Za'atar is the signature spice blend of Palestine! Our za'atar is prepared in women's cooperatives using home-grown ingredients from Palestinian farmers. This herb will add lots of flavor to your tomatoes, meat, fish, eggs, or cheese. Use za'atar as a rub for chicken or with oil as a dressing on salads or sprinkle it over cream cheese, yogurt, or on toasted bread with olive oil.
Ingredients: oregano, roasted sesame seeds, and sumac.


text:
Dominic Thiem: Everyone was trying to show their best
Dominic Thiem photo: twitter.com/XHSports
By Nick Braven / June 15, 2020
Austrian tennis player Dominic Thiem won the first Adria Tour tournament.
In the final, Thiem defeated Serbian athlete Filip Krajinovic in three sets – 4/3(2), 2/4, 4/2.
"The tournament was great because everyone was trying to show their best. At a number of first-class matches, there was a great atmosphere, there were many fans. A perfect weekend" – Thiem commented on his victory.
Recall that the second tournament of the series will be held on June 20 and 21 in Zadar.
Recommended article: ROGER FEDERER WILL NO LONGER PLAY THIS SEASON
Tags: Dominic Thiem
Dayana Yastremska: I've never used prohibited substances
By Nick Braven / January 8, 2021
Angelique Kerber: Learned a lot in this crazy time
Dayana Yastremska: I flew to Dubai and two days later contracted the coronavirus
Gael Monfils: I want to return to the top 10
Maria Sharapova: At the end of my career I was no longer interested in competition
Roger Federer will no longer play this season.
Karolina Pliskova: The sooner we resume the season, the better.

redpajama_set_name: RedPajamaCommonCrawl


text:
App State Reserve Quarterback Peyton Derrick to Transfer
Published: Wednesday, February 27, 2019 at 9:57 am
By Tim Gardner
Peyton Derrick, a backup quarterback for Appalachian State, has announced via Twitter that he is transferring.
"I would like to thank everyone who has supported me along the way," Derrick said in his post. "The memories and the relationships I have made (at Appalachian State) will last forever. I would like to open my recruitment to all programs for the 3 (three) remaining years of my availability."
With Derrick gone, Appalachian State will have five quarterbacks in 2019: Zac Thomas, Jacob Huesman, Jackson Gibbs, Tanner Wilson, who helped call in signals for the offense last year, and new recruit David Baldwin-Griffin.
Starter Thomas and backup Jacob Huesman have received notable playing time for the Mountaineers.
Thomas, a first-year starter last season (2018), has two more years of eligibility as a rising redshirt junior.
Derrick, a redshirt freshman, played in six games for the Mountaineers during the 2018 season. He completed 13-of-22 passes for 186 yards and two touchdowns. He also ran for another touchdown.
Upon entering Appalachian State, Derrick was rated as a two-star quarterback out of Conway, SC High School, where he passed for 8,666 yards and 77 touchdowns and ran for sixteen touchdowns.
Derrick did not say where he would transfer. Appalachian State's Sun Belt Conference rival Coastal Carolina University is located in his hometown of Conway if he seeks to, and is offered an opportunity to play there.
Categories Front Page, Sports Post navigation
Sherrie Norris Lovin' Spoonful Cooking Column: Low Carb Diet Keeps It Healthy And Delicious
Watauga, Avery Obituaries – February 26, 2019


This full-coverage combo, available in several shades, adds creamy color like a gloss and dries to a satiny finish.
Luxurious in every way, it has a lush texture, feels satisfyingly weighty in-hand, and comes packaged with a deluxe dual-sided mirror. Even once it's dried, the formula leaves lips soft and plump. Just work fast, as the gloss sets rather quickly.


text:
\section{Introduction} \label{sec:introduction}

    The continuous double auction (CDA) is a well studied and heavily utilized mechanism for matching buyers and sellers in a variety of markets. It is typically used by trading exchanges in financial markets such as stocks and commodities exchanges. A CDA is referred to as a \emph{double} auction as it allows both buyers and sellers to submit orders and \emph{continuous} as trades are instantaneously executed upon receipt of compatible orders \cite{wellman2011trading}.

    The financial markets can be thought of as a multi-agent system composed of a variety of heterogeneous trading agents with different objectives and timescales. These agents also have different utility functions (\emph{e.g.} maximizing profit or reducing risk exposure) which are highly dependent on the behaviour and actions of the other market participants. Exchange-traded markets use a CDA mechanism and record the agents orders in a \emph{Limit Order Book}, which we outline in section \ref{subsec:lob}.

    In this paper, we explore the feasibility of classifying and imitating the behaviour of a representative set of trading agents in a limit order book. To this end, we attempt to answer the following questions:

    \begin{enumerate}
        \item Can we build a realistic agent-based model of a set of agent archetypes trading in a limit order book?
        \item Can we learn to classify these trading agent archetypes based on the limit order book data and agent orders?
        \item Can we imitate the behaviour of these trading agents using the limit order book data as inputs to a model and agent order information as the labels?
    \end{enumerate}

    Learning to classify and imitate trading agents is particularly useful for regulators around the world who are interested in preventing the occurrence of market manipulation. Moreover, it allows regulators to better understand the impact of various types of participants on the market which in turn leads to better regulations and stability in the financial markets.
\section{Background} \label{sec:background}

    \subsection{Limit Order Books} \label{subsec:lob}

        \begin{figure}[!htb]
             \centering
             \includegraphics[width=0.4\textwidth]{figures/lob.png}
             \caption{Visualization of the limit order book}
             \label{fig:LOB}
        \end{figure}

        The limit order book (LOB) is an indicator of the supply and demand for an exchange traded instrument at a given point in time (see figure \ref{fig:LOB}). It is an electronic record of all the outstanding buy and sell limit orders organized by price levels. The LOB is split into two sides; the ask and bid sides containing all the sell and buy limit orders respectively.

        A matching engine is used to match the incoming buy and sell orders. This typically follows the price/time priority rule \cite{preis2011price}, whereby orders are first ranked according to their price. Multiple orders having the same price are then ranked according to the time they were entered. If the price and time are the same for the incoming orders, then the larger order gets executed first. The matching engine uses the LOB to store pending orders that could not be executed upon arrival.

        Order types are distinguished between \emph{limit} orders and \emph{market} orders. A limit order includes a price that should not be exceeded in the case of a buy, or should not be gone below in the case of a sell. A market order indicates that the trader is willing to accept the best price available immediately.

    \subsection{Opponent Modeling}

        Opponent modeling refers to a set of methods to construct models of the other agents in the same environment with the goal of predicting various properties of interest such as the agents' actions, types, beliefs and goals \cite{albrecht2018autonomous}. Different approaches are studied in the literature including policy reconstruction \cite{brown1951iterative, veloso1994planning}, type-based reasoning \cite{barrett2013teamwork, albrecht2015hba}, classification \cite{weber2009data}, plan recognition \cite{carberry2001techniques}, recursive reasoning \cite{carmel1996incorporating}, graphical models \cite{howard2005influence} and group modeling \cite{stone1999task}.

        Learning from data~\citep{shoham2008multiagent} is an attractive framework under which assumptions can be relaxed and flexible models can be developed and calibrated to real-world data. In particular, recent advances in machine learning~\citep{goodfellow2014generative, mnih2015human} allow for black-box, task-agnostic, large-scale modeling. However, in all these settings, the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable dynamics or undesirable final results \cite{balduzzi2018mechanics, foerster2018learning}. Opponent modeling~\citep{carmel1995opponent} addresses this problem, attempting to \emph{decouple the sources of non-stationarity} by modeling separately the other learning agents from the stationary components of the environment.

    \subsection{Behavioural Cloning}

        Behavioural cloning or learning from demonstrations is a supervised imitation learning paradigm building a mapping from a historical set of observations to actions without recovering the reward function \cite{osa2018algorithmic}. The paradigm is particularly useful in applications whereby an agent is able to demonstrate its behaviour rather than specify its policy directly or formulate its reward function.
        Behavioural cloning has been studied and applied in various domains including self-driving cars \cite{pomerleau1989alvinn}, playing the game Go \cite{silver2016mastering}, generating natural language \cite{wen2015semantically} and image captions \cite{karpathy2015deep}. \cite{osa2018algorithmic} provides an excellent overview of the key methods underpinning learning from demonstrations and current open research problems.

    \subsection{Related Work}

         The importance and applications of opponent modelling techniques in first-price sealed bid iterated auctions and continuous double auction markets is discussed in \cite{mahfouz2019importance}. \cite{yang2012behavior, yang2015gaussian} perform opponent modeling implicitly in a continuous double auction setup by using Inverse Reinforcement Learning (IRL) to characterize the different trading strategies in a limit order book. In their approach, they model the trades placed in the order book as a Markov Decision Process (MDP) and cluster the reward space to differentiate between high frequency, opportunistic and market making strategies.
\section{Conclusion} \label{sec:conclusion_future_work}

    We have explored and shown the feasibility of applying opponent modelling and behavioural cloning techniques in simulated continuous double auctions. To this end, we built an agent-based model consisting of four different trading agent archetypes following strategies common in practice. We assessed the realism of our ABM against known statistical properties of real limit order book data to ensure our simulation is realistic. We formulated our approach to opponent modeling in a limit order book as a supervised learning problem of identifying (classifying) agent archetypes explicitly designed in the ABM and designed a neural network model that performs relatively well against a number of baseline methods. Finally, we used the same data and showed the feasibility of performing behavioural cloning by using a similar regression model to perform this task.

    The simple experiments explored and the initial results obtained are encouraging evidence that opponent modeling and behavioural cloning methods are potentially feasible and useful in settings of interest to the different actors in the financial markets. Future work in this direction could explore more unsupervised or semi-supervised learning approaches for opponent modelling. However, the challenges anticipated following such approaches would be around labeling any clusters obtained which would still require domain knowledge. This suggests the need for both supervised and unsupervised learning approaches.

    Methods of opponent modeling in financial multi-agent systems can be useful in a number of scenarios and from different perspectives, including but not limited to:

    \textbf{Regulation and Safety:}
    Market manipulation strategies represent an element of price distortion and creation of artificial market conditions, which in turn impedes market efficiency, transparency and fairness. In the limit order book context, spoofing agents, for instance, submit orders only to cancel them before their execution and then submit a buy or sell order to benefit from the change that their initial (fake) order caused. The ability to identify these spoofing agents and explicitly model their policies could help in the early detection of market abuse and avoiding adverse consequences like the flash crash that happened on May 6, 2010, causing a large and quickly reverting dip in the Dow Jones Industrial Average stocks. Moreover, \textit{Level 4} information, consisting of LOB labeled records, are exclusively available to exchanges and regulators to verify that market participants are complying with rules and processes \cite{paddrik2017effects}. Having access to such a data set is an opportunity to make a more informed and dynamic utility modeling. Quantifying proportions of market makers, liquidity takers and buy side participants would be valuable in detecting signs of recession or unhealthy market conditions.

    \textbf{Decision Making and Automation:}
    Market participants usually only have access to (at most) \textit{Level 3} LOB data \cite{paddrik2017effects}, which is \textit{anonymized} order flow data with new, modified, and canceled limit orders placed at a given snapshot. Although this poses a challenge to the disambiguation of limit orders placed in the order book because it imposes a myopic approach as opposed to using a consistent timeseries of consecutive orders placed by the same agent, unlabeled data does not fall short of information and opportunities. Opponent modeling can be used for the detection of iceberg orders and other forms of hidden liquidity \cite{hautsch2012dark}. By studying the occurrence of such orders, a \textit{posteriori}, in the LOB and the surrounding market conditions, we can attempt to model the utility behind such strategies. \cite{hautsch2012dark} studied the correlation between dark orders and market conditions and showed that market conditions reflected by the bid-ask spread, depth and price movements significantly impact the aggressiveness of `dark' liquidity supply. If a trader can detect such dark orders through recognizing a pattern that iceberg order placing agents fail to conceal or through modeling the utility of such agents and the specific cases that make them submit such orders, they can estimate and take into account these orders' effect on the LOB balance and predict a larger than observed spread.

    Additionally, the Request for Quotes (RFQ) process is another setting where we could benefit from opponent modeling. In this setting, clients send quote requests to multiple dealers for a specific quantity of a security. Ultimately, the dealer with the most attractive offer wins the auction. In this single, reverse, and static auction setting, one buyer (the auctioneer/the client) is looking to buy a single security from multiple sellers (the dealers) who do not observe the offers submitted by the other dealers, and only receive feedback from the auctioneer once the latter's decision for the winner of the auction is made. It is therefore important for dealers receiving RFQs from different clients to have a good estimate of the expected level at which the other dealers are willing to sell the security to avoid mis-evaluating the offer price. This can in theory be accomplished by having a good estimate of the fair value of the asset in question. However, it is difficult to estimate the fair value of the asset if the asset is illiquid. If a participant is able to infer the utilities, and by extension, the expected offer prices of each of the other dealers in the auction, they can narrow down their uncertainty about the fair value of the asset and avoid mispricing it. In RFQs, apart from modeling competitors, it can be extremely valuable to be able to model clients. In this case data is labeled, as sell side firms (banks) are aware of their RFQ counter party (client). Performing efficient client tiering would allow to respond to client orders in an efficient manner, which is beneficial for both the client and the bank.

    Behavioural cloning, on the other hand, is very useful in trading applications where agents are able to demonstrate their behaviour rather than specify their policy directly or formulate their reward. The abundance of trading agent demonstration data stored by financial institutions makes this paradigm very interesting to explore and presents a unique opportunity to use machine learning for both regular and algorithmic trading applications.

\section{Our Contributions} \label{sec:contributions}

    \begin{enumerate}
        \item We build an agent-based model (ABM) consisting of four different trading agent archetypes following strategies common in practice and vary their behaviours to ensure a wide coverage of trading strategies.
        \item We assess the realism of our market configuration and constituent agents by comparing the statistical properties of the simulated market to those observed in real markets.
        \item We formulate our approach to opponent modeling in a limit order book as a supervised learning problem of identifying (classifying) agent archetypes explicitly designed in the ABM. To this end, we designed a feed-forward neural network classifier and compared its performance against other various classification models.
        \item We show how a similar model to that developed for the opponent modelling task can be used for behavioural cloning and compare the distribution of actions between the predicted and ground truth labels.
    \end{enumerate}
\section{Agent-based Model for trading in a Limit Order Book} \label{sec:abm_lob}

    Agent-based modeling (ABM) is a bottom-up approach to building synthetic multi-agent financial markets composed of various types of trading participants. In these models, the agents are reactive to new unseen scenarios allowing them to perform counterfactual reasoning and answer various what-if scenarios. In our work, we utilize ABM as a method to construct a synthetic limit order book market composed of various trading agent archetypes, which we attempt to classify and clone their respective behaviour.

    Game theory~\citep{shoham2008multiagent, wooldridge2009introduction} provides a mathematical formalism for describing the complex behaviours that emerge from the different agent interactions and suggests tools to develop successful strategies. Nonetheless, strong assumptions are traditionally made about having access to reliable models describing the system dynamics, which is invalid even in the simplest of setups. For example, in financial markets designing a strategy that takes into account the impact of its decisions, is as difficult as solving the original problem itself, and heuristic approaches~\citep{gsell2008assessing} are not adaptive hence their quality degrades as the dynamics drift over time.

    \subsection{Simulator}

        We utilize and extend an open source discrete event simulation environment for our work \cite{byrd2019abides}. The simulation environment is designed for simulating trading agents in a continuous double auction market through a NASDAQ-like exchange agent with price-then-FIFO (first in, first out) matching rules. It also allows for the simulation of variable electronic network latency and agent computation delays, features that are specifically important for high frequency trading in exchange-traded markets. The agents communicate solely through a standardized message protocol similar to that used by NASDAQ. The environment is controlled by a simulation kernel which manages the flow of time and handles all the communication between the different agents in the simulation to request latest limit order book information, place market or limit orders, and cancel outstanding limit orders, etc.

    \subsection{Agents}

        Our agent population consists of a mixture of zero-intelligence (ZI) and strategic limited-intelligence agent archetypes. The ZI agents were originally introduced by \cite{gode1993allocative} to describe a family of trading agents submitting orders in the market in a random fashion without basing the decisions on knowledge of the current state of the limit order book. Interestingly, it was shown in this work that the allocative efficiency of a market arises from its structure and not the particular strategy or intelligence of its participants.

        The strategic limited-intelligence agents in our work are based on the authors knowledge of the types of trading agents acting in real exchange-traded markets. We refer to the zero-intelligence agents as background agents and define three archetypes of strategic limited-intelligence agents: market makers, market takers and directional traders which we explain in detail in this section.

        \subsubsection{\textbf{Background Agents:}}

            Our background ZI agents consist of two agent types: (1) \emph{Noise} agents which closely resemble the agents introduced in \cite{gode1993allocative} and (2) \emph{Value} agents which base their decisions on an exogenous price time series.

            The noise agents represent traders participating in the market once a day \cite{kyle1985continuous} by placing a market order and their participation time is sampled from a $U$-quadratic distribution over the interval $[t_{\text{open}}, t_{\text{close}}]$, where $t_{\text{open}}$ and $t_{\text{close}}$ are the start and end of the trading day, respectively. This is designed to achieve higher trading activity at the beginning and at the end of a given trading day reflecting traders' propensity to be more active at these times, a pattern observed in real limit order books \cite{Dacorogna1993AGM}.

            The value agents trade based on an exogenous time series representing the agents' belief of the true fundamental value of the asset they are trading \cite{wang2017spoofing, wah2017welfare}. In our work, we define this time series to be the historical intra-day price of a specific stock trading in NASDAQ.

            \begin{algorithm}
                \DontPrintSemicolon
                \caption{Noise Agent trading strategy}
                \label{alg:noise_agent}
                \KwData{quantity $q$, simulation start time $t_{\text{sim\_start\_time}}$, simulation end time $t_{\text{sim\_end\_time}}$, market open time $t_{open}$, market close time $t_{close}$}
                \SetKwProg{noiseagent}{NoiseAgent}{}{end}
                \noiseagent{$(q, t_{\text{sim\_start\_time}}, t_{\text{sim\_end\_time}}, t_{open}, t_{close})$}{
                Sample $t \sim \ensuremath\operatorname{UQuadratic}(t_{open}, t_{close})$\;
                \eIf{$t \in [t_{\text{sim\_start\_time}}, t_{\text{sim\_end\_time}}]$\ }{\ProgSty{wakeup\_at}$(t)$\;}{\Return}
                Sample $\epsilon \sim \ensuremath\operatorname{Uniform} \{+1,-1\}$\;
                \ProgSty{place\_market\_order}$(\epsilon \cdot q)$\;
                }
            \end{algorithm}

            \begin{algorithm}
                \DontPrintSemicolon
                \caption{Value Agent trading strategy}
                \label{alg:value_agents}
                \KwData{Arrival rate $\lambda_a$, fundamental value series $\{r_t\}_{t > 0}$, spread depth $\delta_s \in \mathbb{N}$, order size $q$, inside spread probability $\xi \in [0,1]$, observation noise variance $\sigma_n$}
                \SetKwProg{valueagent}{ValueAgent}{}{end}
                \valueagent{$(\lambda_a, \{r_t\}, \delta_s, q, \xi, \sigma_n )$}{
                    \While{\ProgSty{market\_open}$()$}{
                        $t \leftarrow $ \ProgSty{current\_time}$()$\;
                        $\widehat{r}_t \leftarrow$ \ProgSty{update\_fundamental\_estimate}$(r_t, \sigma_n)$\;
                        $b_t, a_t \leftarrow$ \ProgSty{get\_best\_bid}$()$, \ProgSty{get\_best\_ask}$()$\;
                        $m_t := \frac{1}{2} (a_t + b_t)$\;
                        $s_t := a_t - b_t$\;
                        Sample $X \sim \ensuremath\operatorname{Bernoulli}(\xi)$\;
                        \eIf
                        {$X = 1$}
                        {
                        $\Delta_{\text{max}} \leftarrow s_t \cdot \delta_s$\;
                        Sample $\Delta \sim \ensuremath\operatorname{Uniform}\{0, 1, \ldots, \Delta_{\text{max}}\}$
                        }
                        {$\Delta \leftarrow 0$}
                        \eIf
                        {
                            $m_t < \widehat{r}_t$
                        }
                        {
                            $p \leftarrow b_t + \Delta$\;
                            \ProgSty{place\_limit\_order}$(q, p)$
                        }
                        {
                            $p \leftarrow a_t - \Delta$\;
                            \ProgSty{place\_limit\_order}$(-q, p)$
                        }

                        Sample $t' \sim \ensuremath\operatorname{Exponential}(\lambda_a)$\;
                        \ProgSty{wakeup\_at}$(t + t')$
                    }
                }
            \end{algorithm}

        \subsubsection{\textbf{Market Maker Agents:}}

            These play a central role in financial markets by providing liquidity allowing buyers or sellers to find a match for their orders. We implement a simple market maker in a similar fashion to that in \cite{wah2017welfare, chakraborty2011market} that utilizes both the order book information and exogenous fundamental time-series and places orders on both sides of the limit order book with varying order sizes and participation rates.

            \begin{algorithm}
                \DontPrintSemicolon
                \caption{Market Maker Agent trading strategy}
                \label{alg:market_maker_agent}
                \KwData{Fundamental value series $\{r_t\}_{t > 0}$, Max arrival rate $\lambda_a$, maximum order size $q_{\text{max}}$, maximum spread depth $\delta_s \in \mathbb{N}$}
                \SetKwProg{marketmakeragent}{MarketMakerAgent}{}{end}

                \marketmakeragent{$(\{r_t\}, \lambda_a, q_{\text{max}}, \delta_s)$}{
                    \While{\ProgSty{market\_open}$()$}{
                        $t \leftarrow $ \ProgSty{current\_time}$()$\;
                        \ProgSty{cancel\_all\_orders}$()$\;
                        $b_t, a_t \leftarrow$ \ProgSty{get\_best\_bid}$()$, \ProgSty{get\_best\_ask}$()$\;
                        $m_t := \frac{1}{2} (a_t + b_t)$\;
                        $\widehat{r}_t \leftarrow$ \ProgSty{update\_fundamental\_estimate}$(r_t)$\;

                        $p_t := \frac{1}{2} (m_t + \widehat{r}_t)$

                        sample $q_t \sim \ensuremath\operatorname{Uniform}\{1, \ldots, q_{\text{max}}\}$\;
                        sample $s_t \sim \ensuremath\operatorname{Uniform}\{1, \ldots, \delta_s\}$\;

                        {
                        \ProgSty{place\_limit\_order}$(\frac{1}{2} q_t, p_t - s_t)$\;
                        \ProgSty{place\_limit\_order}$(-\frac{1}{2} q_t, p_t + s_t)$\;
                        }
                        Sample $t' \sim \ensuremath\operatorname{Exponential}(\lambda_a)$\;
                        \ProgSty{wakeup\_at}$( t + t')$\;
                    }
                }
            \end{algorithm}

        \subsubsection{\textbf{Market Taker Agents:}}

            These agents place large orders over a period of time consuming liquidity from the order book. In our experiments, we developed two types of market taker agents adapted from common trading practices. The first \textit{TWAP agent}, aims to execute the large order at a price as close as possible to the time weighted average price. It does this by breaking up the large order into smaller orders of equal size and dynamically placing these orders in the market using evenly divided time slots during a pre-defined time window. The second type of market taker agent, \textit{VWAP agent}, uses a similar approach. However, the size of the orders placed at each point in time is weighted against the historical traded volume of the stock. Variations of both strategies are heavily utilized in practice by trading participants wanting to buy or sell large orders on behalf of clients.

        \subsubsection{\textbf{Directional Trader Agents:}}

            These agents represent traders that use technical analysis to predict the direction of the stock price. In our work, we developed two simple agents; the first, \textit{mean-reversion agents}, assume that prices revert back to a mean value over a given time window. The second, \textit{momentum traders} assume that strong movement in prices in one direction are likely to be followed by movement in the same direction. Both strategies implemented compare two simple moving averages over different time periods (20 minutes and 50 minutes) and trade accordingly. In the case of the momentum agents, the agent places a buy order of a random size if the former moving average exceeds the latter and a sell order otherwise.

    \subsection{Configuration and Stylized Facts}

        Our market configuration consists of 5100 background agents (5000 noise agents and 100 value agents), 3 market makers, 6 market takers (3 TWAP agents and 3 VWAP agents) and 10 directional traders (5 momentum traders and 5 mean-reversion traders). We utilize the simulator's variable network communication properties and use a default computational delay of 50 nanoseconds and a pairwise latency between the agents and the exchange equivalent to the distance between New York and Seattle. In our experiments, we use historical mid-prices of J.P. Morgan Chase's as the fundamental value the agents observe and run the simulation across one week worth of NASDAQ data between 09:30 AM and 16:00 PM which are the market open and close times of this exchange.

        In order to assess the realism of our simulation, we compare a number of its' statistical properties (referred to as \emph{stylized facts}) to those observed in real markets. \cite{bouchaud2018trades} provides a detailed overview of these statistical properties covering stylized facts about asset price return distributions, order volumes and order flow. Figures \ref{fig:one_min_returns} and \ref{fig:ten_min_returns} show the distribution of daily asset price returns for the simulation dataset we used with the minutely returns displaying fat tails that become slimmer when the period over which the returns are calculated is increased. This matches the statistical properties observed in real market data according to \cite{bouchaud2018trades}.

        \begin{figure}[H]
             \centering
             \subfloat[One-minute log returns]{\includegraphics[width=0.23\textwidth]{figures/minutely_returns.jpg}}\label{fig:one_min_returns}
             \hfill
             \subfloat[Ten-minute log returns]{\includegraphics[width=0.23\textwidth]{figures/aggregation_normality.jpg}}\label{fig:ten_min_returns}
             \caption{Log returns distribution}
        \end{figure}

\section{Classifying Trading Agent Archetypes} \label{sec:om}

    \subsection{Overview}

        We formulate our approach to opponent modeling in a limit order book as a supervised learning problem of identifying (classifying) agent \emph{archetypes} explicitly programmed in the simulator, a task which can be cast in the auxiliary tasks framework \cite{jaderberg2016reinforcement, hernandez2019agent} for semi-supervised reinforcement learning. Using a classification approach for opponent modeling \cite{weber2009data} allows for predicting properties of the other agents as opposed to predicting the future actions of the agents, which can be useful in a range of market scenarios discussed in our work.

    \subsection{Model}

        The classification model used is a simple feed-forward neural network (see figure \ref{fig:nn_arch}) that attempts to infer the agent archetype given the limit order book state and the agent's order. The state of the limit order book is given by the z-score normalised ask and bid prices and volumes for the first 5 price levels. The agent's order is represented by the direction of the order (buy or sell), its price and size. The model consists of 4 hidden layers with 256, 1024, 1024 and 1024 units respectively. In order to reduce over-fitting on the training data, we add a drop out layer after each hidden layer. We used a mixture of ReLU and sigmoid activation functions, an ADAM optimizer to update the network weights based on the training data and ran the model for 200 epochs. Finally, we used an open-source hyper-parameter optimization framework \cite{bergstra2013making} to find the best parameters to use for our model and dataset.

        \begin{figure}[!htb]
             \centering
             \includegraphics[width=0.13\textwidth]{figures/nn_arch.png}
             \caption{Neural network model architecture diagram}
             \label{fig:nn_arch}
        \end{figure}

        \begin{table*}[!tb]
        \small
            \begin{tabular}{c | c c c | c c c | c c c | c c c}
                \toprule
                 &
                \multicolumn{3}{c}{\textbf{Market Maker}} & \multicolumn{3}{c}{\textbf{Market Taker}} & \multicolumn{3}{c}{\textbf{Directional Trader}} & \multicolumn{3}{c}{\textbf{Background Agent}} \\

                 & Precision & Recall & F-score & Precision & Recall & F-score  & Precision & Recall & F-score  & Precision & Recall & F-score \\
                 \hline
                 KNN & 0.55 & 0.69 & 0.61 & 0.47 & 0.66 & 0.55 & 0.41 & 0.29 & 0.34 & 0.41 & 0.26 & 0.32 \\
                 \hline
                 Linear SVM & 0.73 & 0.56 & 0.64 & 0.36 & 0.24 & 0.29 & 0.41 & 0.25 & 0.31 & 0.31 & 0.61 & 0.41 \\
                 \hline
                 RBF SVM & 0.51 & 0.80 & 0.63 & 0.61 & 0.54 & 0.57 & 0.42 & 0.35 & 0.38 & 0.44 & 0.34 & 0.38 \\
                 \hline
                 Decision Tree & 0.99 & 0.47 & 0.64 & 0.69 & 0.70 & 0.70 & 0.55 & 0.85 & 0.67 & 0.67 & 0.64 & 0.65 \\
                 \hline
                 Random Forest & 0.44 & 0.61 & 0.51 & 0.40 & 0.65 & 0.50 & 0.38 & 0.25 & 0.30 & 0.53 & 0.21 & 0.30 \\
                 \hline
                 AdaBoost & 0.80 & 0.59 & 0.68 & 0.78 & 0.69 & 0.73 & 0.57 & 0.84 & 0.68 & 0.70 & 0.63 & 0.67 \\
                 \hline
                 Naive Bayes & 0.94 & 0.47 & 0.63 & 0.18 & 0.03 & 0.06 & 0.41 & 0.92 & 0.56 & 0.57 & 0.57 & 0.57 \\
                 \hline
                 \textbf{Our model} & 0.76 & 0.75 & 0.75 & 0.70 & 0.71 & 0.71 & 0.60 & 0.63 & 0.61 & 0.68 & 0.63 & 0.65 \\
                 \bottomrule
            \end{tabular}
            \label{tab:tab_om_results}
            \caption{Opponent modelling classification results}
        \end{table*}

        \begin{figure*}[!tb]
            \centering
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_NearestNeighbors.png}
                \caption{KNN}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_LinearSVM.png}
                \caption{Linear SVM}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_RBFSVM.png}
                \caption{RBF SVM}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_DecisionTree.png}
                \caption{Decision Tree}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_RandomForest.png}
                \caption{Random Forest}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_AdaBoost.png}
                \caption{AdaBoost}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/om_jpm_NaiveBayes.png}
                \caption{Naive Bayes}
            \end{subfigure}
            \begin{subfigure}[b]{0.3305\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/nn_09_10_09_02_cm.png}
                \caption{Our model}
            \end{subfigure}
            \label{fig:fig_om_results}
            \caption{Baseline classification models confusion matrices}
        \end{figure*}

    \subsection{Experiments and Results}

        We run our ABM using fundamental values covering five trading days for one stock and produce labelled synthetic market data that we use offline for the opponent classification exercise. An issue arises regarding the different time scales that each agent archetype operates on. For example, some archetypes place substantially more orders than others, leading to the data set we generate having high imbalance between the classes. We produce a balanced dataset by simply down-sampling the over-represented classes.

        We train our classifier on data for 3 days and evaluate its performance on the subsequent 2 days. Figure 6 shows the confusion matrix for the four trading archetypes in our ABM model. We compare the performance of our model against a range of classifiers and show the precision, recall and F1-score associated with each agent archetype in table 1.

        The confusion matrix for our model shows relatively good performance when looking at the true positives. Comparing the precision, recall and F1-scores of our model against the baseline models shown in table 1, we see mixed performance indicating an ensemble model is most suited. Our model performs best in identifying the market makers and market takers and displays lower performance identifying the directional and background traders. It is important to note that the down-sampling of the data and the lack of long term memory in the neural network architecture plays a significant part in this. For example, the directional traders developed rely on past observations of the order book to place their orders that our model and data sampling methodology makes worse. On the other hand, we see the ensemble AdaBoost model \cite{hastie2009multi} performing well confirming our hypothesis about the need for an ensemble model. The AdaBoost model \cite{hastie2009multi}, however, does not show the same performance for the market makers compared to our model.
\section{Behavioural Cloning of Trading Agent Archetypes} \label{sec:bc}
    \subsection{Overview}

        Using the same labelled synthetic data produced by the ABM model, we formulate our approach to behavioural cloning of the agents as a supervised regression problem of learning the agents' order price and size given observations of the limit order book. This approach ties well with the paradigm taken in the previous classification exercise and allows us to use the same dataset but change the inputs and labels of our model.

    \subsection{Model}
        We use the same neural network architecture shown in figure \ref{fig:nn_arch} but change the last layer of the neural network to output a vector of z-score normalized order prices and sizes. Instead of training the model on the entire dataset, we train it only on the observations and actions of each agent archetype. In this model, we also used the hyper-parameter optimization framework \cite{bergstra2013making} to find the best parameters to use for our model and dataset for each agent archetype.

    \subsection{Experiments and Results}
        We train our regression model on data specific to each agent archetype for 3 days and evaluate its performance on the subsequent 2 days. Our results indicate the possibility of predicting the order prices and sizes with room for improvement. Figures \ref{fig:bc_mm}, \ref{fig:bc_mt}, \ref{fig:bc_dt}, \ref{fig:bc_bt} show the univariate distributions of the predicted and ground truth data of both order prices and sizes as well as a kernel density estimate for better visualisation. In almost all cases, we see non symmetric distributions suggesting the need for a more complex regression model.

        \begin{figure*}[!tb]
            \centering
            \begin{subfigure}[b]{0.48\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/bc_mm.png}
                \caption{Market Makers}
                \label{fig:bc_mm}
            \end{subfigure}
            \begin{subfigure}[b]{0.48\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/bc_mt.png}
                \caption{Market Takers}
                \label{fig:bc_mt}
            \end{subfigure}
            \begin{subfigure}[b]{0.48\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/bc_dt.png}
                \caption{Directional Traders}
                \label{fig:bc_dt}
            \end{subfigure}
            \begin{subfigure}[b]{0.48\textwidth}
                \centering
                \includegraphics[width=\textwidth]{figures/bc_bt.png}
                \caption{Background Agents}
                \label{fig:bc_bt}
            \end{subfigure}
            \caption{Distribution of order prices and sizes for the cloned agent archetypes}
        \end{figure*}

redpajama_set_name: RedPajamaArXiv


The severe power deficit and increasing power tariffs have seen many companies seeking to source power from renewable sources. With more than 28 GW of installed capacity, renewable energy is well placed to power Indian industries.
Procuring Renewable Energy may, however, be a daunting task due to the number of competing technologies, avenues for procurement available, and the regulations governing the process. This paper examines the various technical, economic, and regulatoryaspects to be considered when deciding your renewable energy procurement strategy.


Many people remember the day when Chrysler introduced 2011 model year, the luxury midsize sedan – Chrysler 200. In fact it was the renaming and slightly modified Sebring. Now, the company is working on its replacement.


text:
Новопучкаково () — деревня в Чекмагушевском районе Республики Башкортостан Российской Федерации. Входит в состав Юмашевского сельсовета.

География

Географическое положение
Расстояние до:
 районного центра (Чекмагуш): 44 км,
 центра сельсовета (Юмашево): 12 км,
 ближайшей ж/д станции (Буздяк): 75 км.

Население

Примечания

Ссылки
 Совет муниципальных образований Республики Башкортостан .

Населённые пункты Чекмагушевского района

redpajama_set_name: RedPajamaWikipedia


Pipeline management presents challenges that are quite unique. Their long length, high value, high risk and often difficult access conditions require continuous monitoring and optimizing maintenance interventions. The main concern for pipeline owners involves leaks that if not detected can severely impact the environment or render the pipeline out of service.
Leakages can be detected and localized using distributed fiber-optic temperature sensors. Distributed Temperature Monitoring can also detect third-party tampering and non-authorized work before any damage is done to the pipeline. Additionally, pipeline strain distribution and soil movement can be detected and localized using our distributed strain monitoring systems.
We are a leading expert in monitoring pipelines. We have instrumented pipelines of all types world-wide, let us support your next pipeline monitoring project. We offer a wide variety of sensors using leading-edge technology and can assist you in identifying the best products for your project and budget.
Integrated solutions Deliver integrated monitoring solutions for pipelines using technologically advanced distributed fiber-optic sensor cables, measurement instruments and software.
Efficiently employ targeted inspections and repairs Identify specific issues with unparalleled accuracy by identifying leaks, intrusions and deformations within the accuracy range of one meter over the entire length of the pipeline.
Continuous monitoring 24/7 Reduce down-time by employing systems that operates continuously and autonomously to provide alerts as soon as they are detected.
Reduce economic, environmental, and social liabilities Prevent severe environmental consequences caused by leaks and reduce insurance costs.
Assessing pipeline safety immediately after a major event Evaluate possible damage and failures caused by extraordinary events such as earthquakes, third party tampering, explosions or floods.
Proven reliability Provide pipeline monitoring systems that have been successfully installed and operated on pipelines around the world.


FanFaire, Block of the Month is a six-month block of the month. Each block is made twice totaling twelve blocks.
Every block teaches you a new way to use the Creative Grids 60 Degree Diamond Ruler, designed by Krista Moser.
Finished Size 66" x 79".


text:
Napolean's Toothbrush Now on Display
Britain's newest medical museum features Charles Darwin's walking stick, Napoleon's toothbrush and anatomical drawings by Leonard da Vinci.
The $60 million Wellcome Collection in London is filled with items collected by Henry Wellcome, a pharmaceutical tycoon in the late 19th and early 20th centuries.
When he died in 1936 at the age of 83, he had gathered some 1 million objects, The New York Times said Wednesday.
James Watson, the Nobel prize winner scientist who co-discovered the structure of DNA was at the opening this week, The Guardian newspaper said.
Where else can you browse the more than 3 billion letters of the human genome, see one of the robots that helped to sequence it and contemplate the reactions of contemporary artists to this major scientific development? In the pioneering Wellcome Collection — how wonderful!, Watson said.
Wellcome Trust director Mark Walport said the museum includes a forum that will host debates encouraging people to really think and be challenged by important contemporary issues around biomedicine.
Via: Webindia
Stuttering Myths Revealed
Large Lake in..


The craze of playing official MOBA style games keep on increasing among the game lovers from all around the earth. There are plenty of games present but Mobile Legends is the perfect option for those who love to play action-packed games with their friends. This game has lots of marvelous features which will hook up the players for a long time. This is not all about taking part in battles or kill enemies, it also includes lots of other things such as choosing the best heroes, buying special items from the game shop by getting chests and much more. All these things are making the game appealing to the players and it also encourages them to download it. The most amazing feature of the game is that it is free to download and anyone can easily download it from the on-device app store to start playing it.
If you are going to play this game then it is important to understand its core concepts which will help you to play it in a perfect manner. Well, the concept of this game is very simple but you should know more about it to ease up your work. In this game, players have to pay proper attention to the selection of heroes and building up a strong team and also on buying several items from the game store. When you will start playing this game then you will find a tutorial which is the special feature added by the developers for the convenience of the beginners. It will help the beginners to know more about the basics and how to control the heroes in the game. After this, you can play the game with ease and also enjoy the gameplay to have unlimited fun in your free time.
How to play the game with perfection?
The game is simple to play and you can also become a master in this game after playing it on regular basis. You have an option to play it whenever you want on your mobile device and also in the comfort of your home. You should focus on choosing the right heroes which will help you to buy them to create a good team to take part in 5vs5 battles. You can earn battle points by taking part in battles and you also don't need to worry about the outcome of battles. It doesn't matter you win or lose, you will definitely earn battle points which is the primary currency of the game. you should try to win the battles which will help you to earn more battle points instead of losing it. with the help of earning battle points, players can buy their desired heroes, resources and gears from the game shop in order to become advance in the game.
Mobile Legends is a perfect game with unique features and vivid graphics that will make you addicted. There are numerous features present that are really interesting which are also making the game attractive for the game players. If you don't have enough time to play the game on regular basis then you should log in the game on a daily basis which will help you to get chests. There are two types of chests present and every chest has its significance. You can win two chests in a day and for this, you have to log in the game. This can help you to win lots of rewards in the form of in-game currencies and resources that will make your tasks simple and easy. By playing the game, players can enjoy their free time and also flavor up their game experience.


text:
New production of Martin McDonagh's The Pillowman is delayed
Published 5 June 2020
The Pillowman, written by the multi Olivier, BAFTA and Academy Award-winning playwright and screen-writer Martin McDonagh, which was due to make it West End premiere this Summer, is to be delayed.
The Pillowman was due to begin performances at the Duke of York's Theatre on Friday 24 July, for a 12-week season. Due to the current global situation, the production is now postponed and new season details and all further information will be announced at a later date.
Ticket holders do not need to do anything, the point of purchase will be in touch with you soon.
McDonagh and director Matthew Dunster said: "Whilst we are naturally disappointed that The Pillowman can't go ahead this Summer, we are fully committed to realising this new production in the near future. We have an extraordinary cast and creative team and can't wait to bring this new vision of the play to audiences."
The Pillowman will star Aaron Taylor-Johnson (Golden Globe Award winner,Nocturnal Animals) and Steve Pemberton (Bafta Award winner, The League of Gentlemen).
Taylor-Johnson said: "I'm excited to hold the promise of a return to theatre, a place of magic, creativity and Unity."
Pemberton said: "It's been a dark time for the performing arts and I can't wait to have the theatres open again. Unfortunately The Pillowman has to be put to bed for now, but I very much hope that we'll be able to bring Martin's dazzling play to the West End before too long."
We look forward to hearing new dates from The Pillowman soon. Current ticket holders will be contacted by their point of purchase.
aaron taylor johnson martin mcdonagh matthew dunster steve pemberton the pillowman


text:
Strellson Sells Clothes For Facebook Likes
The campaign is intended to go viral
The Swiss clothing brand Strellson has announced that it is to begin selling its products in return for Facebook likes. The Like Shop is part of the company's main website, but the items cannot be bought with currency. In order to buy an item, a user must share the piece of clothing on Facebook with a personalised message, and if a specified number of likes is surpassed, the item will be sent out to them. Currently, there are 14 items included in the shop, with a polo shirt costing 50 likes and a leather jacket costing 500 likes. The agency behind the campaign says that it is intended to "achieve a viral effect using like requests and post-sharing" to broaden the community and brand recognition.


text:
Report: Jared Kushner, Ivanka Trump Scale Back Roles amid Increasing Pressure
Win McNamee/Getty Images
Jared Kushner, President Trump's senior adviser, and daughter Ivanka Trump are reportedly shrinking their West Wing roles as the pair comes under increasing pressure and annoys top officials with their presence and terrible advice to the president.
According to Axios, the couple is making more of an effort to "stay in their lane" due to their creating "periodic confusion and resentment" by their presence. White House counsel Don McGahn has reportedly complained about the couple's presence, while Secretary of State Rex Tillerson is rumored to have nicknamed them "the royal family," according to the outlet.
The decision comes after Trump's decision to back Sen. Luther Strange in the Alabama Senate primary backfired spectacularly and Strange was crushed by conservative challenger Judge Roy Moore. Kushner is widely believed to have been a key voice in urging Trump to get involved in the race and back Strange — a decision Trump himself admitted may have been a "mistake."
It is not the first piece of bad advice Kushner has given. He was also reportedly central in advising Trump to fire FBI Director James Comey — a move that led to the appointment of FBI special counsel Robert Mueller to probe alleged Russian interference in the election.
Kushner is now under the spotlight in that investigation, too, in part due to his attendance at a Trump Tower meeting between Donald Trump Jr. and a Kremlin-linked lawyer last year.
Federal investigators reportedly subpoenaed his family firm, Kushner Companies, this summer for its use of an investment-for-visas program — a program it hawked to Chinese investors and later apologized for its naming of President Trump in its pitches.
Just last week, Kushner was revealed to have been one of six current and former Trump White House officials to have occasionally used private email for White House business — triggering interest from congressional lawmakers.
Axios cites "the couple's friends," who say that the pair sees themselves as under constant attack and that they have never planned to stay four years in the administration, possibly a hint that a departure from the Manhattanite couple may come sooner rather than later.
Until then, under the tighter discipline imposed by White House Chief of Staff John Kelly, the pair is expected to stick to their respective portfolios.
"Now there are proper channels, and they're just doing what they're supposed to be doing," a colleague told Axios.
Adam Shaw is a Breitbart News politics reporter based in New York. Follow Adam on Twitter: @AdamShawNY.
PoliticsDonald TrumpIvanka TrumpJared Kushner


text:
#ifndef __ipxfltdf_h
#define __ipxfltdf_h

typedef struct _IPX_TRAFFIC_FILTER_GLOBAL_INFO {
  ULONG FilterAction;
} IPX_TRAFFIC_FILTER_GLOBAL_INFO,*PIPX_TRAFFIC_FILTER_GLOBAL_INFO;

#define IPX_TRAFFIC_FILTER_ACTION_PERMIT 1
#define IPX_TRAFFIC_FILTER_ACTION_DENY 2

typedef struct _IPX_TRAFFIC_FILTER_INFO {
  ULONG FilterDefinition;
  UCHAR DestinationNetwork[4];
  UCHAR DestinationNetworkMask[4];
  UCHAR DestinationNode[6];
  UCHAR DestinationSocket[2];
  UCHAR SourceNetwork[4];
  UCHAR SourceNetworkMask[4];
  UCHAR SourceNode[6];
  UCHAR SourceSocket[2];
  UCHAR PacketType;
} IPX_TRAFFIC_FILTER_INFO,*PIPX_TRAFFIC_FILTER_INFO;

#define IPX_TRAFFIC_FILTER_ON_SRCNET 0x00000001
#define IPX_TRAFFIC_FILTER_ON_SRCNODE 0x00000002
#define IPX_TRAFFIC_FILTER_ON_SRCSOCKET 0x00000004
#define IPX_TRAFFIC_FILTER_ON_DSTNET 0x00000010
#define IPX_TRAFFIC_FILTER_ON_DSTNODE 0x00000020
#define IPX_TRAFFIC_FILTER_ON_DSTSOCKET 0x00000040
#define IPX_TRAFFIC_FILTER_ON_PKTTYPE 0x00000100
#define IPX_TRAFFIC_FILTER_LOG_MATCHES 0x80000000

typedef struct _FLT_IF_SET_PARAMS {
  ULONG InterfaceIndex;
  ULONG FilterAction;
  ULONG FilterSize;
} FLT_IF_SET_PARAMS,*PFLT_IF_SET_PARAMS;

typedef struct _FLT_IF_GET_PARAMS {
  ULONG FilterAction;
  ULONG FilterSize;
  ULONG TotalSize;
} FLT_IF_GET_PARAMS,*PFLT_IF_GET_PARAMS;

typedef struct _FLT_PACKET_LOG {
  ULONG SrcIfIdx;
  ULONG DstIfIdx;
  USHORT DataSize;
  USHORT SeqNum;
  UCHAR Header[30];
} FLT_PACKET_LOG,*PFLT_PACKET_LOG;
#endif

redpajama_set_name: RedPajamaGithub


text:
Q: Function parameter turns into Object's Name instead of its reference this is my JSON structure:
var json = {
procedure: {
    id: "content",
    child: {

        154: {
        class: "sequence nest",
        text: "LED Ausgeben",
        id: 154
        }
    }
}
};

For some reason prop in for (var prop in scope.child){} is only a string instead of the object scope.child.154 as you can see below at Watch Expressions.

any ideas why? i tried specifying JSON objects with "154" but it doesn't matter. do their names have to contain characters aswell?
here is the fiddle: JSFiddle but you have to use the browsers console. to check it, press on the folder icon.
cheers!

A: I changed the for loop slightly. You should have access to the object doing it this way.
for (var key in scope.child) {
  var obj = scope.child[key];
  ...
}

Fiddle
Also check out Stackover post where I got the idea from

redpajama_set_name: RedPajamaStackExchange


Read more about Bremen, our tips and tricks and much more.
We've compiled a list of some of Europe's very best destinations for beer-lovers. Cross these beauties off your Beer Bucket List….
Have you been to Bremen? Have you even thought about going? You really should. For a little inspiration, here's a list of ten cool things to see, eat and do there that cost under a tenner each.
What to do in Bremen?
The fairytale location - is known for its history and traditions, as well as for its cosmopolitan outlook. But Bremen is also the eleventh largest city in Germany, a European centre for aerospace, a city of science and home to the Überseestadt district, one of the largest urban development projects in Europe. Bremen is a destination full of surprises – there's always more to discover.
As well as the historical sights to be explored in the Old Town like the stunning Bremen Rathaus (Town Hall), Bremen offers exciting modern attractions to appeal to visitors of all tastes with its vibrant nightlife scene. Book your Bremen flights today with Ryanair – Europe's first and largest low fares airline!
Tour Beck's Brewery and even sample some of the local produce with a beer tasting session.
Enjoy the beautiful scenery in the Bürgerpark right in the middle of Bremen – enjoy mini golf, a boating lake or take the kids to the playground.
Take in an exciting football game at Weser Stadium, home of Werder Bremen.
Visit the breath-taking Bremen Rathaus (City Hall), a certified UNESCO World Heritage Site.
Sample some of Bremen's famous cuisine and nightlife – the cafés and restaurants in the Schlachte region along the Weser river are particularly popular.
Alongside its cultural attractions, Bremen is also a fantastic place to go shopping. From the boutique stores in the Schnoor area to the shops, restaurants and amenities in the Waterfront Shopping Centre, families can enjoy a great day out in the city. Book cheap flights with Ryanair and skip the queue with the Plus programme to get Priority & 2 Cabin bags .
Click here to rent a car in Bremen.


I've had larger offices in my career. At Charles Drew University in Los Angeles, the director's office had a gargantuan desk, a 10-seat conference table, and enough extra space to host a kiddie-league soccer match, with a concession stand in the corner.
But no office has ever felt quite so big as this one — the news editor's office at Hoch Publishing.
It's an office haunted by the kindly ghosts of editors past who left their indelible marks on the newspaper, the community, and Kansas journalism. Three of them — Edward W. Hoch, Wharton Hoch, and Bill Meyer — are in the Kansas Journalism Hall of Fame. I feel like a rookie baseball player taking a spot on the bench in old Yankees Stadium, where Babe Ruth, Lou Gehrig, and Joe DiMaggio once sat.
Bill was my mentor, and had a genuine knack for teaching. Bill taught me about more than burros. He taught me what good community journalism is all about. Perhaps he taught me too well, for I found journalism school in college too boring and negative, and abandoned it for a major in education.
Now, after more than a quarter-century in education, I'm back, excited with my new career direction as news editor of our three publications and eager to bring you the stories and pictures that tell the tale of community life in our remarkable Midwest county. That's tale as in story, vs. tail on the end of a burro.
Twenty years ago, a kid by the name of Derek Jeter took a spot on the Yankee bench, and he's carved out a place alongside Ruth, Gehrig, and DiMaggio in Bronx Bomber lore. He embraced the legacy but made his own mark.
I'm not a journalist the likes of a Hoch or Meyer, but I understand and embrace the legacy of community journalism they established that's served the people of this county well for more than 140 years.
Now it's my turn to burrow in at the plate and start swinging. And if I'm lucky, I won't make a burro out of myself while doing it.


text:
Luna Kafé article
Of Incense
European Tour 1998 (1)
Won't you, won't you come with us on a psychedelic holiday, sang my friends in The Smell Of Incense, and without too much hesitation I grabbed them by the words. The first gig was to be in a youth club in Berlin called Die Linse, which sounded all right to a vegetarian like me (Linse = lentil).
Most of the recorded output of The Smell Of Incense has been released by the German label September Gurls. It includes two albums and singles and half a 10" EP (the second album was released last summer and pre-reviewed at Luna at an early stage). Because of good reception to all their releases, they've had an offer for several years from the people of the Belgian psychedelic magazine Crohinga Well to do a low budget tour. Now finally they decided to make the great leap. Work and parental obligations had to be sorted out, a mini bus to contain eight people, lots of instruments and gear was hired. The tour was to include Germany and Belgium, and a gig in the Netherlands in addition. They were not sure how many gigs they'd play until the last days before departure. And they didn't have any written agreements (contracts if you like); Smell Of Incense is not a professional band. They set out on a rainy Thursday evening in the middle of October from their home town of Arendal, picked up a couple of the guys living in Oslo and left Norway the next morning. I went by train the following evening.
Time to present the Smelly Vincents: Lumpy Davy (guitars and vocals), Cool Kat (drums and sneezes) and Han Solo (bass and vocals) started the band about 12 years ago. Ernie Chung (guitars, vocals and brass) and Bumble B (vocals and fiddle) joined a few years later and naturally expanded the sound. On this tour they also brought along semi-permanent members Brt Blaster (voice and spacebubbling synth), Mickey Moog (keyboards, brass and vocals) and producer/soundman Bjorn F. The latter inclusions were very wise moves. I guess they would've had huge problems to get any sound, at least any decent sound, if it hadn't been for Bjorn F. in particular. The music is so-called retro, a blend of their own psychedelic and progressive songs and cover versions of highlights from the psychedelic 60s and progressive 70s such as The Smell Of Incense by The West Coast Pop Art Experimental Band, A Visit With Ashiya by Merrel Fankhouser's HMS Bounty, Why Did I Get So High by Peanut Butter Conspiracy, Gong's What Do You Want and Magick Brother etc. These are not pale blueprints of the originals. The band tries to interpret them their own way. For instance, Incredible String Band's acoustic Witch's Hat is turned into a full blown electric folk-rocker, Kinks' Fancy (not part of the tour set) is taken to the extremes: a raga rock number with sitar and all, whereas with Pink Floyd's Interstellar Overdrive, they've only kept the opening and closing riff of the original. The 15 plus minute middle parts used to sound closer to Gong earlier, I think, now it's something else, probably their own sound. That's why they call it (The Smell Of) Interstellar Overdrive.
FIRST WE TAKE BERLIN
My journey by train took about 17 hours via Copenhagen and Hamburg. After a lot of rain, it was a clear and sunny afternoon in Berlin, almost summer. Hooray! I took the underground from Bahnhof Zoo to a station nearby Die Linse in Lichtenberg in the far east of Berlin. And guess what; the band hadn't arrived yet. They should've been there half an hour earlier at 17, but of course had troubles to: a) find the club, and finally b) find a way to drive the minibus to the entrance of the club.
They turned up within an hour and had enough time to do a decent sound-check. I was told of an almost sleepless previous night and how the bus had stopped on a Swedish motorway. "Well, the engine is broken down, this is it, end of tour", they'd thought. It turned out they had only run out of gas, but the trip to the nearest gas station took at least an extra hour.
We got an excellent and simple vegetarian meal and a few excellent German beers at the club. The local opening act Frustration des Elch had put a contingent of friends and fans on the guest list, and their set of heavy-progressive kraut-rock was well received. I particularly enjoyed their organ sound. The only problem was that half the people left after they had finished... I won't tire you with endless descriptions of how The Smell Of Incense's music varied from evening to evening. Now, the first gig abroad went smoothly. The local PA system was the best during the tour, I guess, and the stage was big enough for the entire six men and one woman ensemble in spite of Mickey's range of keyboards including an old electric Fender Rhodes piano, the Mellotron and an extra mixing console. They started and ended the show with Bumble B singing some Norwegian folk songs on her own. Then straight into Czar's Tread Softly On My Dreams with lots of mellotron. They continued with most songs off their singles, a few from the first album and the suite From The Third Hemisphere off the second album. They also performed a very memorable version of Memories by Soft Machine, the best I've ever heard, and a 20 minute version of Hawkwind's space shanty Space Is Deep, including some atonal and improvised parts. I guess there was an encore before Bumble B ended the show at about 1.30 in the morning. Brt introduced most of the songs from his low lotus position at the right of the stage. But the band seldom started playing straight afterwards, they had to discuss, hesitate, sip beer etc. first, which goes to show they're an amateur band. Anyway, they turned more professional that way during the tour.
I guess the band was tense at first, but eased off after the first three or four songs. They've had far greater audiences back home, but was very well received by the handful of people who stayed until the end, especially Gert, the main organiser at the club, Henning who runs the Lollipop Shop record label, and Sabina who on (too) short notice organised the German part of the tour. She is a tour manager in her spare time, working with Man and Fantasyy Factoryy among others, but surely lost more money than she gained on this tour. It was decided to leave the equipment at the club. After some more beers it was time to drive to the house in Wedding (yes, very matrimonial!) at the other side of town where we should stay for the night. Sabina drove in front in her car to show the way. A wise move. We arrived there at about 4 in the morning. Time for some more drinking and chatting. I eventually went to sleep in the bus with Mr. Kat. In the end I managed to kick him into a snoreless position... Soon after, it seemed, Lump entered the bus to get some of his luggage. It was broad daylight, and one more sunny day in Berlin. But evil omens were looming in the distance...
The very hospitable Wedding people had occupied the house some 15 years ago. Now the relationship with the owners were well arranged and they had rented their separate flats for quite some time. We had a long, relaxed and nice Sunday breakfast in a huge kitchen. Some of the inhabitants were old friends of Gert at Die Linse. Kalle had been the organ player of a successful krautrock band in the early 70s when he was still a teenager. Now he rehearsed with a heavy-progressive trio that was not quite ready for the public yet. It was his wife's birthday this very day.
Sabina offered a sight-seeing tour of Berlin if it was time after the sound-check for tonight's gig. First we had to fetch the gear at Die Linse. Once more Sabina lead the way through the streets of Berlin, followed by the band in their bus. The expedition took about two and a half hours. It was a long way to drive and a lot to carry - including Cool Kat's fan (well, not that kind of rock'n'roll fan; one to keep him as cool as his name indicates, you know), video gear and psychedelic light equipment - and it was time for a photo session, too, under the rainbow. At 6 p.m. we turned up in front of the Bumerang club, as agreed! This was much closer to the city centre than the club we had just left. Bumerang was small, sort of a pub, the stage was too small for seven people. And it was no room for the mixing desk in front of the stage. Bjorn F had to stand behind the bar at the side of the stage. Brt was to sit at the public side of the bar with his hand-painted synth, and Bumble B had to stand in the steps to the stage part of the time. Anyway, the people at the club brought along a case or two of beer bottles and we got the equipment and instruments into the room and in correct positions within a couple of hours. Two boys calling themselves Family Freshlight (a well known local name, I believe) had been hired and brought along a complete psychedelic light show and occupied most of the rear end of the club. At 8.30 the sound- and light-check was finished. No time for sight-seeing, of course, but some of us drove back to the house in Wedding to change clothes while the rest remained in Bumerang to handle some small technical difficulties. We got some warm and nice fish soup in the kitchen. Not quite the Norwegian way as intended, but excellent it was. Back to the Bang-bang a-boome-boome-rang at 10 along with Kalle and Paul.
The club was crowded. Gert and Henning had turned up. Everything looked promising, but Bumble B was tense. She was catching a cold and ordered a cup of tea at the bar. Because of misunderstandings she didn't get it for some time and it was not as hot as it ought to have been when she finally received it. Anyway, the band had a good start with their Gong suite. A long and psychedelic instrumental part with glissando guitar and all, then folksy and whimsical at the end. Bumble B sang a couple of songs, but not as convincing as usual. She was on the verge of losing her voice. The set had to be changed, to spare her vocal chord, and it ended with Tread Softly which had been intended for the start after a break. Bjorn ran from behind the bar to the centre of the room now and again to adjust the sound. It was better than expected, Brt didn't even manage to make feed-back from his position close to one of the speakers!
The members of the band were not in the best of moods, and found out they had to include both (The Smell Of) Interstellar Overdrive and Space Is Deep for the second set. Almost an hour of instrumental space oddities and free-form freak-out which was very well received. Ernie and Mickey used their horns to great effect during the middle of Interstellar Overdrive. In between they played Put The Clock Back On The Wall by San Jose's E Types. The Smelly version sounds almost as a country-pop-rocker in a Byrds'ian way. Quite schizophrenic to squeeze in among the 20-30 minute instrumentals if you ask me, almost as schizophrenic as the first Pink Floyd album. The band was encouraged to do encores and started with the old I'm Allergic To Flowers by Jefferson Handkerchief (off the legendary Pebbles Vol. 3 album) from the band's early days. But they couldn't remember the song and had to give in. No problemo! Instead we got Tread Softly once more. Great playing and singing, great lights and slides. One of the highlights of the tour for yours truely which still keep flashbacking in my head once in a while. Bumble B tried to finish the evening with a Norwegian lullaby, but her voice gave in. A charming end of what turned out to be a successful evening. The boys with the lights got away with half of the money from the door, but the band didn't care too much at the time. They were busy to talk to people, sell a few records and get the equipment back into the bus. It was nearly 2 a.m.
Back in Wedding the birthday celebration continued. The most eager boys of the band kept going until 7.30 next morning. They found it somewhat hard to get up again at 10 as agreed. Anyway, everyone was ready to leave the premises at about 12.30. Bumble B and Lump also got to have a shower at the nearby bath and brought along a pile of goods from the local chemist's shop to cure the former's sore throat. And Han Solo and Lumpy Davy even washed the dishes after breakfast.
At 1 p.m. they set off for the next town of the tour, Celle north of Hannover. I went for some sight-seeing of Berlin at last, and followed by train later in the afternoon.
After a pleasant day in sunny Berlin and a comfortable journey by rail, I arrived in Celle at 9 in the evening. It was not hard to find the pub where the gig was to be. Celler Loch (meaning something like trap-door of the cellar) was in Bahnhofstrasse, i.e. Railway station street. I soon saw some familiar persons at the pavement. It turned out they had arrived only a few minutes before me. They had taken a wrong route out of Berlin and was well on their way to Leipzig (in the south) instead of Hannover (to the west of Berlin)... Also, Brt had been busy drinking in the bus and they had to get off the Autobahn at a turnoff a little too early, for him to pee, and of course they had difficulties once more to find the right direction afterwards. When they finally arrived in Celle after an 8 hour drive instead of 3-4 hours, the crowd at Celler Loch applauded in welcome. In fact the gig should have started at 7 and ended when the band arrived. Usually they only had live music on Sunday afternoons, and they expected a very relaxed Monday band with endless spheric sounds(!), according to the programme. There was a bedroom where children were sleeping at the first floor just above the pub. Or shall we say the small bar. It was small indeed, and crowded as hell. Bumble B had lost her voice completely, it started to rain, the band occupied about 1/3 of the room that made it even more crowded (and half the pavement outside). Perfect conditions! Lump wanted to call the gig off...
But wonder of all wonders! Before 10 they had got the most neccessary gear in position, managed to get some sound from the local antique amplifier for the voices and had a sort of sound-check that gradually turned into Space Is Deep. Not at all a bad idea! They managed to fill a nearly two hour set without Bumble B's voice. Lump sang better than ever, Mickey sang a lot more than usual and handled the little mixing console from his position at the keyboards. Bjorn F and I sipped our beers at the other side of the room in the crowd and Bjorn signalled to Mickey how to adjust the sound. Brt was to introduce the songs as usual. He stood with his back to the audience and got more and more hoarse and drunk and was finally asked to leave by Lump. He sat sulking in the bus for the rest of the evening. Rock'n'roll, eh? Anyway, some of the young girls with punky coloured hair left the pub, too, but the middle aged men were very enthusiastic, sang along to Put The Clock Back On The Wall and went bananas during Interstellar Overdrive. They looked up all band members afterwards, to get their autographs! That's the way to do it when you're pushing 50! Bjorn F was a bit disappointed that no-one was interested in the autograph of the patient producer of the second Smell Of Incense album or the man behind Melt ...
The concert was kind of a success against all odds, and ended at midnight, sharp. Then, when half the people had left, the organisers at the pub let a couple of hats go round to get some payment for the band... That was the Celler Loch policy. If they had a normal entrance fee at the door, no one would turn up, we were told. I think the band received about 130 marks, enough to cover the petrol expenses from Berlin and a little more, maybe. Well, we got a lot to drink in addition and were taken to the outskirts of the town where we should stay for the night. We were warned that the house was a sort of building site (just like parts of Berlin!) and very cold, but at least it was room for everyone. Otherwise we might have spent the night in two different camps. Anyway, we kept together, on the second floor of an old farm-house, it seemed. And a cold and seedy room it was. I was lucky to put my sleeping-bag in the huge bed at a comparably early stage (2.30 in the morning, I guess) and stayed there for the rest of the night while Mickey occupied another third of the bed. The others were sleeping on old mattresses on the dirty floor. Even more rock'n'roll, eh?
Next morning we were guided to a cafe in town and got the best breakfast of the entire tour. A long and luxurious meal. Great! We even got time for some sight-seeing of the old town with lots of houses from the 14th and 15th century that was not destroyed during the last war. At 3 in the afternoon I parted with the band. They had two evenings off before the next gig and intended to go to Amsterdam. I went to the railway station and headed for Belgium to visit some old friends.
Read all about even more troubles and the rest of the psychedelic crusade in Luna K's December menue.
Copyright © 1998 JP
You may also want to check out our Smell Of Incense articles/reviews: A Curious Miscellany, European Tour 1998 (2), Of Ullages And Dottles, Through The Gates Of Deeper Slumber.
Chrissy Zebby Tembo & Ngozi Family: My Ancestors (LP 1974/Re 2012500 LTD 500)
Clash: The Singles (19x7 Box 2006)
Electric Eye: Different Sun (LP 2016 LTD 100)
I.E.M. Steven Wilson: Arcadia Son (LP 2001 LTD 500)
Led Zeppelin: The Song Remains The Same (4xLP Box 2008)
Opium Cartel: Ardor (LP 2014)
Spectrum: Geração Bendita (LP 1971/Re 2001 LTD 450)
The Wizards: The Singles 1964-1967 (LP 2016 LTD 100)


Sorry for the delay in posting yesterday.
We started off at 8:00 with another keynote speech. Prashant Sridharan spoke about MSFT Team System. You know the piece of $10,000 software that you can recreate with Open Source software for free. The session was pretty boring. It's nice spending this much money and having a marketing pitch thrown at you. One thing he mentioned was a new VS ad campaign at www.400plusdifferences.com . There were a few humorous clips.
I was looking forward to this one. We design our apps to have objects in a middle tier and this seems to be targeted exactly to that. Kashif Alam was the speaker for this session. Basically, he demoed the same databind/paging/sorting crap that they do in all the demos. He just used an ObjectDataSource instead of SqlDataSource. That's fine if he had used a real object, you know like a custom class, but he just used a typed dataset. Also, his presentation leaves a lot to be desired. He is well-spoken, but very lowkey and he jumps around in the code a lot and never completes a thread.
He referenced www.asp.net/QuickStart which look pretty cool.
As is becoming the standard, the slides were not in the book. Remember, this is the extra book I paid $100 for. Thanks.
My first boneheaded move of the conference. The way the doors were labeled, we were in D&E and should have been in F (which was in the back of the room). So we sat in the wrong session for 20 minutes. We were wondering when he was going to get to web parts. We found the right room and discovered that the session we wanted had been rescheduled. So we went to the mobile pc lab and checked out tablets and got a hat.
Lunch and cruising the vendor area for more swag.
This is covering new stuff in C# 2.0 and upcoming features in 3.0. The presenter was Mads Torgersen and he did a very good job. This has been the best session so far. There is going to be some cool stuff in C# 3.0; Lambda trees, Extension methods and the LINQ stuff are very interesting.
This was pretty lame. The only Enterprise Library pattern I was even remotely interested in was the Logging. And it doesn't support rolling log files, so log4net is still my choice.
I didn't have high hopes for this, except for the fact that it was Scott Guthrie presenting. But it was awesome. Lots of useful stuff. He showed an RSS from MSFT that lets you write a few lines of code and generate an RSS feed for your site. That will be added to www.workagile.com very soon.
We came back at 7:00 to the MSFT "Unplugged" event. Free pizza and beer and a Q&A session with all the MSFT guys. Some people had some good questions. I didn't get to ask mine. I wanted to know what they thought the advantages were with Team System over Open Source alternatives. After that, we went and watched the Florida-UCLA game.
Day 3 should be up tonight. It's actually lunchtime on day 3 right now.


text:
Q: Reference for solvability of Baumslag-Solitar groups I've been searching for a while and couldn't find any reference which provides necessary and sufficient conditions over $m,n$ for the group $BS(m,n)$ to be solvable. The only I could find is that it is solvable when $n=1$ or $m=1$, but I don't know if those are the only cases.
Any help is much appreciated! Thanks in advance.


In order to shield the terminals of the 7002 and 7009 series receptacles, Rebling has introduced a series of protective covers.
These covers protect both the terminals, and the mechanic working on them.
Part number 644A1626 is designed for compact fit over standard termination options.
Part number 6441627 is a larger version designed to accommodate terminal adapters for 4/0 cables.
Both are available in the same chemically resistant, V-0 flame rated plastic as our mil-spec two-wire plug covers.
In addition, both can be ordered with an EMI shielding polymer (-E). Contact sales for availability.


The group is here to provide a safe and friendly environment where High School age students can learn how to appropriate the life they have in Jesus through worship, fellowship, prayer, witness, and giving to one another as needed (spiritually, emotionally, and materially). This is the behavior we can learn from the book of Acts. As a result of coming to group each week I believe our students leave encouraged, refreshed, and ready to take on the world.


Home » Atlanta REIA Blog » Articles » The Profit » September 2013 » Atlanta REIA Welcomes the Sign Mafia!
Atlanta REIA Welcomes the Sign Mafia!
If you've been around the real estate investing community for a while, you already know the Sign Mafia. They have been serving our community of interest since 2002. They are best known for their bandit signs and property marketing packages.
What are bandit signs? They are any signage placed in highly visible spots, designed to generate leads for buyers and sellers. They are key in guerilla marketing on the fly. Signs are typically placed in areas that are sometimes illegal, if not in violation of local ordinances, hence the name "bandit signs" and the SIGN MAFIA.
The reason bandit signs are so effective is, when people are in a certain situation or are in need of something and they see a sign for that purpose, they are likely to call. Signs are a great way for you to let people know your business and what services you provide. Without these signs, they may never know you exist. Plus, it is a certainty they are capturing information from other bandit signs, yours should be among the choices of information available.
Used as informational signs, directional pointers, sign toppers, or for quick info like phone numbers and web sites, bandit signs are a quick, inexpensive and highly visible way to get attention. Whether it's to get leads to buy or sell properties, signs can be most effective, especially if utilized on a continuous basis.
Sign Mafia Inc. offers any and all configurations of durable screen printed corrugated plastic signs and car magnetic signs. Also, sign toppers, wire stands, brochure boxes, lock boxes and agent marketing packages. Bulk or low volume, they can also ship anywhere in the US.
You want the sign to be simple and to the point. Use only the information that is "need to know". Do they need to know what your company name is? No. Do they need to know what your logo looks like? No! Just the basics. Bandit signs do not follow the conventions of consistent company image branding, it is not their purpose. Provide the most reliable contact data points only. Avoid posting a primary cell number or your business number, rather use a Google number, VOIP or a disposable cell number. Do not put multiple numbers on the sign, it is confusing, unnecessary and you may not get the call. A web site or email address is ok but not necessary, particularly if it is too long. Ask yourself… in your specific situation or objective, what piece of data will make it the easiest for them to contact you. Everything else is irrelevant to the concept of using bandit signs.
Don't blanket an area with a sign on every corner on every street. Spread them out and use strategic roads that get a lot of traffic. The signs will last longer if you put them out on Friday and even longer if you pick them up on Sunday night. Be visible. Get car magnetics, business cards, post-it notes, and flyers. Sign Mafia also supplies these items. Give a business card to everyone you talk to. Put post-it notes on car windows so they can get to them easily. Utilize your vehicles – car magnetics are like having a driving billboard. You can put them on your car doors and even on the backs of your vehicles, so people behind you in traffic, etc can also see your information. You might get your first lead just by being visible. Want to step up the visibility? Ask Sign Mafia about whole car wraps.
For more information, contact Dave or Cindy Berman at 770-592-2522. See them on the web at http://signmafia.com or via email at sales@signmafia.com.
Jeff Nix is a founding member of Atlanta REIA and serves as Director of Business Membership. Jeff is also the owner/operator of Atlanta Carpet Services, Inc. whom has been serving the real estate community throughout Metro Atlanta for over 20 years. Jeff has also been serving in the Air Force Reserves since 1981 and is an active real estate investor with a self-managed portfolio of single family homes.