(\sigma, h\cdot a)$ as the expected utility of player $i$ given that the history $h$ is reached and then all players play according to strategy $\sigma$ except player $i$ who selects action $a$ in history $h$. Formally, $u_i(\sigma,h)=\sum_{z \in Z}\pi^\sigma(h,z)u_i(z)$ and $u_i(\sigma, h\cdot a)=\sum_{z \in Z}\pi^\sigma(h \cdot a,z)u_i(z)$.
The \textit{counterfactual value} $v_i^\sigma(I)$ is the expected value of an information set $I$ given that player $i$ attempts to reach it. This value is the weighted average of the value of each history in an information set. The weight is proportional to the contribution of all players other than $i$ to reach each history. Thus, $v_i^\sigma(I) = \sum_{h\in I} \pi^\sigma_{-i}(h)\sum_{z \in Z}\pi^\sigma(h,z)u_i(z)$.
For any action $a \in A(I)$, the counterfactual value of action $a$ is $v_i^\sigma(I,a)= \sum_{h\in I} \pi^\sigma_{-i}(h)\sum_{z \in Z}\pi^\sigma(h \cdot a,z)u_i(z)$.
The \textit{instantaneous regret} for action $a$ in information set $I$ of iteration $t$ is $r^t(I,a)=v_{P(I)}^{\sigma^t}(I,a)-v_{P(I)}^{\sigma^t}(I)$. The \textit{counterfactual regret} for action $a$ in $I$ of iteration $T$ is $R^T(I,a) =\sum_{t=1}^{T}r^t(I,a)$.
In vanilla CFR, players use \textit{Regret Matching} to pick a distribution over actions in an information set proportional to the positive cumulative regret of those actions. Formally, in iteration $T+1$, player $i$ selects actions $a\in A(I)$ according to probabilities
\begin{equation}
\sigma^{T+1}(I,a)=
\begin{cases}
\frac{R^T_{+}(I,a)}{\sum_{b \in A(I)}R^T_{+}(I,b)} & \text{if $\sum\limits_{b \in A(I)}R^T_{+}(I,b) >0$} \\
\frac{1}{|A(I)|}& \text{otherwise,}
\end{cases}\nonumber
\end{equation}
where $R^T_{+}(I,a)=\max\{R^T(I,a),0\}$ because we are concerned about the cumulative regret when it is positive only.
If a player acts according to regret matching in $I$ on every iteration, then in iteration $T$, $R^T(I) \leq \Delta_i\sqrt{|A_i|}\sqrt{T}$ where $\Delta_i= \max_z u_i(z)-\min_zu_i(z)$ is the range of utilities of player $i$. Moreover,
$R^T_i \leq \sum_{I\in \mathcal{I}_i}R^T(I) \leq |\mathcal{I}_i|\Delta_i\sqrt{|A_i|}\sqrt{T}$.
Therefore, $\lim_{T \rightarrow \infty}\frac{R^T_i}{T} = 0$. In two-player zero-sum games, if both players' average regret  $\frac{R^T_i}{T} \leq \epsilon$, their average strategies $(\overline{\sigma}^T_1,\overline{\sigma}^T_2)$ form a $2\epsilon$-equilibrium~\cite{waugh2009abstraction}. Most previous works focus on tabular CFR, where counterfactual values are stored in a table. Recent works adopt deep neural networks to approximate the counterfactual values and outperform their tabular counterparts~\cite{brown2019deep,steinberger2019single,li2019double,li2021cfr}. In this work, we adapt Deep CFR which uses neural networks to replace tabular representation to offline setting.

\section{Datasets for Offline Equilibrium Finding}
\label{sec:dataset}
In the offline setting, a fixed pre-collected dataset is provided, and the goal is to compute NE using the dataset only. In the real life, offline datasets may be collected using different behavior strategies. In this paper, to simulate real-world cases, we focus on the following three types of datasets.

\textbf{Random dataset.} This type of dataset is collected using a random strategy. The motivation behind this is that when we do not know how to play a game, we may try to explore it randomly. The process of collecting is akin to that of a beginner familiarizing themselves with the game for the first time. The dataset is collected in three steps. First, we assume that every player plays a uniform strategy. Second, the players engage in the game repeatedly. Finally, we collect the generated game data. Note that in imperfect-information extensive-form games, the definition of information sets corresponds to the decision points of players. Here, we take the information sets of all players in the game as a game state. Therefore, the game data include the transition information such as current game state $s_t = (I^t_1, I^t_2, ..., I^t_n)$, action $a$, next game state $s_{t+1} = (I^{t+1}_1, I^{t+1}_2, ..., I^{t+1}_n)$, and other game information, e.g., whether the game ends and players' rewards $r_{t+1} = (r^{t+1}_1, r_2^{t+1}, ..., r_n^{t+1})$.

\textbf{Learning dataset.} This type of dataset is collected when learning the Nash equilibrium strategy. When solving a game using some existing equilibrium finding algorithm, the players have to interact with the game environment. During the course of learning, we may gather these intermediate interaction game data and store them as a learning dataset. In contrast to the random dataset, the players' strategies gradually improve as we get closer to Nash equilibrium.

\textbf{Expert dataset.} This type of dataset is collected using an NE strategy. The motivation behind the dataset is that when learning a game, we often prefer to observe more experienced players at play. We simulate the expert players using the NE strategy and collect the interaction data. We follow a similar methodology as with the random dataset. First, we compute the NE strategies using any existing equilibrium finding algorithm. As a second step, the Nashian players repeatedly interact in the game. Finally, we gather the generated data and store them as the expert dataset.

In addition to the three types of datasets mentioned above, we also consider hybrid datasets consisting of random and expert interactions mixed in different ratios to simulate offline datasets generated by unknown strategies. We collect the data from player engagements in the most frequently used benchmarking imperfect-information extensive-form games in contemporary research on equilibrium finding. These games include poker games (two-player and multi-player Kuhn poker, two-player and multi-player Leduc poker), Phantom Tic-Tac-Toe, and Liar's Dice.

\section{Benchmark Algorithms for Offline Equilibrium Finding}

\begin{wrapfigure}{r}{0.4\textwidth}
\vspace{-15pt}
\begin{center}
\includegraphics[width=0.4\textwidth]{figures/oef_flow.pdf}
\end{center}
\caption{The flow of OEF algorithms. }\label{fig:oef_flow}
\vspace{-5pt}
\end{wrapfigure}

Here, we propose a benchmark algorithm for OEF, a combination of the behavior cloning (BC) and model-based (MB) methods. Note that BC and MB are obvious choices for OEF. However, both BC and MB methods exhibit various flaws under different circumstances. Therefore, to avoid their drawbacks, we combine these two methods by assigning different weights to the trained policies using these two methods (see the experiments part for details).

Figure~\ref{fig:oef_flow} and Algorithm \ref{oef} show the general procedure of the OEF benchmark. We first learn a BC policy from the dataset with a behavior cloning algorithm and a model with model-based methods. Then we learn a model-based policy (MB policy) from the model. We finally combine the two policies, i.e., the BC policy and the MB policy, by assigning proper weights to these two policies to obtain the final policy under different cases. We leave two unexplored options to future research: i) whether we can learn an MB policy with the regularization of the BC policy, as well as interacting with the dataset, and ii) if we can use the learned model to get the proper weights when combining the two policies.
\subsection{Behavior Cloning}
\begin{algorithm}[t]
\label{oef}
\caption{General Framework of Offline Equilibrium Finding}
Given the dataset $D$\;
Based on $D$, learn a behavior policy $\bm{\pi}^{bc}$ with BC and learn an environment model $E$\;
Learn an equilibrium policy $\bm{\pi}^{mb}$ on $E$ using OEF-PSRO or OEF-CFR\;
Combine $\bm{\pi}^{bc}$ and $\bm{\pi}^{mb}$ with appropriate weights $\alpha$ to get final policy $\bm{\pi}$
\end{algorithm}
Perhaps the most straightforward way to obtain a good strategy based only on the offline dataset is through behavior cloning -- a method that mimics the behavior policy in the dataset. Behavior cloning is used frequently in Offline RL~\cite{fujimoto2021minimalist}. In our case, we can also use the behavior cloning technique in OEF by learning a behavior cloning strategy of every player from the offline dataset by an imitation learning method. More specifically, we can use imitation learning to train a policy network $\sigma_i$ parameterized by $\theta$ for every player $i$ to predict the strategy given any information set $I_i$. Note that only the information sets and actions data are needed when training the behavior cloning strategy. We employ the cross-entropy loss as behavior cloning training loss, defined as
\begin{align}
    \mathcal{L}_{bc} = \mathbb{E}_{(I_i, a) \sim D} [l(a, \sigma_i(I_i;\theta))] = \mathbb{E}_{(I_i, a) \sim D} [- \sum\nolimits_{i = 1}^K a \cdot \log(\sigma_i(I_i;\theta))],
\end{align}
where $K$ is the number of actions. Figure \ref{fig:bc_model} shows the structure of the behavior cloning policy network. Because equilibrial strategies in most information sets are non-trivial probability distributions, we apply a softmax layer after the output layer to obtain the final mixed strategy.

\begin{figure}[ht]
\centering
\subfigure[Behavior Cloning policy network Structure]{\includegraphics[width=0.48\textwidth]{figures/bc_model.pdf}
\label{fig:bc_model}}
\subfigure[Environment model structure]{\includegraphics[width=0.48\textwidth]{figures/mb_model.pdf}
\label{fig:mb_model}}
\caption{Structure of the neural networks used in the Behavior Cloning and Model-based methods.}
\end{figure}

\subsection{Environment Model}
The ultimate goal of OEF is to learn the approximate NE based on the offline dataset. However, if we directly use the data to compute the NE strategy, we may not be able to evaluate the game states not included in the dataset. One possibility to deal with this issue is to train an intermediate environment model using the offline dataset to capture the game structure which is necessary for computing the equilibrium. In other words, the purpose of the trained environment model is to learn the dynamics of the game environment and output the next game state information based on the current game state and action.
Since deep neural networks have powerful generalization abilities, the neural network model may provide approximate results for game states not sufficiently captured in the data. Therefore, the environment model can offer enough game information for computing the NE.

Figure \ref{fig:mb_model} shows the structure of the environment model. The environment model $E$ parameterized by $\theta_e$ takes the game state $s_t$ and action $a$ as inputs and outputs the next game state $s_{t+1}$, rewards $r_{t+1}$, and other information such as the next legal action set and whether the game ends. We use stochastic gradient descent (SGD) optimizer to perform parameter updates when training the environment model. We may employ any loss function that satisfies the conditions of Bregman divergence \cite{banerjee2005clustering}, such as mean squared error loss as below:
\begin{align}
    \mathcal{L}_{env} = \mathbb{E}_{(s, a, s') \sim D} [MSE(s', E(s, a;\theta_e))] = \mathbb{E}_{(s, a, s') \sim D} [(s' -  E(s, a;\theta_e)))^2].
\end{align}

\subsection{OEF-PSRO and OEF-CFR}
After training the environment model, we aim to compute the Nash equilibrium strategy based on this model. To this end, we propose Offline Equilibrium Finding-Policy Response Oracles (OEF-PSRO) and Offline Equilibrium Finding-Deep CFR (OEF-CFR) algorithms by adapting PSRO and Deep CFR, which are two widely-used equilibrium-finding algorithms.

In PSRO, a meta-game is represented as an empirical game starting with a single policy (uniform random) and iteratively enlarging it by adding new policies (oracles) that approximate the best responses to the meta-strategies of other players. It is clear that when computing the best response policy oracle, interactions with the environment are required to gain game information. In OEF, only an offline dataset is provided, and directly applying PSRO is not feasible. In OEF-PSRO, we use the trained environment model to replace the actual environment to provide the game information. It is a common knowledge that when computing the best response policy using DQN or other reinforcement learning algorithms, the next state and reward based on the current state and the action are needed. Our environment model can provide such information. The model can also offer additional information for approximating the missing entries in the meta-game matrix using the same manner.

Deep CFR is a variant of CFR which uses neural networks to approximate counterfactual regret values and average strategies. During this approximation, the game tree has to be partially traversed to arrive at the counterfactual regret value. This process requires an environment to provide the necessary game information. Akin to OEF-PSRO, also in OEF-CFR, we use the trained environment model to replace the actual environment. During the traversal, the environment needs to identify the next game state and utility for the terminal game state, for which we may employ our trained environment model. These two algorithms are described in detail in Appendix B.
\section{Experiments}\label{sec:experiments}
Finally, in this section, we present the benchmark results of the OEF algorithms on the OEF datasets collected in various games. First, we evaluate the behavior cloning method and the model-based method on different datasets independently. Then, we move to assess the performance of our benchmark algorithms (BC+MB), a combination of both methods described in the previous section.

\subsection{Experimental Setting}\label{sec:experiments_setting}
OpenSpiel~\footnote{\url{https://github.com/deepmind/open_spiel}} is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games \cite{lanctot2019openspiel}. It is widely accepted and implements many different games. We use it as our experimental platform and select several poker games as experimental domains. We opt for Kuhn poker, Leduc poker, Phantom Tic-Tac-Toe, and Liar's Dice, which are all widely used in previous works~\cite{lisy2015online,brown2019solving}.
For every game, we first generate the three datasets introduced in Section \ref{sec:dataset}. Then we mix the random dataset and the expert dataset in different proportions to get hybrid datasets. Based on these hybrid datasets, we first train the behavior cloning strategies using imitation learning. Then we train the environment model by performing mini-batch stochastic gradient descent (SGD) iterations. After obtaining the environment model, we employ it in the OEF-PSRO and OEF-CFR algorithms to compute the Nash equilibrium strategy. Finally, we combine the BC policy and the MB policy to obtain the final policy. We use NashConv (exploitability) as the optimality measure.
All the experiments are performed on a server with a 10-core 3.3GHz Intel i9-9820X CPU and an NVIDIA RTX 2080 Ti GPU. All results are averaged over three seeds. Only selected results are shown here and the rest is deferred to Appendix C.

\subsection{Performance of the Behavior Cloning Method}

\begin{figure}[t]
\centering
\subfigure[Kuhn Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_2_players_bc_2.pdf}
\label{bc_1}}
\subfigure[Kuhn Poker-3 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_3_players_bc_2.pdf}}
\subfigure[Kuhn Poker-4 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_4_players_bc_2.pdf}}
\subfigure[Kuhn Poker-5 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_5_players_bc_2.pdf}
\label{bc_2}}
\subfigure[Kuhn Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_2_players_mb_2.pdf}
\label{mb_1}}
\subfigure[Kuhn Poker-3 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_3_players_mb_deep_cfr_2.pdf}}
\subfigure[Kuhn Poker-4 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_4_players_mb_deep_cfr_2.pdf}}
\subfigure[Kuhn Poker-5 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_5_players_mb_deep_cfr_2.pdf}
\label{mb_2}}
\caption{Experimental results of the BC method and the MB method.}
\label{fig:bc_results}
\end{figure}

We first evaluate the performance of the behavior cloning technique in the OEF. We apply the behavior cloning technique to learn the BC policy for every player on every hybrid dataset and compute the NashConv of these BC policies. Figures~\ref{bc_1}-\ref{bc_2} show the results of Kuhn pokers with a different number of players. The x-axis represents the proportion of random data in the hybrid dataset. If the proportion is zero, the mixed dataset equals the expert dataset. If the proportion is one, it indicates that the hybrid dataset is the random dataset. We can see that as the proportion of random dataset increases, the performance of BC policy decreases in all games. The finding is intuitive since the BC method only performs better on datasets containing more expert data. We also test the BC method in several datasets of different sizes. With the increase in the dataset size, the performance evolves more stable while the performance improvement is not very significant. Therefore, the performance of BC policy depends on the quality of datasets, i.e., the quality of the policy used to generate the dataset.

\subsection{Performance of the Model-based Methods}
Here, we evaluate the performance of the model-based algorithm in the OEF.
For every hybrid dataset, we first train an environment model. Then we perform OEF-CFR or OEF-PSRO algorithms on the trained environment model to learn the NE strategy as the MB policy. Finally, we compute the NashConv to evaluate the MB policy. Figures \ref{mb_1}-\ref{mb_2} show the results of the model-based algorithm in Kuhn poker with a different number of players. From Figure \ref{mb_1}, we find that OEF-CFR and OEF-PSRO can get almost the same results. It indicates that the performance of MB mainly depends on the quality of the trained environment model, and we can use either algorithm to calculate MB policy. We find that with the increase in the dataset size, the performance becomes better. It indicates that if the dataset includes enough data, the trained environment model is closer to the actual environment (test environment). Though with the increase of the proportion of random datasets, the performance of MB policy becomes better, the improvement is not very significant, and the trend is almost gone when the number of players is large. Therefore, the performance of MB policy mainly depends on the size of datasets and is mildly influenced by the quality of datasets. We noticed that the MB method performed worse in games with more players than in games with fewer players. It indicates that OEF in multi-player games is more challenging than in two-player games.

\subsection{Performance of the Benchmark Algorithm BC+MB}
From the above results, we find that the BC policy performs poorly in the random dataset while the performance of the MB method is good. However, the MB method performs poorly in the expert dataset, while the BC policy can perform well.
Therefore, to get a better policy on the dataset generated by an unknown strategy, a straightforward way is to merge these two policies by assigning proper weights to them in different cases. Here, we use a simple merging method to combine these two policies. Let $\alpha$ be the weight of BC policy. Then the weight of the MB policy is $1-\alpha$. We preset 11 weight assignment plans, i.e., $\alpha\in\{0, 0.1, 0.2, ..., 0.9, 1\}$. Next, we use these 11 weight assignment plans to combine these two policies to get a set of final policies. Then we test these final policies in a real game to get the best final policy. Another more complex method is to learn the weights using few-shot interactions with the real game. We left this as future work.

\begin{figure}[t]
\centering
\subfigure[Kuhn Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_2_players_bc_mb_500_2.pdf}
\label{kuhn_2}}
\subfigure[Kuhn Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/kuhn_poker/kuhn_poker_2_players_bc_mb_1000_2.pdf}}
\subfigure[Leduc Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/leduc_poker/leduc_poker_2_players_bc_mb_10000_2.pdf}}
\subfigure[Leduc Poker-2 Player]{
\includegraphics[width=0.235\textwidth]{figures/results/leduc_poker/leduc_poker_2_players_bc_mb_20000_2.pdf} \label{kuhn_5}}
\subfigure[Kuhn Poker-2 Player]{
\includegraphics[width=0.23\textwidth]{figures/results/kuhn_poker/kuhn_poker_2_players_min_weight_2.pdf}
\label{weight_1}}
\subfigure[Leduc Poker-2 Player]{
\includegraphics[width=0.23\textwidth]{figures/results/leduc_poker/leduc_poker_2_players_min_weight_2.pdf}
\label{weight_2}}
\subfigure[Kuhn Poker]{
\includegraphics[width=0.23\textwidth]{figures/results/kuhn_poker/kuhn_poker_players_learning_dataset.pdf}
\label{learning_1}}
\subfigure[Leduc Poker]{
\includegraphics[width=0.23\textwidth]{figures/results/leduc_poker/leduc_poker_players_learning_dataset.pdf}
\label{learning_2}}
\caption{Experimental results of the benchmark algorithm BC+MB.}
\label{fig:mb_bc}
\end{figure}
Figures~\ref{kuhn_2}-\ref{kuhn_5} show the performance of the BC+MB method in Kuhn poker and Leduc poker under offline datasets with different sizes. The results of BC and MB methods are also plotted for comparison. We find that the BC+MB method performs better than both BC and MB methods in all cases. Figures~\ref{weight_1}-\ref{weight_2} show the weights of BC ($\alpha$) that make these combined policies perform best in these datasets. We find that as the proportion of the random dataset increases, the weight of the BC policy in the final policy decreases. It indicates that when the dataset includes many expert data, i.e., the proportion of random datasets is low, the performance of the BC policy is always better than the MB policy, whatever the size of the dataset. Therefore, in that case, the weight of the BC policy is high.
In conclusion, the BC+MB method can take the best of both BC and MB methods and perform better than BC method and MB method. We also test the BC+MB algorithm in learning datasets which can be viewed as datasets produced by an unknown strategy, and the results are shown in Figures \ref{learning_1}-\ref{learning_2}. We find that BC+MB performs best among these methods. It indicates that given a dataset generated by an unknown strategy, BC+MB can always get a good policy.

\section{Conclusion}
\label{sec:con}
We initiated an investigation of offline equilibrium finding (OEF), i.e., equilibrium finding on offline datasets. We collected data from four widely-used games using three data-collecting strategies and proposed a benchmark method BC+MB -- a combination of behavior cloning (BC) and model-based (MB) methods. Our two MB algorithms, OEF-PSRO and OEF-CFR, are adapted from two well-known equilibrium finding algorithms, PSRO and Deep CFR. Extensive experiments demonstrate that BC and MB experience certain shortcomings, which we can overcome with the benchmark algorithm. We hope our efforts may open new directions in equilibrium finding and accelerate the research in game theory. Datasets and code are available at~\url{https://github.com/SecurityGames/oef}.

\textbf{Future works.} There are several limitations of this work that we intend to tackle in the future. First, the games we considered are rather smaller and large-scale games like Texas Hold'em poker~\cite{brown2018superhuman} were postponed till future work. Second, the types of generated offline datasets are limited. For future work, we plan to collect datasets using large-scale games and connect our library to StarCraft~II Unplugged~\cite{mathieu2021starcraft}. We will also include more data-collecting strategies (e.g., bounded rational agents) as well as additional human expert data\footnote{\url{http://poker.cs.ualberta.ca/irc_poker_database.html}} to diversify the provided datasets. Moreover, we will investigate the relations between data distributions and OEF algorithms to characterize the influence of the data on the performances of OEF algorithms.

\textbf{Negative societal impacts.} We do not foresee any negative societal impacts.

\clearpage

redpajama_set_name: RedPajamaArXiv


text:
Q: ReplaceMent for Jet Excel Database Connection? I have quite an old code that should run on modern machines. The purpose of that code is to read Excel Files and do some fancy stuff with it.
The Connection-string to the Excel-file is the following:
private static readonly string CONNECTION_STRING_TEMPL =
"Provider=Microsoft.Jet.OleDb.4.0; data source={0}; Extended
Properties=\"Excel 8.0;HDR=YES;IMEX=1;\"";

Now it looks like modern OSses (especially 64bit) do not like Jet.OleDB that much.
Is there a more "modern" driver I can use as a replacement just by changing the connection string? (I do not want to completely rewrite the Code using state-of-ther-art techniques.

A: You can try ACE.OLEDB
private static readonly string CONNECTION_STRING_TEMPL = "Provider=Microsoft.ACE.OLEDB.12.0;" +
                       "Data Source=" + FilePath + ";" +
                       "Extended Properties='Excel 12.0;HDR=Yes;IMEX=1' ";

redpajama_set_name: RedPajamaStackExchange


text:
2012 International Documentary Association IDA Awards Winners | Indie Film Spotlight
The 28th Annual IDA Documentary Awards were handed out tonight at the Directors Guild of America in Los Angeles. Penn Jillette, the speaking half of Penn and Teller, hosted the ceremony. The big winner was Searching for Sugar Man, taking home the Best Feature Award.
It was especially gratifying for me as I attended the World Premiere of this marvelous doc at the 2012 Sundance Film Festival, then selected it as one of my Top 10 (see my review and pictures of the Q&A). The film also won top honors for Best Music, with original songs penned by Sixto Rodriguez and additional score by director Malik Bendjelloul. Saving Face was awarded Best Short.
via 2012 International Documentary Association IDA Awards Winners | Indie Film Spotlight.
Awards, News, Searching For Sugar Man Film
The Skinny – Searching for Sugar Man – December 2012
Best Album Reissues of 2012: Rodriguez, 'Searching for Sugar Man' | Rolling Stone
2 thoughts on "2012 International Documentary Association IDA Awards Winners | Indie Film Spotlight"
JoAnn Witt says:
Congratulations! Well deserved!
marisol mireles says:
No doubt ! Awsome

redpajama_set_name: RedPajamaCommonCrawl


text:
Leicester University porter saves day after seeing student's suicide note
Nicola Woolcock, Education Correspondent
July 5 2018, 12:01am, The Times
The porter is among dozens of support staff trained in spotting signs of distress and mental health disordersUniversity of Leicester
A university porter helped to save the life of a student after finding a suicide note in a halls of residence.
The porter is one of dozens of support staff at Leicester University, including chefs and cleaners, who have received training in spotting signs of distress and mental health disorders.
There were almost 100 student suicides for the year to July 2017. Sam Gyimah, the universities minister, told vice-chancellors last week that students' wellbeing had to become a priority. Universities will be urged to create systems that allow them to contact students' families if they have concerns — at present they are unable to do so because of privacy laws.
Several suicides have occurred at Bristol University, and criticism was levelled at Exeter University during…


text:
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29/4/2020· Zinc processing, the extraction of zinc from its ores and the preparation of zinc metal or stone compounds for use in various products. With its low melting point of 420 °C (788 °F), unalloyed zinc has poor engineering properties, but in alloyed form the metal is used extensively. The addition
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Is the Pontiff of Peace advocating war? No, but the self-proclaimed "Caliph" Ibrahim wants his fight to be a true holy war on both sides, and his strategy seems to be succeeding.
The "him," the "aggessor" in this case is the self-anointed "caliph" whose forces, formerly called ISIS, now dominate swathes of Syria and Iraq, and this is just the kind of reaction he's been hoping for.
Caliph Ibrahim, as he calls himself, wants to provoke a 21st century crusade against his Islamic State. He wants to force his enemies into a religious war arousing atavistic instincts rooted in the Middle Ages—the great glory days of Islam—that linger in the hearts of many Muslims around the world. And by every indication he is succeeding.
Certainly in recent days desperate men of the cloth, including some of the highest officials in the Catholic and Anglican churches, have played into the caliph's hands by speaking out in support of the U.S. military action or calling for still more to be done—thus imbuing close air support with heavenly purpose that it probably can and should do without.
Even before Monday, Pope Francis used language interpreted by many as an endorsement of war.
There is a painful irony here. As recently as last month Francis was moved to tears by the carnage in Gaza, Iraq, and Ukraine.
Then Archbishop Silvano Tomasi, the Vatican's observer at the United Nations in Geneva, called not only for humanitarian assistance for those persecuted by the Islamic State, but explicitly for "effective military protection," as well. Archbishop Giorgio Lingua, the pope's ambassador to Baghdad, told Vatican radio that U.S. military intervention in Iraq in recent weeks "had to be done," otherwise the caliph's forces could not be stopped.
In Britain, meanwhile, Prime Minister David Cameron just started beating the drums of war in the pages of The Sunday Telegraph: "If we do not act to stem the onslaught of this exceptionally dangerous terrorist movement, it will only grow stronger until it can target us on the streets of Britain." And this against the backdrop of calls to action by the Bishop of Leeds, who has raised "the serious concern that we do not seem to have a coherent or comprehensive approach to Islamist extremism as it is developing across the globe," and the Bishop of Manchester, who called on the government to meet its "moral obligation" to Iraq's Christians.
All this must be gratifying to the caliph, who has managed to transform himself and his followers from a group of fanatics roaming the Syrian and Iraqi desert only a couple of years ago to a force that seems to be challenging the whole of Christendom.
When Abu Bakr al-Baghdadi of ISIS proclaimed his Islamic State and re-branded himself as Caliph Ibrahim at the end of June, his calls for holy war were not veiled in the least, and his imagery was straight from the Middle Ages: "O mujahidin in the path of Allah, be monks during the night and be knights during the day," he told his followers. "Take up arms, take up arms, O soldiers of the Islamic State! And fight, fight!" he exhorted them.
Apparently, Rome and the world are taking him seriously.


The design of this model successfully combines the elegance of the past with modern inspirations in order to offer a comfortable and classy armchair – suitable for the most elegant environments. Its harmonious lines provide safe and comfortable support, with a wide and deep seat. The cushions are made of expanded polyurethane foam. The frame, raised by metal legs, is made entirely of wood and is available in two versions: armchair or two-seater sofa. Also available as a swivel armchair and highback chair.


text:
namespace metrics {

#if !defined(OS_ANDROID)

// This function makes it possible to tell from the callstack why startup is
// taking too long.
NOINLINE void StartupHang();

// This function makes it possible to tell from the callstack why shutdown is
// taking too long.
NOINLINE void ShutdownHang();

#endif  // !defined(OS_ANDROID)

// This function makes it possible to tell from the callstack alone what thread
// was unresponsive.
NOINLINE void CrashBecauseThreadWasUnresponsive(
    content::BrowserThread::ID thread_id);

}  // namespace metrics

#endif  // CHROME_BROWSER_METRICS_THREAD_WATCHER_REPORT_HANG_H_

redpajama_set_name: RedPajamaGithub


At a defense industry event in London on February 2, 2016, the U.K. Defence Secretary Michael Fallon briefed a crowd of contractors and reporters on some exciting news. The British Ministry of Defense has agreed to purchase two Airbus Zephyrs, an odd, spindly, solar-powered drone with some remarkable capabilities. The drones "will be able to fly higher and for longer to gather constant, reliable information over vast geographic areas," explained Fallon. This is not exaggeration: The aircraft will be able to fly at higher altitudes—up to 70,000 feet—and for longer—up to several weeks per flight—than any military drones currently in use.
Sometimes referred to as sub-orbital satellites or high-altitude pseudo-satellites, these high-altitude long-endurance drones fly above 60,000 ft. and are capable of remaining aloft for long periods, possibly even years. In the military sphere, these systems could serve as persistent surveillance and reconnaissance platforms or communications relay point, beyond the reach of air defenses. Meanwhile, Facebook and Google are betting on this technology to bring Internet to remote areas. Here's what you need to know.
A high-altitude long-endurance drone is an unmanned aircraft that flies at altitudes higher than about 60,000 ft. and can remain airborne for extremely lengthy periods of time. The Northrop Grumman RQ-4 Global Hawk (and the U.S. Navy variant, the Triton), which flies at altitudes of up to 65,000 ft. and can remain aloft for up to 35 hours, according to a company fact sheet, is currently the highest-flying and longest-endurance unmanned aircraft to see extensive use. However, drones such as the Zephyr represent a radical departure from the Global Hawk: they can fly at higher altitudes and, as they are powered by small engines, they have much higher endurance.
High-altitude long-endurance unmanned aircraft can be used for various applications. They are well-suited for carrying out extensive surveillance and image-collection operations. According to an executive at Titan Aerospace, a high-altitude drone company that was acquired by Google, a solar-powered sub-orbital drone could collect the kind of multi-spectral imagery captured by satellites today for about one seventh of the cost. In the military sphere, these aircraft could carry Wide Area Persistent Surveillance systems, which require high-altitude platforms to be effective. These surveillance systems are designed to track activity over an area for an extended period of time. Sub-orbital drones could also serve as effective communications relay hubs. The potential application that has attracted the most attention, however, is the use of these systems to deliver wireless Internet to remote areas. Google and Facebook are both currently working to develop high-altitude unmanned aircraft specifically for this purpose.
This history of these aircraft is intimately tied to the development of solar-powered flight. Endurance is one of the principal advantages that unmanned aircraft offer over manned aircraft, which can only fly for as long as the pilot or pilots are able to fly safely. The marriage of solar power and unmanned flight is therefore appealing, as it could allow endurance times measured in days and weeks, rather than hours. In 1974, a group of engineers led by Robert Boucher conducted the first successful flight of a solar aircraft, the Sunrise I. The aircraft, which had a wingspan of 35 ft. but weighed just 22 lb., was developed under the auspices of the Advanced Research Projects Agency as part of an initiative to explore ways of creating unmanned aircraft with endurance greater than 12 hours. Over the following decade, engineers in both the U.S. and Europe worked to improve solar flight technology, and began to explore the feasibility of manned solar flight.
In the late 1980s, a California company by the name of AeroVironment, which had been working on a large solar-powered manned aircraft prototype, was contracted by the U.S. government to develop an ultra-long-endurance unmanned solar aircraft. The resulting prototype, called the Pathfinder, demonstrated the feasibility of using the power of the sun to create sub-orbital unmanned aircraft. The program was cancelled in 1993, and the Pathfinder was transferred to NASA. By 1998, the Pathfinder had been tested at 80,000 ft., breaking the altitude records for both solar-powered and propeller-driven aircraft. NASA is continuing its research on solar-powered high-altitude drones with the Centurion and Helios, two variants of the Pathfinder.
During the late 1980s and through the 1990s, NASA also tested High-Altitude Long-Endurance (HALE) unmanned aircraft as a means of collecting environmental data in the upper reaches of the atmosphere. Using two aircraft developed by Aurora Flight Sciences, the Perseus and the Theseus, NASA engineers conducted several dozen flight tests between 1993 and 1999. The initiative aimed to develop technologies that would enable flights at up to 100,000 ft.
Not all early ultra-high-altitude unmanned aircraft are solar-powered. In the late 1980s, Boeing developed a high-altitude long-endurance unmanned surveillance aircraft that demonstrated how the use of lightweight composites was a viable means to achieve high-altitude flights. Powered by two 175 hp engines, Boeing's Condor spy plane had about as much power as a large sedan, and yet it was capable of flying at altitudes of over 60,000 ft. The Condor completed extensive testing, but was eventually cancelled due to lack of stealth capabilities, according to a company statement.
One of the most successful HALEs to date, and one of the first to draw the wider public's attention to the potential of the technology, is the Airbus Zephyr. Now in its eighth iteration, the Zephyr was originally developed in the early 2000s by QinetiQ, a British defense contractor, and was later acquired by Airbus (in 2013). In 2003, the BBC noted that British Airways, in order to make space for a maiden flight of the Zephyr 3 (which was later cancelled because of damage sustained to the balloon that was going to launch the drone), planned to modify the route of its Concorde supersonic passenger jet. Weighing just 90 lbs., the aircraft has a wingspan of just over 70 ft., and can be launched by hand. The Zephyr is only capable of carrying a few pounds of payload, but it has extremely high endurance. The Zephyr holds the record for the longest flight by an unmanned aircraft, at 336 hours (or 2 weeks), which it set in 2010, as well as the record for the highest flight by an unmanned, winged aircraft. In September of last year, the sixth incarnation of the Zephyr conducted the first authorized flight of a sub-orbital unmanned aircraft in civil airspace, flying at 61,000 ft. over the United Arab Emirates for a period of just under 24 hours, demonstrating the feasibility of safely integrating this type of aircraft into domestic airspace. In April, Airbus announced that its Zephyr 8, which will have an even higher endurance than previous versions of the aircraft, will be ready by the end of the year, and will be intended for certification in civil airspace, in addition to military applications. Airbus is reportedly looking to increasingly position the Zephyr for commercial markets, according to Flightglobal. The U.K.'s purchase of two Zephyrs, which will be used to evaluate whether the aircraft is capable of fulfilling the armed forces' requirement for a long-endurance surveillance drone, represents the first sale of the aircraft.
Aerospace giant Boeing is also hoping to gain a share of the sub-orbital market with its Phantom Eye unmanned prototype. While the liquid-nitrogen-fuelled Phantom Eye—designated an experimental aircraft—will only fly at around 65,000 ft., and could potentially remain aloft for four days, it is capable of carrying a payload of up to 450 lbs., enough for some of the high-powered surveillance and reconnaissance equipment currently used in the RQ-4 Global Hawk, which has a much lower endurance (but a larger payload). The Phantom Eye was tested in collaboration with NASA's Dryden Flight Research Center, and has so far not progressed beyond an experimental phase. Earlier this year, Boeing Phantom Works president Darryl Davis told reporters that the company was considering the feasibility of mounting a solid-state laser onto a Phantom Eye so that it could be used for missile defence. In 2010, Boeing won a contract to develop a solar-powered ultra-high-altitude drone for the U.S. Defense Advanced Research Projects Agency, with the aim of achieving a platform endurance of five years, but the program was cancelled in 2012.
In the summer of 2013, a New Mexico-based startup with just twenty employees made headlines when it announced that it was developing a solar-powered drone that could fly for up to five years at a stretch. Titan Aerospace unveiled its spindly Solara 50, and the larger Solara 60, ultra-high-altitude prototypes at the Association for Unmanned Vehicle Systems International's annual Unmanned Systems convention, claiming that the Solara 60 model could carry up to 250 lb. of payload. In April, 2014, Google announced that it had acquired Titan Aerospace for an undisclosed amount. The Internet giant plans to use the solar-powered aircraft as communications relay hubs to beam Internet to remote areas. As part of its Project Loon initiative, Google plans to use the drones alongside high-altitude balloons to create a global wireless network. In May of this year, it was announced that a Solara 50 was destroyed when it crashed shortly after take-off during testing at a facility in New Mexico. An investigation of the crash carried out by the U.S. National Transportation Safety Board found that an unexpected thermal current had destabilized the aircraft, causing both wings to detach from the fuselage. In January, the Guardian published an in-depth report revealing new details about Google's sub-orbital drone program, including the company's plans to beam 5G wireless Internet from the aircraft.
Before it was acquired by Google, Titan Aerospace engaged in extensive acquisition talks with another Internet company hoping to bring connectivity to the Earth's remotest areas: Facebook. But when it was outbid by Google, the social media company turned to a UK-based outfit, Ascenta, which it purchased in March, 2014. A small a secretive firm, Ascenta is said to have been staffed by some of the team members from QinetiQ's Zephyr program. In March of this year, just one year after the acquisition, Facebook's Mark Zuckerberg announced that the company had successfully completed the first test flight of a subscale prototype of its high-altitude drone, which will be called the Aquila. The final aircraft is planned to fly at an altitude of between 60,000 ft. and 90,000 ft., and have an endurance of up to 90 days. The company described in a video that it will use laser technology to beam connectivity among a constellation of aircraft, which will then provide wireless Internet to populations on the ground.
A version of this story was originally published by the Center for the Study of the Drone.
Arthur Holland Michel is a researcher and writer covering unmanned systems, defense, and national security. He is a founder and co-director of the Center for the Study of the Drone at Bard College.


I'm inquisitive, simple, and caring. I believe in mutual respect, love, and loyalty. I'm serious that way.
I'm hard working, honest to a fault, and a little shy around women.
I'm a techie in ever sense of the word. I love science in sci-fi, but I'm no nerd. I hit the gym at least three times a week, so I'm in good shape. I love one-on-one deep conversations but can't stand drama.


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Q: ElasticSearch-composite IndexReaders cannot exceed 2147483647 Getting following exception from one of the shard in ES Index,
[Failed to start shard, message [IndexShardGatewayRecoveryException[[fw][4] failed recovery]; nested: IllegalArgumentException[Too many documents, composite IndexReaders cannot exceed 2147483647]; ]]
Please somebody help.

A: I confirmed through another post, we cannot store more than 2147483647 docs per shard.This is the inernal limit of the lucene index 4.x.


Could be wrong but both web sites appear to be Dino Direct clones --same site layout--only with different title names.
What sane individual would give thier Credit Card information to a web business with no business enitiy onformation, no business address and no telephone number shown anywhere on the web site even if the prices were not double or triple for same products elsewhere?
I know I always check the about us/contact us area. I also, google everything including the address and number.
Laserpointersales shows this item as $2167.60---the Bob price times 258%--- to charge a 158% markup for a personal shopper, order/money taker web site in China,to buy for you in China from Bob Laser is more than a bit too much and unreasonably greedy.
"Dino Direct, Lasersman, Yourlaser, TheLaserFans. Pretty much bad feedback from forum members. I know Chinese business style cause I'm a Chinese too.
I live in Malaysia, for local we call it as - Cina Ah Pek company(Chinese Grandfather company)."
"Due to priority of making money, they will sweet talk to convince you, promise you heaven and earth...and that's the promise, nothing more or less. They might or might not fulfill their promise and it greatly depends how lucky you are when dealing with them. When the limited company went bust or stinks, they will recreate a new image to camouflage the old practice."


M I H Communications is a mobile phone shop selling a range of phones and associated products. Based in Melton Road, Leicester, M I H Communications is a mobile phone shop.
M I H Communications is in Melton Road, Leicester. You can view a list of neighbouring businesses by going to the Melton Road page.
If you would like a list of other mobile phones shops & other retail outlets listings throughout Leicester, please visit the Mobile Phones section of this website.
Note: the pin is positioned at the postcode centre, and may not correspond with the precise location of 201 Melton Road.


NHL notes: Milan Lucic to the Oilers?
The Edmonton Oilers are the reported front-runners to sign free agent forward Milan Lucic.
Sportsnet's Gene Principe tweeted that one source said it was 100 per cent certain the 28-year-old would sign with Edmonton on July 1, the first day teams are permitted to sign free agents.
However, Boston Bruins beat writer DJ Bean later tweeted the Lucic denied he had any agreement with the team. Under the NHL's collective bargaining agreement, teams and free agents have an "interview" period in the six days leading up to July 1, but are not permitted to negotiate or agree on contracts.
Lucic, who spent last season with the Los Angeles Kings, was in Edmonton Tuesday to meet with Oilers management, after spending Monday in Vancouver to meet with the Canucks.
Milan Lucic spent most of yesterday being wooed by the Canucks in his hometown. The Oilers are the front-runners though.
Lucic is one of the top players available in what is considered a generally shallow free-agent pool, behind Tampa captain Steve Stamkos. the 6-foot-3, 233-pound Lucic played 81 games with the Kings, scoring 20 goals and adding 35 assists.
A person with direct knowledge of the decision has confirmed to The Associated Press that the Colorado Avalanche have placed veteran defenceman Brad Stuart on waivers with the intention of buying out the final year of his contract.
Stuart is a 16-year NHL veteran, who is set to make $3.6 million US next season in the final year of his two-year, $7.2 million contract. A nagging back injury limited Stuart to appearing in just six games last season.
Stuart will become an unrestricted free agent if he goes unclaimed on Wednesday.


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Mission preparations were becoming more routine in the 446th's second month, and this allowed for more to be flown. The 705th squadron began a streak of 25 consecutive missions without the loss of a plane. The first awards of the Air Medal were made to 446th personnel for participating in 5 missions over enemy territory.
Mission Details
January 4: Kiel
January 7: Ludwigshaven
January 11: Brunswick
January 14: Noball target at St Pierre-des-Jonquies, France
January 21: Noball target
January 29: Frankfurt
13 planes bombed the city of Kiel, with 11 more experiencing mechanical problems which forced their return before they could accomplish their mission objective. One crewman died from a lack of oxygen. Bombs were believed to have hit the city itself and it's surrounding areas. There was moderate-heavy flak, but chaff dropped by lead planes reduced it's severity.
15 planes bombed the harbor at Kiel, along with the shipbuilding yards, with good results. Incendiaries burst in the northeast section of the harbor, north of the naval base, and on the south side of the entrance to Kiel canal. More fell within the Deutshe Werk shipbuilding yards. Smoke was seen from 100 miles away. S/Sgt Edward Partridge received credit for shooting down a JU 88.
This mission was aborted when heavy overcast prevented the planes from meeting the 392nd bomb group.
This mission was recalled when the weather took a turn for the worse.
Railroad tracks on each side of a bridge and runways and buildings at an airdrome were hit. A photograph showed a heavy concentration of bursts on and around a 12-span railroad. Direct hits were observed on the primary target area, a buzz bomb launch site. The group would be grounded for the next week due to poor weather.
The group was unable to locate the target due to cloud cover and was forced to turn back. On their way home, they were met by intense flak, which heavily damaged one plane, "Merle Lee" (42-7584), forcing a crash landing in the UK. There were no casualties.
The target was bombed successfully by 25 of the 26 planes that took off. One plane did not drop when another plane flew under it, avoiding a catastrophe.
24 planes dropped on Frankfurt with good results. One plane, "Hula Wahine" (42-7578) piloted by 2/Lt Ernest Bruce, lost all 4 superchargers. Fighting off repeated enemy fighter attacks while flying 75 feet over Belgium, the pilot crash landed at an RAF base with 2 wounded crewmen.
1,146 bombs were dropped on Brunswick, in one of the coldest missions yet flown, -18C. There was little enemy opposition.
This mission was scrubbed when the planes failed to assemble, but 2 planes "tagged along" with the 389th bomb group and bombed the intended target.


Krispy Kreme's seasonal holiday donut menu for 2017 remains largely unchanged with the arrival of the same trio of donuts as last year and includes the Santa Belly Donut.
The Santa Belly Donut is a yeast shell donut filled with chocolate Kreme and decorated to look like Santa's jolly belly with a combination of red and chocolate icing plus a candy buckle.
- Melted Snowman Donut - A yeast shell donut filled with original Kreme and dipped in white icing. It's decorated chocolate icing and a candy snowman face.
- Holiday Sprinkles Donut - A yeast chocolate iced ring donut with holiday sprinkles.
You can find the festive donuts at for a limited time at participating locations in the US and Canada.


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Home Comics Kickstarter INTERVIEW: Hollywood concept designer Ricardo Delgado talks new Kickstarter, DRACULA OF TRANSYLVANIA
INTERVIEW: Hollywood concept designer Ricardo Delgado talks new Kickstarter, DRACULA OF TRANSYLVANIA
Ricardo Delgado is perhaps best known in comics for his AGE OF REPTILES graphic novels.
Zack Quaintance
One of this week's new Crowdfunding highlights is Dracula in Transylvania, which is an illustrated novel from accomplished Hollywood concept designer, Ricardo Delgado.
You may or may not know Delgado by name, but you most certainly know his work, which has included films like Apollo 13, Men in Black, The Incredibles, Wall-e, and more. Within comics itself, Delgado is best-known for his Age of Reptiles graphic novel. Now, he has turned his sites on a new story, that of Dracula.
I recently had a chance to ask him a few questions about the book, the format he choose, the role of historical research in this adaptation, and more. Check it out below…
ZACK QUAINTANCE: I know obviously there are many reasons Dracula is such an enduring story and character, but I'm curious, what in particular drew you to adapt DRACULA OF TRANSYLVANIA for your new project?
RICARDO DELGADO: As a kid I noticed all of the different versions of the story, and each of them had their own particular way of excelling at a facet of the overall tale, but none fully grabbed the entire Dracula ambience and ran with it. In some of the stories the bat seemed fake, or the teeth did not seem real, or the wolf form of Dracula was just a regular wolf or the mist form of the vampire was just smoke printed backward, and I never truly got the sense of Dracula the shapeshifter, Dracula the conqueror, or Dracula the embodiment of evil. I also felt that it was strange for the Brides to stay behind to guard castle Dracula. Why? Who's gonna break in to the creepiest place in the world? So I made him this malevolent, powerful presence and brought the brides along on his journey as added muscle, to use gangster film lingo.
ZACK: I'm also curious about the format. It's an illustrated novel, which I find can mean a pretty wide variety of things. What does that look like for this book?
RICARDO: My elevator pitch for DRACULA OF TRANSYLVANIA would be ART OF STAR WARS meets SALEM'S LOT. You're getting a whole story, an entire novel, but it's full of conceptual designs that are part of the novel. Because I can write I can tell this story, but also as a concept artist I can then take the concepts I've written about and weave them into the novel. The best example I can come up with is the Stephen King/Bernie Wrightson illustrated novella CYCLE OF THE WEREWOLF, and while it is daunting to walk in those prodigious steps, in my version you'll see designs that match the ideas that I've come up with for my story. Have you been disappointed with the mist version of Dracula in the films where that form is used? In this book you won't be I can guarantee that. You'll also see a kaiju version of Satan, if that's any further enticement, lol.
ZACK: How did you extensive background as a conceptual designer in Hollywood influence the making of this book?
RICARDO: it helped me early on in the sense that I knew that my conceptual muscles were flexed to help make the story really unique and strong, while also knowing in the back of my had that my ability to design my concepts would make DRACULA OF TRANSYLVANIA a very new, very unique experience. Just because the setting is Victorian does not mean that the supernatural concepts have to be that way. And I had no visual effects budget that I had to negotiate with anyone. If I wanted twenty demons to be dancing around a raging fire ready for Satan to appear, I only had to write it, then have the fun of designing really cool, really scary legions of hell for the audience to enjoy, as far as a hellish congregation can be enjoyed, lol. There's stuff in the book that were ripe for conceptualization at the writing level, like the dungeons of Dracula's castle, which turned out to be one of the writing highlights of the story. There's actually a church and a library in Dracula's basement. Among many things.
ZACK: One of the choices that really caught my eye with this project was the historical footnotes. What was your research process like for this book?
RICARDO: The research for the early formation of the story led to the footnotes in later drafts. I needed the details of 1899 to be factual, and that led to points of interest that I thought, "How can I squeeze that into the story?" and sometimes I could and other times I could not, and that's where the footnotes kicked in. In addition, when the story changes locale in the Modern World I also add a page of facts of the city and its' peoples to further bridge the transition and to give the audience more recent information that might or might not have an impact on the story. Also, I do find stuff like that interesting and it's fun to research, but if I wanted to have an opera sequence in my story I needed to find out if there were any opera houses in Rome in 1899, which was of course likely, but also which ones were open at that time. Then I had to find an opera that fit the mood and themes of my story to them place it in the story. After that was the even cooler part, which was adding the vampires to the mix.
ZACK: Finally, tell me about your favorite tier on this Kickstarter?
RICARDO: It's actually both ends of the spectrum. As a kid whose parents gave him everything they had but could not always afford everything I love the idea of the straight download for casual fans of the character who may not be familiar with my work, while the deluxe additions give folks who are there because they enjoy my artwork can also purchase an original piece of my art along with their book! I call that a win/win, or as the Transylvanian might say, "A Vin/Vin!"
Sorry, lol.
Dracula of Transylvania is live on Kickstarter now.
Dracula of Transylvania
Ricardo Delgado
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Zack Quaintance is The Beat's reviews editor. He Tweets compulsively about storytelling and comics as Comics Bookcase.
Comics Crowdfunding Round-Up: FINDING DEE, A THING CALLED TRUTH VOL. 2, and 3 more projects we love!
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Comics Crowdfunding Round-Up: CAT WISHES STORIES, TORRENT, and 3 more projects we love!
DCU's "Gods and Monsters" era begins with announcement of 10 projects
DC ROUND-UP: DC kicks off Black History Month early with DC...
Jay Martin's LOST BOY coming from Dark Horse this summer
GILGAMESH: ETERNAL #1-2 currently crowdfunding on Zoop
Zach M. Stafford's GOOD COMICS FOR BAD PEOPLE gets a hardcover...


US President Donald Trump has said Treasury Department official David Malpass is his choice to lead the World Bank.
Mr Trump introduced Mr Malpass as the "right person to take on this incredibly important job".
Mr Malpass, a staunch critic of the 189-nation lending institution, said he was honoured by the nomination.
Key goals, he said, would be to implement changes to the bank that he and US treasury secretary Steven Mnuchin helped negotiate, and to ensure women achieved full participation in developing economies.
Mr Malpass would succeed Jim Yong Kim, who departed in January three years before his term was due to end.
Other candidates are likely to be nominated for the post by the bank's member countries.
The bank's board will take the final decision about who will become its new president.


Equanimity, surrender, bliss when our heart(s) are graced with them? Is a pure gift. When we surrender to our soul and acknowledge that, we can't do it, this, anymore. When we pause to feel the stream of life carrying us, taking us where we need to go, even though we have no idea where that might be? Some pretty cool "things" happen.
Late last year I found myself working and "living" in our fair city, Cambridge, UK. Having fallen back in love with cycling after trying to kill myself running, instinctively I brought a few bikes with me. Riding in Cambridge is not the horror show many would lead you to believe. The streets, bridleways and footpaths this spring and summer have been refreshingly pleasant. I wake early, and not long after that, I'm out finding a pedaling flow before the sun comes up – Bliss! My almost daily rides have truly been wonderful, that is until last Sunday morning.
As I approached a blind corner near the top of Chapel Hill, which runs into a lovely, peaceful wooded path. There was a woman walking ahead of me, and my presence startled her.
"You need to be riding back on Chapel Hill road with the other cyclist," she told me hastily.
"Good morning ma'am?" I replied, as my unquestionable 'Murican accent wafted in the still air.
"Why don't you cyclist follow the rules like everyone else" she replied.
Kate smiled and laughed at my "Cars R Coffins" jersey. I on the other hand, melted, as Kate let down her startled guard. We walked for a short while, then parted ways further down the dirt path that was unfolding in front of us … I had broken into her peaceful temple, and for that I was deeply sorry.
The moment I finally surrendered my heart and mind to being alone, solitude became so, so sweet.
Equanimity, allowing the world to unfold before your very eyes. Steady, kind, compassionate thoughts. Your mind not grasping, reaching, or recoiling from the unpleasant words sitting just behind your pursed lips and brow. When this happens? The sweet pulse of the universe, resonates deeply.
Cultivating self-compassion softens the loneliness that often makes finding equanimity unbearable. I often have to remind myself that the pain of loneliness, ebbs and flows. Sure, my loneliness is burning deep right now. Although if I'm patient?!? This loneliness too shale pass, and the sweetness of equanimity will soon take its place.
From an early age, I was led to believe that I needed to become a "better" and "improved" version of myself, even if at first I didn't exactly understand why. When I was young lad, I found myself inured with the basic belief that the main underlying issue to all my problems was – Me. I'm what's wrong in the world. These thoughts have stayed with me my entire life, even now, writing to you with an open heart and mind. I have traveled the world far and wide; taken to heart many self-help books, sought out the best doctors and healers, consulted spiritual folks whose motives were unclear at the time. Everything and anything else you can imagine, all in an effort to make myself just good enough.
Many of you reading tenderly this early Spring morning, may be thinking that mindfulness, spirituality and self-help are one in the same – the latest trend to follow or a conversation starter at your local yoga studio. I don't believe they are. Using myself as a relatively small sample size, I believe they are fundamentally different. Our society is currently in the process of turning mindfulness into an endless stream of self-help profits – Mindfulness Based Stress Reduction seminars and many other method(s) for correcting our life long ailments. What these programs do, is to misrepresent the awakening that discovering our own spirit offers.
Mindfulness is not about "fixing" anything even remotely spiritual or becoming spiritually better off than anyone else. There is no price tag attached to mindfulness. For myself, mindfulness is freedom. Freedom from the belief of my unworthiness in our mad world … Mindfulness is about acceptance. Mindfulness, when I find myself at the edge of tears after a bad day, is about meeting (again) who we really are, and allowing ourselves to experience this amazing world, in this raw, unsettling moment.
My deepest apologies for being absent Dear Readers. May all your hearts be filled with warmth and happiness this weekend – Take care!
It really has been too long since my last "real" post hasn't it?!? What could Rapha, Camyoga, cyclocross racing, buddhism and minimalism have anything to do with your humble blog-o-thing? I suppose starting next week we will find out together, until then? Take care and have a wonderful weekend!
Wait in thy cottage free.
Wi' thy white bosom bare.
I find it wholesome to be alone the greater part of the time.
A single gentle rain makes the grass many shades greener.
So our prospects brighten on the influx of better thoughts.
reminded of the lapse of time.


Home » Horse Racing » Maiden Magic- The Original Horse Betting System – Maiden Horse Betting System- Maiden Magic!
Click Image To Visit SiteHow many times have you nailed 2 races of the pick 3 only to lose the third one because it was an unpredictable maiden race? Or how many times have you aced 4 races in the Pick 6, only to lose the two that were maiden races? Frustrating huh? Nothing could be more disheartening. Well that all is about to change now!
From the dawn of maiden racing, several myths have plagued the best of players and are still firm beliefs today. It has become common to see bettors shrug at the sight of these races and refuse to play them or even worse- take a blind stab in the dark! No one likes to be in such a vulnerable position, especially with money on the line!
Let's take a look at these myths and discredit them, so you never have to fall prey to what keeps average players from becoming pro handicappers.
Wrong! Maiden races are easy to pick because maidens have an added incentive to win. They don't want to remain maidens forever, so the trainers pull out all the stops in preparing for a maiden race. This a major factor that most people don't even consider, but knowing this puts you well above the curve!
Click here for more information about "Maiden Magic- The Original Horse Betting System – Maiden Horse Betting System- Maiden Magic!"
Related post to Maiden Magic- The Original Horse Betting System – Maiden Horse Betting System- Maiden Magic!


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.calibre was een Belgische groep uit Kontich die funk, metal en crossover speelde.

Geschiedenis
.calibre bracht in 2002 het album Killthelogo uit bij Warner Electra Atlantic. De band is ontstaan uit de resten van de metalband Facedown. Na de release van het album trad de band onder andere op tijdens festivals als Pukkelpop, Rock Werchter en Ozzfest. Ze stonden bekend om hun socialistische songteksten en veganistische/straight edge levensstijl.

Gitarist Niko en zanger Daniel spelen anno 2011 in de band A School of Quiet.

In 2020 werd bekendgemaakt dat de band een vervolg kent in de band Kill The Logo. In januari 2021 werd een eerste single 'Riot as One' uitgebracht.

Discografie

Album
 Killthelogo (2002)

Singles
 Calibre (2001)
 Karma (2002)
 Where is My Mind i.s.m. Camden (2002)
 The Rapist (2002)

Belgische metalband
Belgische rockband

redpajama_set_name: RedPajamaWikipedia