text:
\section{Introduction}
\begin{figure*}
\centering
\includegraphics[width=\textwidth]{figures/sketch.png}
\caption{Sketch of our machine learning approach based on averaged static descriptors. The static structure factor is the main input of the model and even though it only shows minute changes with the temperature $T$, the model was trained to predict the abrupt slowdown from these minimal changes. The DNN explained in sec.~\ref{sec:dnn} predicts $F^{\alpha\beta}_s(k_p,t)$ from $S^{\alpha\beta}(k)$.  Instead with our evolutionary strategy (sec.~\ref{sec:es}) we are able to construct the memory function that produces the observed dynamics.}
\label{fig:sketch}
\end{figure*}

Understanding the dynamics of supercooled liquids approaching the glass transition represents one of the major challenges in condensed matter science~\cite{Anderson1995,Lubchenko2007,Berthier2011,Langer2014}.
The most striking signature of this phenomenon is the dramatic increase in viscosity or relaxation time upon a relatively mild change in the thermodynamic control parameters.
Despite the magnitude of this effect, there are no substantial changes in the microscopic structure of the material, which severely hinders our understanding of the mechanisms underlying the glass transition.

In recent years, machine learning algorithms have been successfully employed to capture subtle changes in the local structure of glassforming materials to create accurate predictors of the dynamics. The first such example is a machine-learned parameter called softness~\cite{Cubuk2015,Schoenholz2016,xiaoguang19,cubuk20,tah22}, which, based on support vector machines and physical intuition, identifies key structural features that strongly correlate with local particle dynamics.
Neural networks can also identify local structures~\cite{Boattini2018,Boattini2019} and correlate them to local dynamics~\cite{Boattini2020,oyama22}.
Furthermore graph neural networks~\cite{Bapst2020} have shown that the graph structure of each particle's local environment contains significant information to predict its long-time dynamics. It was later demonstrated that more refined observables can be calculated~\cite{Boattini2021} and used in combination with simpler models to capture the connection between statics and dynamics in glassy systems~\cite{Alkemade2022,jung22}.
Similar results can be achieved even with information theory~\cite{jack14,Paret2020} and dimensionality reduction~\cite{Paret2020,coslovich22}.
Recently, neural networks have also been used to find complex order parameters for glassy dynamics~\cite{D2SM00310D}.
However all these approaches are based on a particle resolved description of the system, which requires knowledge of the location of each single particle and its precise local environment. Unfortunately these particle resolved quantities are not always easy to measure, and furthermore single-particle properties do not easily lend themselves for statistical-physical theory development. Hence, a more collective description is often preferred.

Here we propose an alternative approach which is not based on local single-particle features, but instead on system averaged quantities.
This approach takes inspiration from collective theories of the glass transition~\cite{Gotze1992,Xia1999,Tarjus2005a,Sausset2008,Biroli2012,Dell2015,Rizzo2015,Rizzo2020,Janssen2015a,Ciarella2021b,liu2021,Szamel2022} that aim to predict the glassiness of the system using collective static observables that do not need to be particle resolved.
The cornerstone of our method consists of rewriting the dynamics of supercooled liquids following the Mori-Zwanzig procedure~\cite{Gotze1992,Reichman2005} to obtain a form of a generalized Langevin equation called the memory equation~\cite{Kob2002}.
From a mathematical point of view, this equation takes as input the statistically-averaged static structure of the system, mainly through the static structure factor $S(k)$ which is a function of the wave vector $k$. Using $S(k)$ as the initial boundary condition, the memory equation can then be used to predict the time-dependent dynamics of the system, quantified by the intermediate scattering function $F(k,t)$ at a given time $t$.
The key bottleneck, however, is finding the exact memory function that governs the dynamics of $F(k,t)$; this memory function should account for the dynamical slowdown of supercooled liquids, but its functional form is a priori unknown.
After decades of intense research, scientists have been able to solve only approximations of this equation, like mode-coupling theory (MCT)~\cite{Gotze1992,Franosch1997,Voigtmann2003,gotze2008complex,Reichman2005,SzamelPRL2003,brader09,Weysser2010,Janssen2018primer,Ciarella2021b,Debets2021}.

In this paper we use machine-learning to approximate the memory function. In particular we discuss two different approaches to the problem: (i) first we train a deep neural-network (DNN) in order to learn the memory equation using data measured from computer simulations of binary mixtures interacting via Lennard-Jones (LJ) or Weeks-Chandler-Andersen (WCA) potentials. In this approach the DNN plays the role of both the memory equation and the memory function itself.  (ii) Then we develop an evolutionary strategy tailored to identify and construct the memory function that produces the dynamics observed in the simulations.

In Fig.~\ref{fig:sketch} we sketch the concept of our approach.
First we collect extensive simulation data for LJ and WCA binary mixtures. For different values of temperature $T$ and density $\rho$ we measure the static structure factor $S^{\alpha\beta}$ and the self intermediate scattering function $F^{\alpha\beta}_s$, where $\alpha$ and $\beta$ are the indexes to represent the species of the mixture. These averaged descriptors are then given to a DNN that we train to predict the intermediate scattering function for a given $S^{\alpha\beta}$. We show that our DNN achieves excellent performances, thus concluding that the network can learn the memory equation. Next, in order to obtain some physical intuition about the memory function we introduce an evolutionary strategy that, given the intermediate scattering function and the structure of the memory equation, is able to predict the elusive memory function and clarify its form.

Overall, the results of our model are twofold: we introduce an effective approach that is able to rapidly predict the collective dynamics of the system from static measurements, and we are able to produce a representation of the memory function that is more realistic than many state-of-the-art theories. The function predicted by our machine-learning algorithm can be informative for future efforts aimed at developing a more quantitative theory of the glass transition.
In the next sections we will discuss the mechanism of our machine-learning approach, how to generalize it to any system and what are the consequences of our findings.

\section{Results}
\subsection{Dynamics of supercooled liquids from neural networks}
\label{sec:dnn}
We train a multi-layer perceptron (MLP) to predict the dynamics of supercooled liquids from static averaged quantities.
The MLP is a fully connected DNN with a simple feed-forward architecture.
The dynamics is characterized by measuring the intermediate scattering function $F^{\alpha\beta} (k,t)$ from the simulations (details in sec.~\ref{sec:sim}).
Rather than a full $k$-dependent description of the dynamics, we follow the standard procedure~\cite{Berthier2010,Janssen2018primer,Flenner2005} for binary mixtures, which consists in the following steps: (i) we focus only on $k=k_p\equiv |\bm{k}_\mathrm{peak}^{AA}|$ which is the location of the main peak of $S^{AA}$
 and it is arguably the  most descriptive wavenumber for such supercooled mixtures~\cite{Kob2002}, (ii) we consider only the self part of the intermediate scattering function, and (iii) we ignore the mixed term $F^{AB}(k,t)$ since $F^{\alpha\beta}_s=0$. Hence we end up describing the dynamics using $F_s^{\alpha\alpha}(k_p,t)$, where $\alpha=A,B$ represents the two species.

Furthermore, we define $F_s^{\alpha\alpha}(k_p,t)$ in the following way:
\begin{align}
\label{eq:normaliz}
    F_s^{\alpha\alpha}(k_p,t) =
    \frac{1}{N}\langle & \sum_i^N  e^{-i\bm{k}_p\bm{r}_i^\alpha(0)} e^{i\bm{k}_p\bm{r}_i^\beta(t)}\rangle
    \cdot \nonumber \\ & \cdot
    \underbrace{\frac{1}{N}\langle \sum_i^N e^{-i\bm{k}_p\bm{r}_i^\alpha(0)} \sum_j^N e^{i\bm{k}_p\bm{r}_j^\alpha(0)}\rangle }_{\equiv S^{\alpha\alpha}(k_p)},
\end{align}
where $\bm{r}_{i}^{\alpha}(t)$ is the position of particle $i$ of species $\alpha$ at time $t$.
Our choice of normalization imposes
$F_s^{\alpha\beta}(k_p,t=0)=S^{\alpha\beta}(k_p)$. In our work, this has two advantages: (i) we can now directly compare our prediction of $F_s^{\alpha\beta}(k_p,t)$ with collective theories like MCT, and (ii) our machine learning approach will give more importance to the density correlation functions corresponding to large values of $S^{\alpha\beta}(k_p)$, i.e. the most glassy (slowest) dynamics.

The MLP that we use to predict the dynamics takes as input the static structure factor $S^{\alpha\beta}(k)$ on a uniform grid of $N_k=100$ wave vectors in the range $0\le k\le 40\sigma_{AA}$, including the $\alpha\neq\beta$ terms, for a total of $300$ values of $S^{\alpha\beta}(k_i)$ (considering the $\alpha\beta=\beta\alpha$ symmetry).
In addition the MLP receives the temperature $T$, the density $\rho$, a label for the interaction type (i.e., either LJ or WCA), and the logarithm of the time at which it has to predict $F^{\alpha\beta}_s(k_p,t)$.
With this input-output architecture after training we can easily tune the value of $t$ in the input to reconstruct the full time dependence.

\begin{figure}
\centering
\includegraphics[width=\columnwidth]{figures/multiple-F.png}
\caption{Predictions of the multi-layer perceptron (MLP) for the self intermediate scattering function of binary mixtures, for the $AA$ and $BB$ components, normalized as defined in Eq.~\ref{eq:normaliz}. We report four state points from the test set (not used for training): the red is a hot liquid, the orange is a supercooled liquid, the yellow is a supercooled liquid and blue is a glass.
We compare the MLP (dot-dashed lines) with simulations (solid lines) and mode-coupling theory (dotted line) predictions.
}
\label{fig:multi-f}
\end{figure}
In Fig.~\ref{fig:multi-f} we show that the MLP (detailed in sec.~\ref{sec:MLP}) produces good predictions in the full temperature range, from high-temperature liquid to glass on which it was trained.
In this figure we also report the results of mode-coupling theory (dotted lines), which is a theory based on the same static information used as input for the MLP.
The results indicate that the MLP significantly outperforms MCT in predicting the realistic dynamics.

\begin{figure}
\centering
\includegraphics[width=\columnwidth]{figures/r2_vs_Tnormalized_inset.png}
\caption{Performance of the multi-layer perceptron (MLP) in predicting the self-intermediate scattering function of binary mixtures. We report the R$^2$ score as a function of temperature, normalized by $T_{mct}$ which is the temperature at which mode-coupling theory predicts the glass transition.
Its value is averaged over states with similar values of $T/T_{mct}$ and the colored region represents the standard deviation of each bin. A value of $1$ represents perfect predictions. In the inset we compare the MLP prediction with the target simulation, for a set with R$^2=0.76$. We conclude that on average the model predictions are good across the entire temperature range.
}
\label{fig:r2}
\end{figure}
To quantify the performance of the MLP we measure its predictivity on data outside of it training set. The model has been trained using $90\%$ of the data available while we use the remaining data to evaluate the R$^2$ score:
\begin{equation}
\mathrm{R}^2= 1 - \frac{SS_\mathrm{res}}{SS_\mathrm{tot}},
\end{equation}
where $SS_\mathrm{res}$ is the sum of squared residuals
\begin{equation}
    SS_\mathrm{res} = \sum_{\mathrm{observation}_i} \left( \mathrm{prediction}_i - \mathrm{truth}_i\right)^2
    \label{eq:ssres}
\end{equation}
and $SS_\mathrm{tot}$ is the total sum of squares
\begin{equation}
    SS_\mathrm{tot} = \sum_{\mathrm{observation}_i} \left( \mathrm{prediction}_i - \overline{ \mathrm{prediction}}\right)^2,
    \label{eq:sstot}
\end{equation}
where $\bar{...}$ represents the mean.
Notice that one observation in Eqs.~\ref{eq:ssres}-\ref{eq:sstot} corresponds to one time $t=t_i$ of $F_s(k_p,t)$ for any given $T$ and $\rho$, so the sum runs over all the temperatures $T$, densities $\rho$ and times $t_i$.

\begin{table}
\tiny
\renewcommand{\arraystretch}{2.5}
\newcolumntype{z}{>{\centering\arraybackslash\columncolor[HTML]{bfbfbf}}m{1cm}}
\newcolumntype{g}{>{\centering\arraybackslash\columncolor{white}}m{1.1cm}}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{ |z|g|g|g|g| }
\hline
\rowcolor{lightgray} \cellcolor[HTML]{8acf71} $\frac{k_BT_{mct}}{\epsilon_{\scaleto{AA}{2pt}}}$
&$\rho\sigma_{\scaleto{AA}{2pt}}^{\scaleto{3}{2pt}}=1.2$
&$\rho\sigma_{\scaleto{AA}{2pt}}^{\scaleto{3}{2pt}}=1.4$
&$\rho\sigma_{\scaleto{AA}{2pt}}^{\scaleto{3}{2pt}}=1.6$
&$\rho\sigma_{\scaleto{AA}{2pt}}^{\scaleto{3}{2pt}}=1.8$ \\
\hline
 WCA &
 0.74 &
 1.77 &
 3.49 &
 5.10 \\
\hline
 LJ &
 0.90 &
 1.87 &
 3.53 &
 5.10\\
\hline
\end{tabular}
}
\caption{\label{tab:Tmct} Temperatures that we use to normalize the data. The values of $k_BT_{mct}/\epsilon_{AA}$ correspond to the critical temperature of mode-coupling theory for the WCA and LJ mixtures~\cite{Voigtmann2008,Berthier2010,Berthier2011}, below which MCT predicts that the system is a glass. }
\end{table}

A value of R$^2=1$ corresponds to a perfect fit, so R$^2$ is an effective measure to evaluate the quality of the model.
In Fig.~\ref{fig:r2} we report the R$^2$ score as a function of $T/T_{mct}$, where $T_{mct}$ is the temperature at which mode-coupling theory predicts the glass transition, reported in Tab.~\ref{tab:Tmct}. With this normalization we can average simulations at different densities and different pairwise interactions to produce a single curve.
In this work we are not interested in the precise estimation of the critical point of MCT for our mixtures, but a more detailed discussion is available in Ref.~\cite{Voigtmann2008,Berthier2010,Berthier2011}.
In the supplementary information we show the root mean squared error (RMSE) for the same data.
The trend of the curve shows that the performance of the MLP starts to drop at $T<T_{mct}$, when the two-step relaxation becomes more prominent and thus the intermediate scattering function is a more 'complex' function with more features to predict.
At $T<0.6 T_{mct}$ the MLP becomes unreliable.
Surprisingly at even lower temperatures, when the dynamics is even slower, the performances increase, approaching again the perfect R$^2=1$ score. This is a consequence of the fact that in the glass the second relaxation happens at $t>t_\mathrm{max}$, which is outside of the time window that the model observes, so $F$ is characterized by a single relaxation, thus being easier to learn for the model.

\subsubsection{Transferability}
\begin{figure}
\centering
\includegraphics[width=\columnwidth]{figures/Ttransfer-r2_vs_Tnormalized.png}
\caption{Temperature transferability of the MLP: we report the R$^2$ score as a function of the temperature. For each curve the model is trained using only temperatures larger than the one corresponding to the rhombus. The solid lines represent the regions in which the model has been trained, while it has never seen data from the dashed line region.
}
\label{fig:Ttransf}
\end{figure}
\textbf{Temperature transferability:} One of the main strengths of machine learning is the possibility to train a model in a more favorable situation (e.g.\ when there is more data available) and deploy it in a less favorable one.
For computer simulations the hardest region to sample is the low temperature regime, because the dynamics is much slower and more time is required to sample all relevant structural rearrangements.
This means that it is much easier to collect data at high $T$ than at low $T$.

Here we show the temperature transferability of our model, i.e.\ its performance at temperatures different from the training.
We report in Fig.~\ref{fig:Ttransf} the R$^2$ score when the MLP is trained only using data for $T>T_0$.
The RMSE is reported in the supplementary information.
As expected the model performs excellently in its training region (solid lines), but outside (dashed lines) its score starts dropping.
The results of Fig.~\ref{fig:Ttransf} suggest the existence of two regimes:
(i) for $T>0.8\,T_{mct}$ we have full transferability. It is in fact possible to train the model at high temperature and retain good predictions, as evidenced by the red curve in Fig.~\ref{fig:Ttransf}.
(ii) When $T<0.8\,T_\mathrm{mct}$ the transferability is restricted. In Fig.~\ref{fig:Ttransf} we show that the best we can do is to transfer the training at $T>1.2\,T_\mathrm{mct}$ down to $T> 0.5 T_\mathrm{mct}$.

Overall we can conclude that our approach is transferable in the (i) MCT regime corresponding to $T>0.8\,T_\mathrm{mct}$, while the transferability is very limited in the (ii) $T<0.8\,T_\mathrm{mct}$ activated regime. This low temperature region is in fact characterized by heterogeneous activated dynamics and facilitation~\cite{guislein22,scalliet22}.
We believe that this different relaxation mechanism is the reason behind the lack of transferability.
In summary, except for the very low temperature region, our approach presents some degree of temperature transferability that we can use to reduce the need for data that are harder to collect.

\begin{figure}
\centering
\includegraphics[width=\columnwidth]{figures/r2_vs_Tnormalized-modeltransf.png}
\caption{Model transferability of the MLP. We report the R$^2$ score of the model when tested for a different interaction potential not included in the training.
The inset qualitatively shows that R$^2>0.8$ corresponds to acceptable predictions.
}
\label{fig:modeltransf}
\end{figure}
\textbf{Model transferability:} We show in this section the performance of the MLP in predicting the dynamics of computer simulations produced with a different interaction than the one the MLP has been trained on.
Our library consists of simulation data for binary LJ and binary WCA, so their dynamics is similar at high temperature and/or high density, but it becomes significantly different approaching $T_\mathrm{mct}$.
In Fig.~\ref{fig:modeltransf} we show the prediction of the MLP when it is trained over LJ and tested over WCA and vice versa, reporting the R$^2$ score as a function of normalized temperature.
We see that at $T>0.6 T_{mct}$ the MLP produces excellent predictions even when it is trained for a different interaction potential.
However the quality drops for very low temperature, when minute differences between the LJ and WCA  structures are amplified to enormous differences in dynamics~\cite{Berthier2010,Berthier2011a,Coslovich2012,Landes2020,Ciarella2021b,Debets2021}.
In particular the model performs poorly when it is trained using the WCA potential and tested over the LJ data.
This is a consequence of the fact that the LJ model becomes a glass at a higher temperature compared to the WCA model~\cite{Berthier2010,Berthier2011a,Ciarella2021b}, so there is a region where the LJ model is infinitely slower than the WCA.

Overall we see that it is possible to use data measured from another system to obtain reliable prediction for a different (but relatively similar) system. However those predictions become unreliable at very low $T$, when approaching the glass transition.

\subsection{Evolutionary strategy for the memory function}
\label{sec:es}
\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{figures/multiple-F-M_ES.png}
    \caption{Predictions of the evolutionary strategy (ES) for the memory function of binary mixtures, with focus on the dominant $AA$ component at $k=k_p$. We report four state points at different temperature: the red is a hot liquid, the orange is a supercooled liquid, the yellow is a supercooled liquid and blue is a glass.
    In (a) we see that the ES (solid) perfectly reproduces the simulations (dot-dashed). In panel (b) we report the memory that the ES uses to reproduce the simulated $F_s^{AA}$ (solid) and we compare it to MCT (dotted) which produces instead very bad predictions.
}
    \label{fig:FM-es}
\end{figure}

In the previous section we have seen how to numerically correlate static structural information with the dynamics of the system.
Such a deep learning approach however does not tell us much about the physics of the system.
Here we employ a physics-informed strategy that is created in order to avoid the application of a \textit{black box}~\cite{Rudin2019,Murdoch2019}, but instead we bound the machine learning model to play the role of a single physical unknown function: the memory function.

In order to develop this physics-informed strategy we start from the exact memory equation that describes the dynamics of liquids~\cite{Kob2002}:
\begin{align}
\frac{d^{2} F^{\alpha\beta}_s(k_p, t)}{d t^{2}}&+\Omega^2_{\alpha\beta}(k_p) F^{\alpha\beta}_s(k_p, t)+\nonumber \\ & \int_{0}^{t} d \tau M^{\alpha\beta}(k_p, t-\tau) \frac{\partial F^{\alpha\beta}_s(k_p, \tau)}{\partial \tau}=0,
\label{eq:gle}
\end{align}
where $\Omega^2_{\alpha\beta}(k_p)$ is a constant representing the vibrational term.
While the equation above is formally exact, unfortunately the memory function $M^{\alpha\beta}(k_p, t-\tau)$ that appears in the integral is unknown, and thus the equation cannot be solved. MCT is based on an uncontrolled approximation of this memory function that lead to excellent semi-quantitative results~\cite{Reichman2005,sciortino2001debye,Weysser2010}, but its predictions are not able to exactly capture the full phenomenology of the glass transition~\cite{Berthier2010}.
While there are some ways to invert Eq.~\ref{eq:gle}~\cite{Baity-Jesi2019,Landes2020}, and various approaches to numerically estimate the memory function~\cite{Carof2014,Jung2017,Klippenstein2021,Vroylandt2022}, there is no consensus on the general form of $M$. More importantly, most of these procedures to calculate $M$ are very sensitive to noise, thus requiring refined data, and overall they are often computationally expensive.

Here we show that it is possible to efficiently parameterize $M$ as a sum of stretched exponentials, whose coefficients can be determined by an evolutionary algorithm.
We choose the exponential representation because $F_s(k,t)$ behaves as a stretched exponential for long times~\cite{vanmegen93}, and the solution of schematic MCT models~\cite{Gotze1992,Bengtzelius1984} shows that often the memory function has the same structure of the intermediate scattering function.
So, we represent the memory function in the following way:
\small
\begin{align}
    M^{\alpha\beta}(k,t) &= c^{\alpha\beta}(k) + \sum_{i=0}^{N_{exp}} w^{\alpha\beta}_i(k) \;\times \nonumber \\& \left\{ \left[1-a^{\alpha\beta}_i(k)\right] e^{-\left(\frac{t}{\tau^{\alpha\beta}_i(k)}\right)^{b^{\alpha\beta}_i(k)} } +a^{\alpha\beta}_i(k)\right\}
    \label{eq:strexp}
\end{align}
\normalsize
which is a sum of $N_{exp}$ stretched exponentials, for a total of $3(1+4N_{exp})$ parameters for every $k$.
Notice that our parametrization also allows for standard or compressed exponentials, according to the value of $b_i^{\alpha\beta}(k)$.
As previously discussed (sec.~\ref{sec:dnn}), we focus only on $k=k_\mathrm{peak}^{AA}$, which is usually the most important wavevector in simple glassformers such as the systems studied here.

The details of our evolutionary strategy (ES) are discussed in sec.~\ref{sec:method-es}. Briefly, the ES is an optimization technique inspired by evolution and natural selection. It works by creating new generations of memory functions (parameterized as Eq.~\ref{eq:strexp}) by cross-breeding the previous generation and adding random mutations. For each of them we then solve the memory equation (Eq.~\ref{eq:gle}) obtaining $\left\{F^{\alpha\alpha}_{ES}(t_i) \right\}$ and then we do one step of evolution favoring the individuals that minimize the following loss function:
\begin{equation}
    \mathcal{L}_{ES}=\sum_{t_i,\alpha} \left[ F^{\alpha\alpha}_s\left(k_P,t_i\right) - F^{\alpha\alpha}_{ES}(t_i)\right]^2 +\mathrm{regularization}
    \label{eq:loss}
\end{equation}
which is the squared difference between predicted and observed dynamics. In the Supplementary Information we verify that the ES solutions are physically reasonable by replacing the intermediate scattering function with the MCT approximation $F^{\alpha\alpha}_S\left(k_P,t_i\right)\xrightarrow{}F^{\alpha\alpha}_{MCT}(k_p,t_i)$ and confirming that the evolutionary strategy converges exactly towards $M^{\alpha\beta}_{MCT}(k_p,t)$. For this reason we claim that our ES solutions retain some degree of realism.

The results of the ES are reported in Fig.~\ref{fig:FM-es}.
In panel (a) we show that the ES is able to perfectly recover the simulated self intermediate scattering function  $F^{\alpha\alpha}_S\left(k_P,t_i\right)$ that we are targeting. The figure presents four curves that range from the liquid to the glass phase. We have also verified that the ES solution converges to the simulated $F^{\alpha\alpha}_S(k_p,t)$ for any temperature and density that we have available. Even though we have only focused on $k=k_p$, we believe that the ES approach will work for any value of $k$ since the structure of Eqs.~\ref{eq:gle}-\ref{eq:strexp} does not depend on $k$.

In Fig.~\ref{fig:FM-es}(b) we report the results for the memory function.
Following the parametrization introduced in Eq.~\ref{eq:strexp} the ES is able to converge to memory functions that produce the realistic $F^{\alpha\alpha}_S(k_p,t)$ of Fig.~\ref{fig:FM-es}(a).
In particular the memory functions presented in Fig.~\ref{fig:FM-es}(b) are obtained setting $N_{exp}=2$.
We highlight the differences between the $M$ at which the ES converged, with the MCT approximation (dashed lines), that unsurprisingly overestimates the glassiness of these systems~\cite{Kob2002,Berthier2010,Janssen2018primer}.
We conclude that the ES method we introduced is an effective way to obtain a realistic memory function.

\begin{figure}
\centering
\includegraphics[width=\columnwidth]{figures/r2_vs_Tnorm.png}
\caption{Performance of the evolutionary strategy in finding the memory function that correctly predicts the dynamics. We parameterize the memory following Eq.~\ref{eq:strexp}, while the different curves correspond to different values of $N_{exp}$. We show that $N_{exp}=2$ is the minimum number of exponentials to achieve good performances, which corresponds to a total of $27$ parameters.}
\label{fig:es-r2}
\end{figure}

Lastly we discuss the physical insights that we can harness from our evolutionary approach.
We have seen that the ES converges to the simulated intermediate scattering function if we model the memory function following Eq.~\ref{eq:strexp}.
Since Eq.~\ref{eq:gle} is exact, we know that the real memory function has to produce the same output as our ES when propagated through Eq.~\ref{eq:gle}. This means that we can look at the structure of our ES solutions to have some intuition about the real memory.
In particular, in Fig.~\ref{fig:es-r2} we report the R$^2$ for different parametrizations containing a different number of stretched exponentials and we verify that a good solution needs at least $N_{exp}=2$.
In the supplementary information we also show that the second stretched exponential becomes relevant only approaching the glass transition, in the same range where localized unstable modes become relevant~\cite{Coslovich2019}, and when energy-driven and entropy-driven activation start to compete~\cite{Carbone2022}.
We also know that activation and facilitation become relevant only at very low temperature~\cite{guislein22,scalliet22}.
This suggests that there are two separate relaxation channels that real materials follow while relaxing, with the second channel becoming dominant below $T_{mct}$, i.e.\ the temperature that is often identified with a crossover~\cite{Andersen05}.

Moreover, the intuition derived from the evolutionary strategy has also important consequence for the theory.
Models like MCT invoke uncontrolled approximations to solve Eq.~\ref{eq:gle}. One possibility is the exponential closure that assumes that $M^{\alpha\beta}(k,t)\sim \exp\left[\Omega^2_{\alpha\beta}(k) t\right]$.
This schematic model is simple enough to be solved analytically, but its results are not satisfying~\cite{Leutheusser1984,Bengtzelius1984,Janssen2015a,Ciarella2019pnas,CiarellaPhdThesis}.
Inspired by our ES we propose a double stretched-exponential closure defined as
\begin{equation}
    M^{\alpha\beta}(k,t)= \exp\left[\Omega_{\alpha\beta}^2(k) t^{\nu} \right] +  K^{\alpha\beta}(k)\exp\left[\tilde{\Omega}_{\alpha\beta}^2(k) t^{\mu} \right] .
    \label{eq:mctclosure}
\end{equation}
This schematic model retains a structure of Eq.~\ref{eq:gle} similar to the exponential closure, but according to our ES results it should be much more appropriate to describe the glassy dynamics. So overall our ES strategy points towards novel functional forms that can improve upon the conventional MCT closure approximation.

In summary we have defined here a very simple approach to determine the memory function $M$ given the self-intermediate scattering function $F_s$. The ES is fast and reliable across all the temperatures and densities studied. The results suggest that two relaxation channels need to be considered, so we developed the machine-learning inspired MCT closure of Eq.~\ref{eq:mctclosure}.
In practice, we believe that our ES method can be applied to easily gain some intuition about the real memory function of any glass forming liquid.

\section{Discussion}

The goal of this study was to show how a neural network can understand and predict the dynamics of supercooled liquids from static information.
Our approach is based on averaged quantities that are easier to measure experimentally, compared to particle resolved ones. We are also able to interpret the mechanism of the machine learning model to gain physical intuitions about the glass transition.

In Fig.~\ref{fig:multi-f}-\ref{fig:r2} we show that we can train a multi-layer perceptron to efficiently predict the dynamics (represented by the self intermediate scattering function $F_s^{\alpha\beta}$), using only statistically-averaged static information.
This implies that dynamical information is encoded in the static structure of the system, similarly to how higher-order correlations are partially encoded into two body structure~\cite{Coslovich2012}.
Furthermore since the main input of our neural network is the static structure factor $S^{\alpha\beta}$, our results corroborate the idea that two body static correlations when elaborated using an expressive approach like our MLP, are enough to describe the dynamics.

One of the main problems in studying systems close to the glass transition is data collection, because experiments and simulations at deeply supercooled temperatures are slow. We show that our MLP performs relatively well when only high temperature data are provided for training (Fig.~\ref{fig:Ttransf}) or when the MLP is used to make predictions for different variations of the model that is used for training (Fig.~\ref{fig:modeltransf}). Unfortunately this transferability drops when the system is approaching the glass transition, because we know that minute changes in the structure correspond to enormous changes in the dynamics.
Overall this means that, if the interest is in the liquid regime it is possible to fully exploit the MLP transferability by training the model where data is easily available, but to accurately describe the glassy regime, glassy data is actually required.

We then develop a physics-inspired method to obtain an expression for the memory function that realistically describe our data.
Instead of using deep learning as a black box to connect statics to dynamics, we rewrite the dynamics as a memory function and we replace this memory with our machine learning model.
Our approach circumvents on the fly calculations and inverse Laplace transform, which are computationally very expensive, by using an evolutionary strategy to find a structure for the memory function.
We show in Fig.~\ref{fig:FM-es} that the ES easily converges to memory functions that reproduce the real dynamics observed in simulations.

Our physics-inspired evolutionary approach is also able to give real intuition about the physics. The results in Fig.~\ref{fig:es-r2} let us conclude that the memory function can be effectively parameterized as a sum of two stretched exponentials.
We can interpret those two stretched exponentials as two relaxation processes that describe the complex multiscale relaxation of the glassy liquid, and we can also see that only one is needed to describe the liquid phase (SI Fig.~\ref{fig:2strexp}). One possible interpretation for the existence of two dominant relaxation channels might be dynamic heterogeneity, i.e. the coexistence of transiently fast and slow groups of particles, which is known to emerge at temperatures below $T_{mct}$~\cite{Schoenholz2016,Boattini2020,guislein22}. However, more work is needed to identify the microscopic physical origins of the two stretched exponentials.
This intuition motivates us to explore a schematic model where the memory function is exactly represented as two stretched exponential, but we leave that for future work.

In conclusion, we have introduced two powerful tools to evaluate and describe the dynamics of supercooled liquids. The neural network that we propose can be efficiently trained and deployed to predict the self intermediate scattering function from averaged quantities which are simple to measure experimentally. Lastly we have discussed a way to obtain a realistic memory function using an evolutionary strategy, concluding that the memory can be reasonably represented as a sum of two stretched exponentials. We believe that our method can be efficiently applied to predict the dynamics of many other glass forming mixtures and that our conclusions about the functional form of the memory function can pave the way towards the development of more effective theories to describe the glass transition.

\section{Methods}

\subsection{Computer simulations}
\label{sec:sim}
The models reported in this paper have been trained and tested using simulation data of two binary mixtures: the Kob-Andersen binary Lennard-Jones (LJ) mixture~\cite{Kob1994} and its Weeks-Chandler-Andersen truncation (WCA) ~\cite{wca1971}.
They are three-dimensional $80:20$ mixtures of particles $A:B$ interacting with each other via
\begin{equation}
    V_{\alpha\beta}(r)=
    \begin{cases}
    4\epsilon_{\alpha\beta}\left[\left(\frac{\sigma_{\alpha\beta}}{r}\right)^{12}-\left(\frac{\sigma_{\alpha\beta}}{r}\right)^{6} +C_{\alpha\beta}\right],\hfill r\le r_{\alpha\beta}^c
    \\
    \mathrlap{\qquad 0}\hphantom{4\epsilon_{\alpha\beta}\left[\left(\frac{\sigma_{\alpha\beta}}{r}\right)^{12}-\left(\frac{\sigma_{\alpha\beta}}{r}\right)^{6} +C_{\alpha\beta}\right]},\hfill r>r_{\alpha\beta}^c
    \end{cases}
\end{equation}
where the cutoff radius $r^c_{\alpha\beta}$ is $2.5\sigma_{\alpha\beta}$ for LJ, and $r^c_{\alpha\beta}$ is $2^{1/6}\sigma_{\alpha\beta}$ for WCA~\cite{wca1971}. The choice of $C_{\alpha\beta}$ secures that $V_{\alpha\beta}(r^c_{\alpha\beta})=0$.
As usual~\cite{Kob1994}, we set $\epsilon_{AB}/\epsilon_{AA}=1.5, \epsilon_{BB}/\epsilon_{AA}=0.5, \sigma_{AB}/\sigma_{AA}=0.8, \sigma_{BB}/\sigma_{AA}=0.88$.

For these mixtures we perform molecular dynamics simulations in the $NVE$ ensemble using HOOMD-blue~\cite{anderson2008general}. First we equilibrate the system at different densities $\rho\sigma_{AA}^3 \in \lbrack 1.2,1.8\rbrack$ and temperatures $k_BT/\epsilon_{AA} \in \lbrack 0.2, 15 \rbrack.$  We impose periodic boundary conditions and set the box at $L=10\sigma_{AA}$ while tuning the density by changing the number of particles $N\in \left[1200,2000\right]$. All the particles have the same mass $m$.
We run the simulations for $10^8$ timesteps of size $dt=10^{-3}\tau_{MD}$ with $\tau_{MD}=\left( m\sigma_{AA}^{2}/48\epsilon_{AA}\right)^{1/2}$, which allows us to sample up to $t_\mathrm{max}=3\cdot 10^6\tau_{MD}$.
Finally, we use the simulated trajectories to calculate the partial static structure factors $S^{\alpha\beta}(k)$ and the self intermediate scattering functions $F_s^{\alpha\beta}(k,t)$.

\subsection{MLP}
\label{sec:MLP}
We train a multi-layer perceptron to predict the dynamics of supercooled mixtures from static information.
The results reported in sec.~\ref{sec:dnn} are produced by a multi-layer perceptron.
The network consists of 7 hidden layers of size $\left\{500, 400, 400, 300, 200\right\}$ that transform the $304$ input features $\left\{S^{AA}(k_1),...,S^{BB}(k_{100}),T,\rho,\mathrm{interaction},t_x\right\}$ into the output $\left\{F_s^{AA}(k_p,t_x) ,F_s^{BB}(k_p,t_x)\right\}$. The MLP is trained for $1000$ iterations, taking approximately $1$ hour on a standard laptop. We also use $L1$ loss~\cite{bengio12} and the adam optimization algorithm~\cite{kingma16}.

\subsection{Memory equation and MCT}
In this paper we numerically solve Eq.~\ref{eq:gle}, which is called the memory equation~\cite{Kob2002}. This is an equation of the same class as the generalized Langevin equation (GLE) and we believe that out method can be tailored to solve a wide category of GLE equations with a structure similar to Eq.~\ref{eq:gle}, using physical intuition.

In general, it is possible to exactly describe the dynamics of liquids by deriving an expression for
\begin{align}
    F^{\alpha\beta}(k,t) =
    \frac{1}{N}\langle & \sum_i^N  e^{-i\bm{kr}_i^\alpha(0)} \sum_j^N e^{i\bm{kr}_j^\beta(t)}\rangle,
\end{align}
from the solution of
\begin{align}
\label{eq:mct}
\frac{d^{2} F^{\alpha\beta}(k, t)}{d t^{2}}&+\Omega^2_{\alpha\beta}(k) F^{\alpha\beta}(k, t)+\nonumber \\ & \int_{0}^{t} d \tau M^{\alpha\beta}(k, t-\tau) \frac{\partial F^{\alpha\beta}_s(k, \tau)}{\partial \tau}=0,
\end{align}
but unfortunately the memory function $M^{\alpha\beta}(k, t)$ is unknown.
Notice that in this paper we are mainly interested in solving Eq.~\ref{eq:gle} which corresponds to Eq.~\ref{eq:mct} if $F^{\alpha\beta}(k,t)$ is replaced with $F_s^{\alpha\alpha}(k_p,t)$.
We will also assume that the memory function is the same in Eq.~\ref{eq:gle} and Eq.~\ref{eq:mct}.

The memory equation can only be solved using some approximations like mode-coupling theory~\cite{Gotze1992,Voigtmann2003,Reichman2005,gotze2008complex,Ciarella2021b,Debets2021}, for this reason we also refer to the MCT equation.
The MCT approximation applied to Eq.~\ref{eq:gle} consists of the following definition of the memory function:
\begin{align}
\label{eq:Mmct}
M^{\alpha\beta}_\mathrm{mct}(k,t)=\frac{1}{2k^2}\frac{\rho}{x_\alpha x_\beta} & \sum_{\substack{\alpha'\beta'\\\alpha''\beta''}} \int \frac{d^3\bm{q}}{\left(2\pi\right)^2} \cdot \nonumber \\ & \cdot V_{\alpha\alpha'\alpha''}\left(\bm{q},\bm{k},\bm{k}-\bm{q}\right)F_s^{\alpha'\beta'}(k,t) \cdot \nonumber \\ & \cdot F_s^{\alpha''\beta''}(k,t) V_{\beta\beta'\beta''}\left(\bm{q},\bm{k},\bm{k}-\bm{q}\right),
\end{align}
where $x_\alpha=N_\alpha/N$ is the density of species $\alpha$ and the vertex function corresponds to
\begin{equation}
V_{\alpha\beta\gamma}\left(\bm{k},\bm{q},\bm{p}\right)=\left(\hat{k}\cdot\bm{q}\right)c^{\alpha\beta}(q)\delta_{\alpha\beta} + \left(\hat{k}\cdot\bm{p}\right)c^{\alpha\gamma}(p)\delta_{\alpha\gamma},
\end{equation}
with $S^{-1}_{\alpha\beta}(k)=\delta_{\alpha\beta}/x_\alpha - \rho c_{\alpha\beta}(k)$.

So overall, the inputs for the MCT equation are the bulk density $\rho$, the temperature $T$, and the structure factor $S^{\alpha\beta}(k)$.
In our numerical solution of the MCT equation we follow all the steps discussed in Ref.~\cite{Flenner2005,Weysser2010,Ciarella2021b,Debets2021} over a grid of $N_k=100$ points.
As an alternative to the MCT approximation, we also solve Eq.~\ref{eq:gle} using Eq.~\ref{eq:strexp} instead of $M^{\alpha\beta}_\mathrm{mct}$, from the same inputs.
In summary, at any given $(T,\rho)$ we only require $S^{\alpha\beta}(k)$ as input to predict the microscopic relaxation dynamics of the system, either with MCT or with our parametrization of the memory identified by the evolutionary strategy.

\subsection{Evolutionary strategy}
\label{sec:method-es}
Evolutionary strategy (ES) is a class of machine learning optimization algorithms that are inspired by natural evolution in the following way: at every iteration (or generation), a population of parameters (the genotypes)  are perturbed by cross-breeding and mutations and their objective function (fitness) is evaluated.
Then the highest scoring parameters are recombined to populate the next generation, iteratively until the objective function is optimized.
The huge advantage of this class of algorithms is that they do not require back-propagation, which is particularly useful when the objective function is a complex integro-differential equation like our GLE (Eq.~\ref{eq:gle}).

We use covariance matrix adaptation evolution strategy~\cite{Hansen2001} (CMA-ES), a widely known method of the ES class which describes the population by a multivariate Gaussian.
The algorithm is available as a python package~\cite{hansen2019pycma}. Another advantage is that it does not require any hyper-parameter except for the initial condition and the population size represented by the initial variance of the gaussian $\sigma$.
We then use CMA-ES to find the best function $M^{\alpha\beta}_{ES}$ parametrized as Eq.~\ref{eq:strexp} that reproduces the real dynamics.
In practice at each step we propagate $M^{\alpha\beta}_{ES}$ through Eq.~\ref{eq:gle} and we compare its output $F^{\alpha\alpha}_{ES}(t)$ to the real dynamics $ F^{\alpha\alpha}_S\left(k_p,t\right)$, evaluating the fitness function $\mathcal{F}=-\mathcal{L}$ (Eq.~\ref{eq:loss}) for the evolution.
We use as initial condition $M=M_{mct}$ and for the initial population we set $\sigma=5\cdot 10^{-4}$.
We use Lasso regularization to evaluate Eq.~\ref{eq:loss}.
Usually the procedure converges below an error $\epsilon<10^{-6}$ within a couple of hours of evolution.
Results in Fig.~\ref{fig:es-r2} show that the $F$ predicted by ES is very close to the real dynamics, so we conclude that $M$ is well represented by two stretched exponentials.

\section{Data availability}
The simulation results and the data analysis that support the findings of this study are available upon request from the corresponding author.

\section{Code availability}
The codes used to support the findings of this study are available upon request from the corresponding author.

redpajama_set_name: RedPajamaArXiv


text:
Documents filtered by: Author="Sullivan, John" AND Recipient="Washington, George" AND Period="Revolutionary War" AND Period="Revolutionary War"
To George Washington from Brigadier General John Sullivan, 22 January 1776 [letter not found]
Letter not found: from Brigadier General John Sullivan, 22 Jan. 1776. On 23 Jan. Robert Hanson Harrison wrote to Sullivan: "I have it in command from his Excellency to inform you, that General Gates handed him your letter of the 22 inst."1 (DLC: Peter Force Collection).
1. For the full text of Harrison's letter to Sullivan of 23 Jan., see GW to Sullivan, 28 Jan. 1776, source note.
Sullivan, John
"To George Washington from Brigadier General John Sullivan, 22 January 1776 [letter not found]," Founders Online, National Archives, accessed April 11, 2019, https://founders.archives.gov/documents/Washington/03-03-02-0119. [Original source: The Papers of George Washington, Revolutionary War Series, vol. 3, 1 January 1776 – 31 March 1776, ed. Philander D. Chase. Charlottesville: University Press of Virginia, 1988, p. 168.]
From Washington to Sullivan [20 January 1776]
All correspondence between Washington and Sullivan

redpajama_set_name: RedPajamaCommonCrawl


Meanwhile lets just say that we are proud Mischel Yosick contributed a whooping 303 entries.
10/18/2018: FBN – After the Bell – This can no longer be a one sided relationship and there needs to be transparency regarding how Saudi deals with antagonists.
10/22/2018: FBN: Bulls and Bears – The killing of Jamal Khashoggi is horrific, but U.S. should not be in the business of picking dictators.
10/23/2018: FBN Evening Edit – What happened to Jamal Khashoggi is heinous, but is it reason enough to implode an entire U.S./Saudi relationship?


text:
Home / 2017 / April (page 9)
Talking about tourism promotion
By Prajawal Shrestha My cousin, who has settled down in London, was here on a leave. He came from the Turkish Airlines aircraft. He shared the experience that the aircraft had to take five rounds in the Kathmandu sky while waiting its turn to land at the Trubhuwan International Airport …
Construction starts on world's highest power transmission project in Tibet
LHASA, April 7 (Xinhua) — "In winter, power cuts have lasted more than 10 days," said PemaYangzom from her home in Bumba Tsal, a village in southwest China's Tibet Autonomous Region. "But since being hooked up to the new power grid, power cuts are a thing of the past." The …
New Delhi using Dalai as diplomatic tool harms Sino-Indian ties
By Liu Jianxi The 14th Dalai Lama has been invited by New Delhi to Tawang, a disputed region on the China-India border this week. The Dalai Lama is expected to be received by India's Junior Home Minister Kiren Rijiju this time, a move that gives the trip some official implications. …
Himalaya Airlines daily flights to Dubai from April 28
Himalaya Airlines on Thursday announced the launch of daily flights to Dubai, beginning from April 28. This will be Himalaya Airline's fourth international destination after Doha, Kuala Lumpur and Yangon. According to the carrier, the flight will depart from Kathmandu at 10:40 local time, arriving in Dubai at 13:25 hrs. …
NIBL to acquire Ace Development Bank
The Special General Meeting of Nepal Investment Bank Ltd (NIBL) on Friday approved a proposal of its board of directors (BOD) to acquire Ace Development Bank Ltd. Issuing a statement, NIBL said the two entities will provide consolidated service in the name of Nepal Investment Bank Ltd after the acquisition …
Siddhartha Bank appoints Laxmi Capital as its issue manager
Siddhartha Bank Ltd has appointed Laxmi Capital Merchant Ltd as its issue manager for its 10:3 rights issue. According to a statement issued, Rajiv Sapkota, CEO of Laxmi Capital Merchant Ltd and Bhola Nath Dhungana, secretary of Siddhartha Bank Ltd signed the Memorandum of Understanding (MoU) on behalf of their …
Mega Bank's campaign to develop saving habit from young age
Mega Bank Nepal Ltd has started a campaign named 'Nanima Lageko Bani: Aaja Bachau, Jindagi Bhari Ramau' aimed at teaching the habit of saving from the young age. Issuing a statement, the bank said that Nepali cricket team Captain Paras Khadka and actress Priyanka Karki on Saturday requested the students …
FNCCI's first female president, Golchha senior VP
By Our Reporter Bhawani Rana has made a record by becoming the first female president of the FNCCI president from the 51th general assembly of the umbrella organization of the business sector in the country. Rana, who served as the senior vice-president in the previous executive committee of the FNCCI, …
Citizens Bank Open Golf starts
Citizens Bank Open Golf Tournament began from Saturday, April 8. "The bank is organizing the golf tournament since the last three years with an objective of promoting the game," said the bank in a press statement. The tournament is being organized in Gokarna Golf Club. According to the bank, nearly …
Sunrise Bank's Dhalko branch now in Khusibu
Sunrise Bank Ltd has shifted its Dhalko branch office to a new location in Khusibu. Issuing a statement, the bank said that its CEO Ratna Raj Bajracharya inaugurated the relocated branch office amid a function on Sunday. "To celebrate relocation of the branch office, the bank will provide 50 % …


text:
Posts tagged 'Java'
Java Security Updates for Leopard and Snow Leopard
Apple has released Java for Mac OS X 10.6 Update 4 and Java for Mac OS X 10.5 Update 9, patching a number of vulnerabilities in the Java virtual machine. The most serious of these may allow an untrusted Java applet to execute arbitrary code outside of the Java sandbox. Users with Java installed should update soon. Those of you who don't have Java don't need to worry. If you're unsure, just check Software Update.
Apple recently announced that the version of Java ported by Apple for Mac OS X has been deprecated. Starting in Mac OS X 10.7 "Lion", the Java runtime will no longer be installed by default, instead requiring users to install Oracle's Java runtime should they require Java support. Apple also recently stopped bundling Flash with Mac OS X by default, with new MacBook Air and MacBook Pros shipping without Flash. The divesting of these two products will not only eliminate Java and Flash vulnerabilities on default installs of Mac OS X, it will allow users who install these apps to get updates quicker directly from Oracle and Adobe, instead of having to wait for Apple to release software updates.
Hit the jump for details of the Java update for 10.6.
Single Packet Authorization with simpleSPA
I was just made aware of simpleSPA, a recently-released Java implementation of Single Packet Authorization by Chris Chrysler. The package provides a Linux-only server-side daemon, and a Linux and Windows client. Although I haven't testing it yet, I assume the client will also work on Mac OS X. This implementation requires a Linux server as it relies on iptables being the firewall. simpleSPA uses two RSA keys in order to encrypt the authentication information. The first key encrypts the username of the user requesting access, while the second key is used to encrypt a pre-shared key and a timestamp (for freshness). The resulting authorization packet is then sent via UDP to the server. Although a timestamp is used, there doesn't appear to be an explicit check for previously-received packets (ie. using a hash), so there may be some for time-limited replay attacks. Because RSA and public-keys are used, it's a shame to see that it doesn't use digital signatures to protect the payload.
simpleSPA does not allow for dynamic configuration of the firewall. As such, upon receiving a valid authorization packet (using Jpcap to sniff the wire), the server looks at a pre-defined config file that defines the firewall port(s) to be opened for the requesting user. Furthermore the port is opened for the IP that made the request, thus not allowing the client to embed an IP within the request and open a port out-of-band. The author states that this implementation is primarily an academic proof-of-concept at this point, and so is nowhere near as complete as other implementations such as fwknop (Perl/C) and Aldaba (C). It is nice, however, to see continued research and development into SPA as well as a Java implementation!
Armitage: Metasploit Attack Management GUI
Armitage, by Raphael Mudge, is a great little user interface for Metasploit which allows you to easily discover targets, deliver exploits, and manage your attacks to do things like pivots without any hassles.
Getting started with Armitage in Backtrack 4 R2 is easy. First, start the MySQL DB with /etc/init.d/mysql start (root/toor), and then start the Metasploit RPC daemon:
cd /pentest/exploits/framework3
./msfrpcd -f -U msf -P test -t Basic
Once msfrpcd is running, simply launch Armitage using the script provided and click Connect (you may need to check the Use SSL checkbox).
Armitage is written in Java, and works in Linux, Windows and Mac OS X. Download it here.
[Update] Armitage has been added to the Backtrack repos. Here's a short tutorial, and check out the video tutorial below.
[Updated 21/01/2011] Hak5 episode 882 features a tutorial with mubix and Mudge (Hak5).
Mac OS X Java Trojan Horse: OSX/Koobface.A
Antivirus companies have discovered a new Java trojan horse, labeled OSX/Koobface.A (aka. Boonana), which spreads via social networks including Facebook, MySpace and Twitter. The Java applet masquerades as a video or photo gallery plugin, and requests access to the user's computer.
If Allow is clicked, then the applet will attempt to obtain additional files from remote servers and join the computer to the Koobface botnet. Koobface is also known to try and steal credit card, and other personal information, from the user's system.
I'd like to stress that this is a fairly non-event, and this kind of malware poses a low level of risk (hence the peaceful-looking blue triangle). It's pretty clear that you shouldn't allow websites, plugins and applets that you don't trust, to access your computer. Just click Deny and that's the end of it in this case. Snow Leopard does have some built-in anti-malware functionality, although I don't know if or when it may be updated to detect Koobface. Either way, I wouldn't run out to buy antivirus software just yet.
Note this trojan is not Mac OS X-specific, and also affects Windows and Linux systems.
Intego have a Security Memo with some additional details.
Apple-Produced Java Runtime Deprecated
Following its release of Java for Mac OS X 10.6 Release 3 (10.5 R8), Apple stated that "the version of Java that is ported by Apple, and that ships with Mac OS X, is deprecated". Apple has long been producing its own Java runtime, and Java was originally brought to Mac OS X with the aim attracting Java developers to the platform. Since Java application failed to take off, and Apple's own Cocoa has proven to be extremely successful, they have no real incentive to maintain Java themselves.
This doesn't mean that Java will disappear from OSX, but instead will allow third parties, such as Oracle (who recently purchased Sun) to provide their own Java runtime directly. The Java Preferences app will allow users to manage the different versions of Java that are installed.
From a security perspective, there is one distinct benefit of this change: faster updates. Apple has always been slower to release security updates, and this will allow those updates to reach Mac OS X users in a much more timely basis.


An Ordinance on Patents (Third) Amendment was promulgated by the Government on December 26, 2004 to make the Indian patents law WTO compliant and to fulfill India's commitment under TRIPS to introduce product patent protection for Drugs, Food and Chemicals with effect from January 1, 2005.
billion in exports, is showing satisfactory progress in terms of infrastructure development, technology base and product use. The industry now produces bulk drugs belonging to all major therapeutic groups requiring complicated manufacturing processes and has also developed excellent 'good manufacturing practices' (GMP) compliant facilities for the production of different dosage forms. The strength of the industry is in developing cost-effective technologies in the shortest possible time for drug intermediates and bulk actives without compromising on quality. This is realized through the country's strengths in organic synthesis and process engineering.
The focus under the R&D effort is to encourage development of new molecules. A provision of Rs. 150 crore has been made under the Pharmaceutical Research & Development Support Fund. A Drug Development Promotion Board under the Department of Science & Technology has also been set up for the utilisation of this fund. Feasibility of setting up a Mega Chemical Industrial Estate in the country with world class infrastructure facilities is also being studied. For the first time in many years, the international pharmaceutical industry is finding great opportunities in India. The process of consolidation, which has become a generalised phenomenon in the world pharmaceutical industry, has started taking place in India.
drugs, an amendment to the Indian Patents Act has been carried out through the Patent (Amendments) Ordinance, 2004 on December 26, 2004. The Ordinance amends the Indian Patents Act, 1970 for the third time with a view to introducing product patents for drugs, food and chemicals. Apart from manufacture of drugs, the product patent regime will help the pharmaceutical industry to tap outsourcing of clinical research. By participating in the international system of IPR protection, India, with its vast pool of scientific and technical personnel, and well-established expertise in medical treatment and health care, has unlocked vast opportunities in both exports and outsourcing and has the potential to become a global hub in the area of R&D based clinical research. The Patent Ordinance also provides adequate safeguards to protect the interest of the domestic industry, and the citizen from any increase in prices of drugs.
With a regulatory system focused only on process patents, helped to establish the foundation of a strong and highly competitive domestic pharmaceutical industry which in the grip of a rigid price control framework transformed into a world supplier of bulk drugs and medicines at affordable prices to common man in India and the developing world. Introduction of product patents will, however, mark the end of a golden age for IPI (Indian Pharmaceutical Industry). The new regulations will reshape the landscape of IPI forcing significant changes and divide within the industry.
A look into organization of pharmaceutical producers of India (OPPI) directory shows only 300 units out of 10,000 registered companies are in the organized sector. While process patent helped to flourish IPI into a world-class generics industry, product patent regime will filter the best from the pack and would be favorable to players with built-in scientific and technical resources. The impact of the new regulations will not deter the Indian pharma majors as they are already doing roaring business in the very countries where these patent laws are strictly in force.
Export markets increasingly drive IPI: in a turnover of US$5 billion, exports constitute $3.2 billion and the industry is poised to grow to $25 billion by 2010. The share of IPI in world pharmaceutical market is 1.0% (ranks 13th) in value and 8% (ranks 4th) in volume terms. The global market for generic drugs is estimated at $27 billion (2001) and the expiry of patents on drugs will be worth $80 billion (2005) offers a huge opportunity to IPI. India today has the largest number of US Food & Drug Administration (FDA) approved drug manufacturing facilities outside the US. In addition, Drug Master Files (DMFs) filed by Indian companies with the FDA is 126 higher than Spain, Italy, China and Israel put together. DMF has to be approved by FDA for a drug to enter the US market.
Research & Development (R&D) is a key to the strength of pharmaceutical industry especially in the product patent period. The global pharmaceutical industry spent $30.4 billion (2001) on R&D. The R&D expenditure (as a percentage of turnover) by the IPI is low (1.9%) when compared global giants (1016%). With transition into the new regime many Indian companies are mobilizing their resources war chest with an increase in their R&D budget. Government of India (GOI) encouraged the R&D in pharmaceutical companies by extending 10 year tax holiday to this sector. Besides, planning commission has earmarked $34 million towards drug industry R&D promotion fund for the tenth plan.
FDI in India was low in prior Product Patent era. Why?
Bringing a new drug into the market costs a company an average of about $800 to $900 million. Some estimates show that patient recruitment and medical personnel account for nearly 70 per cent of the clinical costs that are required to bring a drug to market. The less expensive means to raise research productivity is outsourcing research to low cost havens such as India and China. The global pharmaceutical outsourcing market stands at $10 billion (2004). Pharma multinationals have maintained a low-key presence in Indian market due to absence of product patents and rigid price controls. Pharmaceutical industry did not receive significant foreign direct investment (FDI). From August 1991 to December 1998 this industry accounted for a meager 0.44% of the total FDI. Introduction of product patents will see multinationals strengthening their presence in the country. The second largest population in the world, a growing economy and rising income levels makes Indian market difficult to ignore. Global companies would be reluctant to invest in a country where there is no IPR protection. Eli Lilly (world's 7th Largest Pharma Firm) has its clinical research focus in the country and had spent considerable amounts over the last 2-3 years. But we would be only maintaining the quantum and will not expand even though there is huge potential. Global companies face the same frustration.
So the main activity of the company in the country would be to introduce products from the parent pipeline.mIn the domestic market, the share of Indian companies has steadily increased from around 20 per cent in 1970 to 70 percent now. Ranbaxy Laboratories is the market leader in terms of revenues followed by Cipla and Dr Reddys Laboratories. Glaxo is the only multinational to figure among the top ten pharma companies in India. In India, 97 per cent of drugs are off patent and are manufactured by a vast number of companies. The key therapeutic segments include anti-infectives, cardio vascular and central nervous system drugs. Anti-infective comprise the largest therapeutic segment in India, accounting for about 26 per cent of the market.
Globally, pharmaceutical industry grew at a compounded annual growth rate of 9.1 per cent in the last 23 years to $491 billion propelled by a string of innovative blockbusters. Multinationals were reshaped by mergers and acquisitions as a way of fattening their research pipelines. This at best represents a short-term solution. With a slew of brand name drugs losing patent protection in the next few years and the pressure building for pharmaceuticals to cut price, these giants find themselves under immense strain to find new drugs and reduce price.
So, from the above discussion it's very evident that before any proper IPR regime specially in the absence of "Product patent" in India it was not a judicious decision for the international Pharma companies to invest here in India. FDI cap was raised from 74% to 100% in 2001 only but we didn't find any change in the pattern of FDI in Pharma Sector.
India a signatory to the WTO resolution on TRIPS Agreement India was thus committed to recognising product patents by amending The Indian Patents Act 1970. As per the minimum standards mentioned in the TRIPS agreement, patent shall be granted for any inventions, whether products or processes, in all fields of technology provided they are new, involve an inventive step and are capable of industrial application without any discrimination to the place of invention or to the fact that products are locally produced or imported. Accordingly, now patents will have to be granted in all areas including pharmaceuticals and the effective period of protection is for twenty years from the date of filing the application. With the implementation of TRIPS agreement by most of the developing countries by 2005, a stronger patent regime or product patents will be uniformly applicable on the pharmaceutical innovations among the member countries of the World Trade Organisation.
The implications of TRIPS for the pharmaceutical sector are that: patents will be granted both for products and processes for all the inventions in all fields of technology; the patent term will be twenty years from the date of the application (compared to the seven years under the 1970 Act), which is applicable to all the member countries and thus rules out all the differences in the protection terms prevailed in different countries; patents will be granted irrespective of the fact whether the drugs were produced locally or imported from another country; though the grant of the patent excludes unauthorized use, sale or manufacture of the patented item, yet there are clauses which provide manufacturing or other such rights of the patented item to a person other than the patent holder. In the case of a dispute on infringement the responsibility (to prove that a process other than the one used in the patented product has actually been used in the disputed product) lies with the accused rather than with the patent holder (in the 1970 Act, the responsibility is with the patent holder). This is the broad framework, which will guide the pharmaceutical industry of India in the WTO regime ( i.e. post 2005 period).
In order to increase the global prospects of the pharmaceutical industry in the post 2005 period, the Central Government has fixed the deadline of December 2003, to comply with the Good Manufacturing Practices set by World Health Organisation. Since this is mandatory for all the units, it means incurring expenditures that could range from Rs. 15 lakhs to 1 crore per unit. In some cases, it would involve shifting to new premises altogether. A few units might exit from business because of this. As contract manufacturers it is essential that both the parent unit and the loan licensee meet these requirements in cases where the production is meant for exports. While these standards improve the quality on par with international standards, it will also act as potential entry barriers for new firms to enter.
The strength of the Indian pharmaceutical industry is in reverse engineering. Such units by utilising the provisions under compulsory licensing, exceptions to exclusive rights and the Bolar exception should aim at producing the generic version of the patented product and those that are nearing patent expiry. Such firms should also be engaged in research leading to new drug delivery mechanisms and in identifying new uses of existing drugs. In this context, it is also essential to protect the innovations that have been introduced by the technology spillovers. It is suggested that in order to develop domestic innovations, developing countries require utility models or petty patents. These petty patents can be available for a shorter period of time for process innovations made over an existing product. The TRIPS agreement leaves members to introduce such legislation, as there are no specific rules on this subject. Such patents will encourage the small firms.
One of the concerns regarding product patents is the access to patented products. Some of the provisions within the TRIPS agreement clearly indicate that price controls could be imposed on the patented products. However, exemptions from price controls has been suggested by the government for the products that are produced domestically using the domestic R&D and resources and are patented in India. Such exemptions will keep the prices high and make access to the drugs difficult. It appears that `who patents the product' matters more for the government than what is patented. In the recently concluded Doha meeting, a separate declaration on the TRIPS agreement has clarified that members have the right to grant compulsory licence in the area of pharmaceuticals and that they have the freedom to determine the ground upon which such licenses are granted, which can have a considerable impact on the availability as well as on their prices. However, the amendments made by the Government of India, make the procedures very cumbersome which needs to be revised in the third amendment to the Patents Act. While parallel trade in pharmaceutical may facilitate access to medicine, yet compulsory licence will be the only course of option to facilitate flow of technology and R&D. Scherer and Watal (2001) suggest that tax concessions should be provided to the pharmaceutical manufacturers to encourage them to donate the high technology drugs to the less developed and developing countries which is a viable option.
A majority of the population does not have access to the essential medicines (most of which are off patent) either in the government or private health care systems because they are not within their capacity to reach. Now that the percentage of drugs under price control has been reduced drastically it is essential to keep the prices of the essential drugs under check, especially those concerning the common diseases.
Currently only a handful of pharmaceutical firms in India invest in R&D which needs to be improved. The Pharmaceutical Research and Development Committee (1999) has suggested that a mandatory collection and contribution of 1 per cent of MRP of all formulations sold within the country to a fund called pharmaceutical R&D support fund for attracting R&D towards high cost-low-return areas and be administered by the Drug Development Promotion Foundation. The domestic universities and other academic institutions can play the role of research boutiques or contract research organisations (CRO), which can supply the technical know-how and manpower. Units that already have such facilities can also function as a CRO for other firms.
In the post TRIPS era, the government will have to probe in to factors that contribute to the widening gap between the proposed FDI and the actual FDI and rectify these bottlenecks. Similarly the difference between the number of patents filed and the patents granted calls for a detailed analysis to figure out where the Indian firms are lacking.
Governments at various levels should take active part in disseminating knowledge about the IPRs and the possible strategies that can be adopted by the industry. This will remove some of the impediments. Lessons should be drawn from the Chinese experiences where systematic efforts were taken to educate the bureaucrats, policy makers and the industry about the WTO and product patents in the pharmaceutical industry. India will have to strengthen the patent examination process and speed up the processing procedures. This will help in checking the products that may enter the country utilising the import monopoly route provided by the EMR. Besides a strong institutional and judicial framework will have to be set up for monitoring the prices, to prevent infringement and trade dress cases of patented products respectively.
As far as India's pharmaceutical industry is concerned, various options are possible in the WTO regime. These are to: (a) manufacture off patented generic drugs, (b) produce patented drugs under compulsory licensing or cross licensing, (c) invest in R&D to engage in new product development, (d) produce patented and other drugs on contract basis, (e) explore the possibilities of new drug delivery mechanisms and alternative use of existing drugs, and (f) collaborate with multinationals to engage in R&D, clinical trials, product development or marketing the patented product on a contract basis and so on. Besides these strategies, India's strength lies in process development skills. This expertise utilised within the WTO framework with emphasis on quality standards will provide India a competitive advantage over other Asian countries.
To conclude we can anticipate more FDI nature of investment in India in the field of Pharma Sector?
It's a question which requires more time to be answered, but we can draw inferences from the facts & data discussed above. As from the above discussion it is obvious that Pharma industry is high investment seeking industry, & the other most important fact about it is that it require enormous R&D. The new Patent regime brings both opportunities and challenges to the domestic pharma industry. Even larger Indian companies lack the financial muscle to be major international player in basic R&D, that involves discovery of new chemical entities (NCEs). They would be helped by the government's decision not to restrict patenting to NCEs. The Patent Ordinance issued recently defines the term patentability as per the TRIPS guidelines but does not exclude patenting of incremental inventions like new drug delivery systems, polymorphs etc, brightening the chances of Indian companies to benefit from the patent regime, but it may act as a disincentive for the international Pharma firms to invest in India.
1. Deletion of the provisions relating to Exclusive Marketing Rights (EMRs) (which would now become redundant), and introduction of a transitional provision for safeguarding EMRs already granted.
2. a) Conditional grant of patent (Section 47) : Empowers the Government to import, make or use any patent for its own purpose. For drugs, it also empowers import for public health distribution.
3. Revocation of patent in public interest (Section 66): Empowers the Government to revoke a patent where it is found to be mischievous to the State or prejudicial to the public.
4. Grant of compulsory licence (Sections 82 to 94): Chapter XVI deals with the general principles and circumstances for grant of compulsory licences in order to protect public interest particularly public health and nutrition. These provisions check the abuse of patent rights. They can be invoked if the reasonable requirements of the public with respect to patented inventions have not been satisfied, and the patented invention is not available for public at a reasonably affordable price, and if the patented invention is not worked in the territory of India. Section 92 of this law provides for action in case of national emergency, extreme urgency and public non-commercial use, and can be invoked without the grace period of 3 years from grant of patent.
5. Use of invention for the purpose of Government [Sections 100 & 101]: Compliments Section 47.
6. Acquisition of invention and patent for public purpose [Section 102]: Empowers the Government to acquire a patent to meet national requirements.
7. Bolar provision [Section 107 (A) (a)]: Facilitates production and marketing of patented products immediately after expiry of the term of patent protection by permitting preparatory action by non patentees during the life of the patent.
8. Parallel import [Section 107 (A) (b)]: Provides for import so that patented product can become available at the lowest international price.
These provisions are basically public interest provisions but these are anti FDI in nature because in a sector of high investment & high uncertainty every investing firm need complete protection & patronage but here it is not guaranteed.
So we can anticipate that product patent is going to have a very little impact on the FDI scenario in a country like India.
Business leaders agree that human capital is the most important asset in organizations. Ironically, particularly in medium-sized and large companies, it is often the most underutilized asset. Although fierce competition is driving an increasing number of workers to put in long hours, the additional hours have not translated into optimal results in organizations where the traditional approach to work has not changed.
This approach disproportionately focuses on motor skills, leaving much of an organization's brainpower untapped. Dutifully repeating the same tasks and routines is only a fraction of the capability of the worker. As a young sculptor, Michelangelo applied for a job at a commercial studio. The owner of the studio informed him that he knew exactly how long it took to carve each piece and that his deliveries were never late.
Obviously, Michelangelo never worked in this studio. At a time when customers constantly demand new and fresh ideas, and when visionary companies identify and create markets to meet needs the customers didn't think of, many businesses still operate like that studio owner.
The solicitation of habitual productive thinking in an organization is often limited to management, which is typically 5 percent to 20 percent of the workforce. When professionals, such as accountants, lawyers, and human resource leaders, are included in the productive-thinking process, that still leaves out more than half of the workforce.
Why is productive thinking critical?
Like all human accomplishments, every business begins as a thought. The inventions, innovations, and small improvements that differentiate winners from stragglers result from thinking. Productive thinking involves actively engaging the brain in seeking ways to improve the process and product of work. It seeks better ways of performing routine tasks. It applies talent and experience in generating novel and profitable ideas. As foresighted and innovative as management might be, the thinking that's likely to have the greatest impact on improving processes, products and services emanates from the people closest to the work. Organizations that understand this fact include frontline staff in an organized productive thinking process.
Under Jack Welch's leadership, GE caught on to the infinite potential of a thinking organization.
Initially, company culture did not allow workers to safely and regularly share ideas. But after conducting tens of thousands of workout sessions — during which employees in groups of 10 to 100 shared their opinions in off-site meetings — acting on employee input became a way of life in the organization. GE harvested hundreds of innovative ideas from non-management employees through these sessions. Welch provides additional detail on the workout sessions in Winning, a book co-authored with his wife, Suzy.
Toyota is perhaps the best example of how continuous productive thinking facilitates enduring success. Following years of steady growth, the company has surpassed GM as the No. 1 automaker. While GM closes plants, offers buyouts to workers and recently posted a $38.7 billion net loss for 2007, Toyota has been expanding factories and building new ones around the world to meet rising demand. How could there be so much discrepancy in one industry? What makes Toyota so audacious, resilient and successful? The efficiency, durability and freshness of its automobiles are a typical response to the latter question. But high-quality products are only a manifestation of the company's success formula. Its efficient production system is a more veritable success factor. Then again, virtually every automaker implements a form of lean production.
In Jeffrey Liker's book (the critically acclaimed The Toyota Way), independent research and public comments by Toyota executives suggest the key to Toyota's success lies in two principles that are rooted in the company's culture: The company relentlessly seeks ways to improve and it engages the entire workforce in a continuous improvement process. Toyota provides leadership, structure, incentives and an environment that motivates employees to constantly think of ways to improve business processes and outcomes.
In a 2005 presentation entitled Conflict in Time: Strategy for the 21st Century, Chet Richards, a lecturer and management consultant who teaches organizations how to apply successful military strategies to winning in business, used the German word auftragstaktik — which translates to missions and contracts — to describe the empowering relationship between management and staff at Toyota. Instead of assigning tasks, work teams are given a mission. The impact of each mission on the larger organization is clearly communicated. This enables the teams to understand the big picture and to function effectively as parts of a system. Contracting allows staff to challenge or question the feasibility of a mission, thus gaining commitment from each member.
Within defined, broad parameters, each team seeks out the most efficient way to execute its mission. The "seeking out" process, as well as the continuous-improvement culture, allows productive thinking to flourish. Identifying opportunities for improvement or coming up with ideas for new products is as important as meeting delivery goals. At all levels of the organization, there is no resting on laurels, no plateaus and no satisfaction with the status quo.
This is the Toyota way of life. As a result, a typical Toyota plant makes thousands of changes to its operation each year. These seemingly infinitesimal changes translate into invincibility in the marketplace. Employees gain dexterity from performing the same tasks over a long period of time. At Toyota, they also gain fingerspitzengefuhl, a Zen-like quality of intuitive understanding that results from cumulative productive thinking.
This sort of prodigious insight enables workers to "think without thinking" — to borrow Malcolm Gladwell's Blink phraseology — and maximize their workday. What's more, it produces the foresight and momentum that can propel any organization to the top of its industry. Considering that the creative potential of the workforce is infinite and because people closest to the work are actively engaged in the innovative and constant improvement process, Toyota will continue to reinvent itself long into the future.
Toyota's business model will yield positive results for any organization that has the courage to implement it correctly. How long the results last will depend on whether the organization has the discipline to sustain the model over the long haul.
Productive thinking requires entrepreneurial acumen. Entrepreneurship thrives in an open, empowering and inclusive environment — an environment where fingerspitzengefuhl can be achieved.
Are productive thinkers made or born?
Productive thinkers, over time, begin to consistently deliver exceptional results. At the height of their achievements, they make contributions that change the course of history. Celebrated as geniuses and high achievers, Galileo Galilei, Leonardo da Vinci, Albert Einstein, Thomas Edison, Marie Curie, Sam Walton, Bill Gates and countless others were productive thinkers.
In their research, the Swartzes found that each of these individuals spent at least 10 years in preparation, developing their thoughts and perfecting their crafts. They point out that by the time Edison discovered the fluorescent electric lamp, he knew more about the gas industry and the research of other electric-energy pioneers than anyone of his day.
As Louis Pasteur, world-renowned French biochemist, rightly noted, "In the field of observation, chance favors only the prepared mind." All types of workers can reap the rewards of productive thinking. While many may never achieve the fame of the greats, they will make contributions that can transform their organizations.
How do you improve productive thinking?
Learning is the fuel for productive thinking. The more relevant knowledge and information you absorb and process, the greater your chances of experiencing moments of illumination.
In addition to formal education, books, periodicals and daily business news, trade meetings and conferences are excellent ways to keep the brain sharp and stay on top of your trade. People who understand the competitive advantage of timely access to information seek and consume it voraciously.
The partnership that birthed Microsoft originated from a magazine article entitled "Breakthrough: World's First Minicomputer." Struck by the photo on the cover of the magazine, Paul Allen picked up a copy and shared it with Bill Gates. The following day, both men called Altair, the manufacturer of the minicomputer, to discuss the possibility of running their software on the machine, according to the Swartzes' book. What happened next is history. According to Bryan George, a world-class executive coach quoted in HFN [Home Furnishings News] magazine, CEOs of Fortune 500 companies read, on average, four books per week. Busy professionals have others regularly scan news and periodicals for topics of interest. They can supplement reading with audio books.
Cross training or learning something new, such as an innovative way of performing tasks or a different language, will help keep the brain stimulated.
How do you become a thinking organization?
* Are you utilizing only a small portion of your organization's brainpower?
* How effective is your organization's process in eliciting ideas for improvement and innovation from your entire workforce?
* How critical is continuous improvement to your organization? How is it operationalized?
* Are your creative and critical thinking skills rusting from lack of use?
* How often do you feed your brain to keep your productive thinking skills sharp?
* Cultivate a culture that's not only safe for employees to share ideas, but that motivates them to do so. Organizational norms, structures, systems and processes should be designed to facilitate ongoing improvement-oriented input from all strata of the workforce.
For instance, thinking skills should be assessed during job interviews and employees should be given the freedom to innovate. Employers should provide necessary resources, encourage experimentation and define acceptable levels of mistakes and failure. Performance management and reward systems should facilitate creativity and innovation.
* Infuse your organization with a continuous-improvement mentality.
Continuous improvement must rank in the top 10 management cliches. However, its relevance as a catalyst for growth is not diminished by time or frequency of mention. A mind-set that incessantly questions the way things are with an eye to bettering them is the most relevant ingredient in engaging the intellect of your workforce.
* If your organization is unwilling to embark on large-scale transformation, practice positive disobedience.
No, this concept is not a recipe for a pink slip. It's a term used by the Center for Creative Leadership to describe the action of subcultures that model a behavior for the larger culture. It's behavior or action that yields superior results, even though it might differ from the mainstream.
In the 1950s, a malaria outbreak killed thousands in a small town in West Africa. Smack in the middle of the town were about a dozen families who were not affected by the disease. These families took steps such as avoiding stagnant water around the house; burning leaves, which repelled the insects; and shutting their windows at night to avoid mosquito bites.
Their neighbors sometimes mocked them for refusing to grow certain plants — plants that attracted mosquitoes. But these actions preserved their lives. To suggest that the vast majority of the workforce is underutilized might be counterintuitive to many workers and employers. Some might even find it offensive. But as Toyota demonstrates, there's more to the line worker than performing a task by rote or making the monthly or quarterly numbers.
Employees whose minds are actively engaged in improving processes, workflows and results are unlikely to suffer from boredom or the drudgery of repetitive work. In fact, countless worker-satisfaction surveys highlight the lack of inclusion in decision-making processes as a major source of dissatisfaction. Engaging the intellect of all your workers is not only a smart business decision, it is a demonstration of respect and recognition for what they have to offer.
Published by permission of LRP publications 2008.
In business, facilitation refers to the successful running of a productive and impartial meeting, without leading or distracting the group from the main goal. Facilitators aid in meetings that involve a decision, finding a solution to a problem, and in the exchange of ideas for discussion.
Meetings are the perfect avenue to do find resolutions as they are where people come together to work on a common goal. Good and effective meetings include: a) one goal that everyone is familiar with. b) A plan to reach those goals. c) Understanding that everyone comes from different backgrounds, therefore, opinions and points of view will vary. d) A sense of responsibility and involvement with the company and one another.
One single and simple way to achieve a successful meeting does not exist. Building synergy usually comes with experience and cooperation from the group. The more people are aware of good group dynamics, the easier the job of the facilitator, and the more productive the end result.
The role of the facilitator – Their task is likened to that of a mediator, who helps out in the process during a grievance, but not involved with what occurred before and what happens after. These are individuals who assist groups of people to effectively reach a goal and work diligently toward accomplishing that goal. They do not take sides or express their points of view, allowing the floor to discuss among themselves.
Consulting with the client – A third-party individual affiliated with another company may seek out the help of the facilitator. This usually involves business deals that include both companies to work together towards a common resolution. The facilitators will be able to understand the purpose and the best expected outcome.
Making arrangements – The meetings are to be arranged and managed by the facilitator. This involves the location to be approved by the participants and the acceptance of the invitation by those needed to be in attendance. This aspect involves researching for background information regarding why the meeting is being held in the first place.
Setting the agenda – In compliance with the previous aspect, this field also involves understanding in detail how each goal can be reached and how long it could take. With enough experience and practice, facilitators explain to the participants the issues and each possible course of action.
Understanding group norms and dynamics – Given that everyone comes from a different department, culture, and background, having enough knowledge on group dynamics can help the overall flow of the discussion and the productivity of the team. Facilitators should not make assumptions, but rather adapt to the differences. They should also be aware of the body language of each participant, and make sure everyone is comfortable.
All of these aspects make up sound and effective facilitation, with improvement noticeable in following meetings. At the conclusion of each gathering, everyone should have a sense of what to expect at the next showing. It is also critical for the facilitators to ask the group what they believe should happen next.
Anybody planning to undertake online business opportunities is concerned about two major concerns. The first is to be able to generate the maximum amount of quality traffic. The second is to succeed in promoting and selling one's goods and services successfully. You only have to develop your networking skills and get a computer with an internet connection. However, you also need to do product research, determine customers' demands, budget and spending habits.
Incidentally, it is a must for internet marketers to examine and understand the online habits of consumers. This is important in getting their attention and conveying to them the right message for internet marketing campaigns. Take note that each user has peculiar preferences and tastes even with respect to the use of the internet. Study online habits of people carefully so you will know how to send a marketing pitch. The ability of an online strategist to formulate a thriving internet marketing program is one of the keys to a highly successful venture. Along this line, there is a need to build and put into action short and long-term strategies to stimulate more traffic to your site. Your technique must be effective to sustain the efforts and time that you have invested in your venture.
Building a strong brand is also a priority in internet marketing. You need to invest in your brand online by putting a concept that will reverberate with your customers and compel them to come back again and again even when there is no promotion to attract them. Fortifying your brand online will undoubtedly intensify responsiveness and bolster your reputation. The more people are fascinated by your brand, the greater the chances that they will assertively look for it and stay loyal to you. One thing is sure that you will reap benefits for investing so much in your brand. Your brand is what customers, friends, suppliers and rivals know and understand about your enterprise. The conviction of most people is that the theory of branding is all about logos and packaging. Actually, the aspects of branding – logos, websites, marketing, layouts and content are not all that matter. There are other essential factors such as enhancing customer knowledge and interaction with your commodities and services. It is vital for you to make use of marketing tactics and strategies to facilitate continuous interface with your base of customers.
In online business opportunities, you also need to realize the value of social media. Social media can make three things happen. One is to grow the business, the second is to make a business organization more personal and lay down the essential foundation for success in any online activity. This concept also makes us understand about social networks and brand conversations. When entrepreneurs use this approach, it starts to generate viable results. The brand becomes known and benefits about this brand spread rather quickly. Conversations begin to happen and proposals are passed from a single individual to hundreds and even thousands of uninformed people. Positive information saturates the search engines and the result is website traffic shoots up. Social media marketing does not stop at acquiring potential customers. It transforms these customers into loyal and satisfied patrons. You are then assured of a thriving business.
Group Facilitation: What Factors Are Controlled to Nurture Efficiency?


Pork rinds make for a great snack, and can make a great substitute for those on the keto diet, but how do you know which ones to buy?
What Are The Best Pork Rinds in 2019?
Paleo-friendly and Keto-friendly. Low in carbs, high in protein.
Easy. Substitute 1 to 1 for bread crumbs in any recipe.


Trees Breeze: Sweetwater 905: festival season opener!
Sweetwater 905 Arts Festival outside Rolla, B.C. tripled in attendance from its previous year despite this past weekend's rain! A stellar line up of musicians and artists from around the Peace, province and continent ensured that this ninth year will not be the festival's last.
The captive, appreciate audience at Sweetwater could be an audience at any northern music festival: inclusive, quick to dance and not shy about showing off their varied footwear and outfits.
Many of us, musicians and audience alike, neglected to change our tops, nevermind undies for the duration of the three-day festival. Impossibly over-made-up gals jived alongside the Mattsons, venue hosts and owners of the property, while plaid-clad dudes in cowboy boots freshly wreaking of the cow paddy camping field mingled with the requisite hippies these events attract dressed in their baffling numbers of coloured layers.
Fire show performers deserved their own spot instead of inappropriately entertaining during an energetic performance from Ben Caplan.
Other standout sets were delivered from Samantha Scott, High Society and Dave Soroka and his "band." Can't wait to hear you all again!