Ounces equal pounds, pounds equal pain. This is a principle taught to Marines in basic training as they get ready for their first hikes. Once they get past mandatory gear lists in the training environment, Marines quickly learn the ultralight backpacking essentials. This is how service members can carry everything they may need for 8 to 15 months in an austere environment. Sticking to that minimalism mindset allows for quicker pack up and set up time while lessening the load on the body.
In the backpacking world, this has grown into an entire subculture of ultralight or minimalist camping. I want to apply that further, and see some ultralight backpacking guidelines or ideas applied in the survivalist circles.
Undoubtedly, your 'go-bag' or any pre-staged survivalist gear is the result of months of planning and research. While you may have the best water filtration system, collapsible shelter, or other gear in that bag, chances are that nearly any go bag weighing over 45 pounds could stand to lose 10 to 20 pounds.
Some figures state that 25 to 33% of your body weight should be a manageable amount of pack weight but even that can cause unnecessary fatigue and wear on your joints. Carrying 33% of your bodyweight for long distances also takes dedicated training that most don't have time for.
The reality is that even with a go-bag, you are likely to be physically unprepared for a survival situation. Carrying a lightweight pack will cut down on that fatigue caused by heavy packs. Keeping these ideas in mind, we will explore some ultralight backpacking gear options to help you save those ounces and ultimately save you some pain!
As we are all aware, food is one of the top priorities in any survival situation. Food can be one of the heaviest items in your pack as well. In survival situations, you can carry or catch your own food, basically. If you carry food, it should only be as a backup anyway and for the lightweight survivalist, space food is the go-to.
Dehydrating food removes all of the moisture and therefore a substantial amount of weight. Most dehydration processes cut weight by half or even two thirds. Contingent on water sources, you could reasonably expect to carry 4+ days supply of dehydrated food.
The other option is to catch your food. This is entirely dependent on the skill level of the survivalist and where you are encouraged to play to your strengths. However, if you're comfortable with one method you should not rest there. For example, if you're competent at setting traps, find a dual purpose for the snare twine and learn improvised fishing.
Finally, food preparation. With the number of different ways to heat or prepare food, we are going to stay focused on the ideal lightweight survival option. The MSR Windburner is a modular stove system weighing in at 15 ounces. Those 15 ounces though can serve many purposes, including being able to boil up to 19 liters (5 gallons) of water with just one fuel pod.
The integrated pot can be used to prepare whatever food you catch or carry as well. It converts easily into storage mode and won't take any more space than a medium-sized thermos.
The MSR Windburner packs a serious multipurpose and lightweight punch.
Water is probably the single most important priority in any survival situation. While it can be tempting to travel with as much water as you can, that too can add up to some painful weight to be carrying around.
Simple solution: Carry a portable water filter. Depending on the situation, an adept survivalist is likely to be traveling from one water source to the next. It's important to keep some water on your person while traveling, but keeping a filter will sustain you longer and is a good alternative if there's no time to boil water.
A shelter can be the second to third priority in any variety of survival situations. However, when assessing weight, a shelter can become one of the biggest burdens to our load-out. While tents are the most familiar to assemble and sleep in, they are sometimes the heaviest shelter option.
Consider the modern backpacking hammock. Much simpler than any tent, you just need two trees to stay warm, dry, and completely off the ground. With a compression sack, you can get this 1 to 2-pound piece of gear compressed into a very small package. For the survivalist looking to save weight and streamline the shelter setup process, a hammock is a great alternative to traditional tents.
A cuben fiber rainfly and hiking poles can be repurposed into a quick personal awning.
With the plethora of medical kits available, it's easy to get drawn into sometimes unnecessary features, serving the assumption that one medical bag should serve multiple people.
The reality is that the medical kit everyone carries should be completely self-serving. Especially in survival situations, you may just need whatever it takes to keep going, hence some OTC medication.
Anything besides the medications and sanitizing aids can be improvised, like tourniquets, splints, slings, and bandages. Sticking to the absolute essentials can shrink your medical kit to the size (and weight) of an Altoids tin.
Especially in a first aid kit, it's important to keep everything labeled.
Shaving weight off your pack will not only lighten the load on your back but your feet also. If you apply this seriously and bring your pack weight down to below 30 pounds, it is worth considering changing your footwear too.
A lot of hiking footwear is designed to support the heavy weight that the hiker is expected to be carrying. If you're cruising with a 25-pound pack though, chances are that those boots are going to end up causing blisters because they aren't being fully employed. Consider switching to lightweight trail shoes like Merrell's or other similar brands.
At its core, applying the ultralight backpacking mindset to survival preparation is a great way to take inventory of what you should reasonably expect to carry. Lay out whatever gear you have now and assess each piece with these questions.
How many purposes does this gear have? Dual or multiple purposes per piece of gear is going to be more valuable than a single-use item.
Is this gear unnecessarily heavy, and if so is there a better way to get the same task done with a lighter or simpler piece of gear?
All too often, we can get drawn into the features of interesting (but heavy) gear and end up never using it. Avoid that waste by asking these critical question.
Thanks to Stephen Escallier, ultralight enthusiast, for sharing his insight and gear with us.
In conclusion, taking all of your gear through this weight-saving inventory is a good exercise, whether you plan on changing your load-out immediately or over the next few months. Make your ultralight backpacking gear list with these backpacking tips and tricks in mind!
What do you think of applying ultralight backpacking to your survival preparation? What did you not realize was adding weight to your go-bag? Share with us in the comments section!
Check out the Survival Life Store for gear and tools to help you prepare for SHTF situations!
Editor's Note: This post was originally published on April 10, 2017, and has been updated for quality and relevancy.
Think we all have packs that csn go on a diet. Military has tents weighing 4lbs and zipped shelter under 3lbs. So incoroating some military into your backs csn give some bonus features.
Good luck stopping a deadly bleed with that first aid kit. The tin alone weighs more than guaze. Best light weight method is the big three sleeping, shelter and pack.
Learning to utilize your gear in ways other than the original design concept needs to be discussed. Your article is missing some very important information, understandably it is a good starting point.
People that backpack should learn to use handkerchiefs, clothing, tarps, tent poles/trekking poles in emergency medical situations. You should address this in your first aid kit section. Using skills is an "ultralight" backpacking core skill that we base our hikes on and end up coming out of the woods alive while only carrying a baseweight of 5-12lbs.
Another simple issue that could be fixed and serve dual purposes is axing that MSR stove. *(MSR is a great company mind you! I love their Drom bags and suggest everyone drop their bladder in the trash and buy this NOW.)* Unless you're using it above tree line/winter. If you are actually serious about a stove that CAN do it all, weighs next to nothing AND burns multiple fuels…head on over to Flat Cat Gear and look at the isopro stove. On his site you'll find other options considering alcohol only and solid fuels only. However the isopro burns darn near EVERYTHING; weighs an ounce and is made of stainless steel while, like stated earlier will burn chemicals that no other stove will while creating no obnoxious black soot. Other serious options for survival situations are the Firebox Nano. Which burns the heck out of some tiny sticks/fuels and can fit Trangia.
As an avid solo backpacker I can advocate for the workmanship and quality of these two small American companies. I prefer alcohol/esbit to propane or butane for the availability. I can buy yellow HEET at almost any gas station and have a weeks worth of fuel for my meals while hiking. The safety factor is better compared to butane/propane/isobutane. Try lighting both in your tent vestibule or under a tarp…an alcohol or esbit stove is safer, a canister stove may soccer ball on you! Always VENT!
Tarps/mids are always more versatile than hammocks and traditional tents. Something 9 to 10 feet for a tarp is perfect! For a mid 9 by 9 is great! If you want to save money look for silnylon as opposed to cuben fiber/dyneema. I own both materials and they are equally outstanding in their own right but have their own cons. I can touch on a few but do your research and don't fall for "survival" company gimmicks.
Silnylon is much easier to pitch; has stretch to the material and is easier for first timers. It needs to be seam sealed!!! This may need to be done occasionally! It has a great hydrostatic head rating of you opt for the 30D!!! This is great and any more is overkill. In winter time use, snow slips right off the material. Silnylon will droop when cold/wet so I always re-tension before bed with no issues.
Cuben fiber/now known as "dynemma"; is a lightweight, strong but also fragile material, IKR…; you will need to handle it with care to make it last. You cannot stuff it like silnylon as it gets "pressure points" easily. It is hard to pitch as there is no stretch. You do not ever have to seam seal dynemma! Completely waterproof, but also not breathable so always vent to avoid condensation; it is expensive and honestly after using both materials the few ounces I've saved I prefer silnylon for shelters.
For recommended shelter companies please check out Yama Mountain Gear, Mountain Laural Designs, Seek Outside and my all time favorite Tarptent. All are American companies.
Packs!! Never, EVER buy a pack until you have ALL of your gear! I recommend ULA packs. I've used the same one for years, great customer service, I recommend the Catalyst for larger loads or the Epic for those looking for serious use. I have friends who hunt and loveeee the Epic!!
Hope my experiences and information help. Remember Google is always available. Check out all the backpacking websites and YouTube videos!!


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Home Celebrity Ariana Grande: The K-Pop Group That Should Be Made
Ariana Grande: The K-Pop Group That Should Be Made
K-Pop gained great recognition in the charts around the world, but another star that is always present is the singer of Positions and we know that a collaboration between both styles would be perfect.
From the beginning of her career, Ariana Grande has demonstrated her great talent, not only through her vocal strength, but also through the creative process to prepare each of her albums. The musical style of this girl has become iconic and unmistakable, what would a collaboration with some idols look like?
In the South Korean industry there are powerful vocalists and groups that have also shone with their incredible voices, some of them have revealed that they are fans of the Florida-born singer, others have even interacted with her and some more simply have the ideal voice to join the girl.
K-Pop fans have pointed out how great it would be if Ariana Grande had a collaboration with an idol, but while we wait for that dream to come true, we tell you about some artists that we wish to hear in a song with this girl.
IDOLS WHO SHOULD COLLABORATE WITH ARIANA GRANDE
Ariana Grande and the BLACKPINK girls in the same song would be a guaranteed success. In addition to the girl power that this melody would have, the talent of each of the singers would be more enough to conquer the public with a track full of energy and charm.
Another talented K-Pop group that could collaborate with Ariana is Red Velvet. Yeri is a big fan of this girl, she follows her on Instagram and has shown her admiration for the singer on several occasions, if we add to that the vocal talent of all the members we can be sure that the song would be fantastic.
Ariana Grande tackled in a piece of Eminem: the fans do not validate!
WHEIN BY MAMAMOO
This vocalist is also a fan of Ariana Grande, she revealed that as a trainee she sometimes presented covers of the singer for her evolutions, she also performed an Ari melody at MAMAMOO's live shows but best of all, her musical style as a soloist it perfectly matches that of the 7 Rings performer.
YOUNG K FROM DAY6
Although this singer has not made mention of Ariana Grande and her music, he has a melodious voice and broad acting abilities so we believe that he is a clear example of male idols who would be perfect to collaborate with the girl.
The Big Hit Entertainment group could not be left out of this list, the union of their voices and that of Ariana Grande would probably be a phenomenon in the music industry, not only because of the surprising popularity of both, but also because of their singing style. . They even met during their rehearsals before performing at the 2020 GRAMMYs and captured the moment in a photo.
Ariana Grande received an engagement ring and this piece of jewelry has a very special value, know the details at Nación Rex.
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redpajama_set_name: RedPajamaCommonCrawl


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Home About Us News & Events Skills Forward Roadtrip
Penn Foster, Roadtrip Nation and Strada Education Network Introduce "Skills Forward Roadtrip" at 2017 ASU + GSV Summit
Roadtrip to Engage and Inspire America's Next Generation Workforce and Address Talent Gap for High Demand Skilled Trades Occupations
SALT LAKE CITY, UT, (May 9, 2017) — Penn Foster, Roadtrip Nation and Strada Education Network together at the 2017 ASU GSV Summit will introduce the Skills Forward Roadtrip, underscoring their commitment to address the acute shortage of skilled trades talent costing the U.S. economy billions each year. The Summit has assembled a group of CEOs from more than 400 of the world's most important enterprise learning and talent companies to discuss education and technology innovations.
As part of the Growth Company Presentations on Wednesday, May 10, Penn Foster CEO Frank Britt will share Penn Foster's work in addressing this challenge through building bridges between local and national organizations to improve worker employability and workforce effectiveness. Britt will discuss Penn Foster's unique business strategy, built from a foundational direct-to-consumer model, designed to connect the dots to help workforce development organizations achieve the greatest impact possible.
"Amidst the backdrop of a large, complex, and fragmented workforce development system, Penn Foster has evolved to become a partnership enablement company focused on employment outcomes," Britt said. "At the ASU GSV Summit, we're calling on all stakeholders invested in moving the needle with education and technology innovation to help us lead the charge towards a connected workforce development ecosystem."
To further propel this agenda, Britt will introduce a new partnership effort with Roadtrip Nation, a Strada Education Network company. In an effort to expand the pipeline of qualified talent entering the field of skilled trades, these organizations have teamed up to offer the Skills Forward Roadtrip, an immersive career exploration experience in which individuals who are engaged in vocational training—or are interested in pursuing it—will be selected to participate in a filmed journey to interview professionals who have found career success in a wide variety of trades.
"Our aim is to to inspire and motivate the next generation of job-seekers," said Mike Marriner, co-founder of Roadtrip Nation. "Penn Foster is such a great organization to partner with because their mission to reinvigorate the skills economy is so important, and through our combined efforts with Strada Education Network, I am confident we will produce some inspiring content."
The content captured on the trip will be turned into a documentary to air on public television, empowering millions of TV viewers to see the value in developing practical, job-ready skills that can help them achieve their dreams. Content from the Skills Forward Roadtrip will broadcast Fall 2018.
"We're extremely excited to announce our new collaboration with Roadtrip Nation," continued Britt. "Through innovative initiatives like this, we can build a lot of energy in new and exciting ways. The skills economy must evolve, and we're here to help ignite new partnerships and energy to push us forward."
Britt will speak at the ASU+GSV Summit on May 10 during the Growth/Venture Track in the Olympus Room of the Little America Hotel at 3 p.m. MST.
Students, employers and partner organizations rely on Penn Foster to build the skills and knowledge to power the 21st century workforce. For over 125 years, Penn Foster has been dedicated to helping people lead more meaningful and productive lives and to improving social outcomes through education. Penn Foster provides career pathways for opportunity youth and adult learners through diverse and affordable online diploma, certificate and degree programs, offered via its high school, career school and college. With more than 30,000 graduates each year, Penn Foster's online and blended learning programs are delivered in a self-paced, competency-based model wrapped by comprehensive academic, professional and personal support and coaching. For more information, visit https://partners.pennfoster.edu.
About Roadtrip Nation
Roadtrip Nation, renowned for its New York Times best-selling book, award-winning documentary television series and films, and acclaimed project-based learning curriculum, is a career exploration organization that creates resources that empower individuals to pursue fulfilling careers. Combining self-reflection with real-world exposure, Roadtrip Nation's tools enable individuals to connect their interests to compatible life pathways and find meaningful work. For more information, visit roadtripnation.com and roadtripnation.org.
About Strada Education Network
Strada Education Network is a new kind of nonprofit organization that takes a fresh approach to addressing — and solving — critical higher education and workforce challenges. We advance our guiding principle, Completion with a Purpose®, through a unique combination of strategic philanthropy, research and insights, and innovative solutions to improve the college-to-career pathway. Visit us at www.stradaeducation.org and follow us on Twitter @StradaEducation.


Close your eyes when you audition Majik 109 and you could think you are listening to a floor-stander. Majik 109 delivers the most musical room-filling sound ever from a compact loudspeaker.
The perfect introduction to specialist Linn performance, Majik 109 will improve any system, bringing music and movies to life like never before.
Using technology derived from Linn's reference loudspeakers, Majik 109 delivers superior musical performance wherever you position them, whether on a bookshelf, wall or stand.
With timeless, distinctive Linn styling and real-wood finishes, Majik 109s are beautifully crafted by some of the most respected bespoke furniture makers.
A Linn Majik sistem delivers authentic Linn performance, quality and value, and with a choice of traditional separates or integrated products, you can build as compact or expansive a system as you wish.


These Customs Union Technical Regulations is worked out to establish, within the common customs territory of the Customs Union, uniform and mandatory requirements to be applied and observed with regard to electromagnetic compatibility of technical products and ensure free movement of technical products which are put into circulation within the common customs territory of the Customs Union.
If, with regard to technical products, other technical regulations setting forth requirements to technical products are adopted by the Customs Union, then technical products shall comply with the requirements of these technical regulations of the Customs Union where applicable.
These Customs Union Technical Regulations apply to technical products to be put into circulation within the common customs territory of the Customs Union which are capable to generate electromagnetic disturbances and/or whose quality of functioning depends on exposure to external electromagnetic disturbances.
BAKIT is the innovative independent company under German law, which helps to the European companies and to the company around the Globe to obtain all required certification and permissions document for import their goods into territory of Eurasian union and CIS.


Reliability Engineering Center, IIT Kharagpur, Kharagpur-721303 (W.B.), India.
Proposed Probabilistic Load Flow (PLF) approach, based on the method of cumulants, is used for bus voltage security assessment. The work accurately estimates the unknown probability distribution and density function of the involved complex random variables (CRV's). With the help of these distributions a fast and accurate contingency ranking for security assessment is made possible. The advantage of the proposed method is that all possible contingencies are taken into account without complex convolutions and the method gives the densities and distributions of the variables in a single run. The method has been applied to the IEEE RTS 7-Bus test system which consists of 7 buses and 10 lines as well as the IEEE 24 bus system which consists of 24 buses and 38 lines. The information will be useful in operational decision making and ultimately help to improve the reliability of the power system.


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\section{Introduction}
    Concepts and applications of Reinforcement Learning (RL) have seen a tremendous growth over the past decade \citep{atari}. These consist of applications in arcade games \citep{atari}, board games \citep{go} and lately, robot control tasks \citep{ddpg}. A primary reason for this growth is the usage of computationally efficient function approximators such as neural networks \citep{imagenet}. Modern-day RL algorithms make use of parallelization to reduce training times \citep{a3c} and boost agent's performance through effective exploration giving rise to scalable methods \citep{SRL, dreamer, aktr}. However, a number of open problems such as approximation bias, lack of scalability in the case of long time horizons and lack of diverse exploration restrict the application of scalability to complex control tasks.

    State-of-the-art RL algorithms such as Soft Actor-Critic (SAC) \citep{sac} maximize entropy which is indicative of continued exploration. However, using a computationally expensive framework limits scalability as it increases the number of gradient-based \citep{backpropagation} updates of the overall algorithm. Moreover, tasks consisting of long time horizons have higher computational overhead as a result of long trajectory lengths. For instance, obtaining accurate position estimates \cite{SRL} over longer horizons require additional computation times which varies \textit{linearly} with the hardware requirement. Such a variation calls for increased scalability in the RL domain.

    Diverse exploration strategies are essential for the agent to navigate its way in the environment and comprehend intricate aspects of less visited states\cite{mmktd}. Various modern-day RL methods lack significant exploration \citep{a3c,atari} which is addressed by making use of meta-controller\citep{hdqn} and curiosity-driven \citep{rnd} strategies at the cost of sample efficiency and scalability.

    Recent advances in RL have leveraged evolutionary computing for effective exploration and scalability \citep{es,epg,erl,neat,backpropamine}. These methods often fall short of optimal performance and depict sensitivity towards their hyperparameters. A common alternative for improving performance is to combine gradient-based objectives with evolutionary methods \citep{erl}. These algorithms allow a population of learners to gain dominant skills \cite{merl} from modern-day RL methods and depict robust control while demonstrating scalability. However, their applications do not extend to high-dimensional tasks as a result of sensitivity to mutational hyperparameters which still remains an open problem.

    We introduce the Evolution-based Soft Actor Critic (ESAC), an algorithm combining ES with SAC for state-of-the-performance equivalent to SAC and scalability comparable to ES. Our contributions are threefold;
\begin{itemize}
    \item ESAC abstracts exploration from exploitation by exploring policies in \textit
    {weight space} using evolutions and exploiting gradient-based knowledge using the SAC framework.
    \item ESAC makes use of soft winner selection function which, unlike prior selection criteria \citep{erl}, does not shield winners from mutation. ESAC carries out genetic crossovers in hindsight resulting in dominant skill transfer between members of the population.
    \item ESAC introduces the novel Automatic Mutation Tuning (AMT) which maximizes the mutation rate of ES in a small clipped region and provides significant hyperparameter robustness without making use of backpropagation updates.
\end{itemize}

    \section{Related Work}

    \subsection{Scalable Reinforcement Learning}
    Recent advances in RL have been successful in tackling sample-efficiency \cite{sac} and approximation bias (also known as overestimation bias) which stems from value of estimates approximated by the function approximator. Overestimation bias is a common phenomenon occurring in value-based methods \citep{doubleq,doubledqn,maxmin} and can be addressed by making use of multiple critics in \citep{td3} in the actor-critic framework \cite{a3c}. This in turn limits scalability of algorithms \cite{SRL} by increasing the number of gradient-based updates. Moreover, memory complexity of efficient RL methods increases linearly with the expressive power of approximators \cite{mem}, which in turn hinders scalability of RL to complex control tasks.

    \subsection{Evolutionary Reinforcement Learning}
    Intersection of RL and Evolutionary methods has for long been studied in literature \citep{epg,neat,backpropamine,symbiotic,contopt,cem}. \citep{es} presents the large-scale parallelizable nature of Evolution Strategies (ES). Performance of ES on continuous robot control tasks in comparison to various gradient-based frameworks such as Trust Region Policy Optimization (TRPO) \citep{trpo} and Proximal Policy Optimization (PPO) \citep{ppo} have been found comparable. On the other hand, ES falls short of competitive performance resulting in local convergence and is extremely sensitive to mutation hyperparameters.

    An alternative to a pure evolution-based approach is a suitable combination of an evolutionary algorithm with a gradient-based method \citep{cma}, commonly referred to as Evolutionary Reinforcement Learning (ERL) \citep{erl}. ERL makes use of selective mutations and genetic crossovers which allow weak learners of the population to inherit skills from strong learners while exploring. ERL methods are scalable to high-dimensional control problems including multi-agent settings \cite{merl}. Such an approach is a suitable trade-off between sample efficiency and scalability but does not necessarily introduce mutation robustness. Other methods in literature \citep{epg} follow a similar approach but are often limited to directional control tasks which require little mutation. Thus, addressing scalability and exploration while preserving higher returns and mutation robustness requires attention from a critical standpoint. Our work is parallel to prior efforts made towards this direction.

\section{Background}

    \subsection{Reinforcement Learning and Soft Actor-Critic}
    We review the RL setup wherein an agent interacts with the environment in order to transition to new states and observe rewards by following a sequence of actions. The problem is modeled as a finite-horizon Markov Decision Process(MDP) \citep{rl} defined by the tuple $(\mathcal{S},\mathcal{A},r,P,\gamma)$ where the state space is denoted by $\mathcal{S}$ and action space by $\mathcal{A}$, $r$ presents the reward observed by agent such that $r: \mathcal{S} \times \mathcal{A} \rightarrow [r_{min},r_{max}]$, $P: \mathcal{S} \times \mathcal{S} \times \mathcal{A} \rightarrow [0,\infty)$ presents the unknown transition model consisting of the transition probability to the next state $s_{t+1} \in \mathcal{S}$ given the current state $s_{t} \in \mathcal{S}$ and action $a_{t} \in \mathcal{A}$ at time step $t$ and $\gamma$ is the discount factor. We consider a policy $\pi_{\theta}({a_{t}|s_{t}})$ as a function of model parameters $\theta$. Standard RL defines the agent's objective to maximize the expected discounted reward $\mathbb{E}_{\pi_{\theta}}[\sum_{t=0}^{T}\gamma^{t}r(s_{t},a_{t})]$ as a function of the parameters $\theta$.
    SAC \citep{sac} defines an entropy-based\citep{entropy} objective expressed as in \autoref{eq:sac}.
    \begin{gather}
    J(\pi_{\theta}) = \sum_{t=0}^{T}\gamma^{t}[r(s_{t},a_{t}) + \lambda \mathcal{H}(\pi_{\theta}(\cdot|s_{t}))] \label{eq:sac}
    \end{gather}

    wherein $\lambda$ is the temperature coefficient and $\mathcal{H}(\pi_{\theta}(\cdot|s_{t}))$ is the entropy exhibited by the policy $\pi(\cdot|s_{t})$ in $s_{t}$. For a fixed policy, the soft Q-value function can be computed iteratively, starting from any function $Q:\mathcal{S} \times \mathcal{A}$ and repeatedly applying a modified Bellman backup operator $\mathcal{T}^{\pi}$ given by \autoref{eq:bell}
    \begin{gather}
    \mathcal{T}^{\pi}Q(s_{t},a_{t}) = r(s_{t},a_{t}) + \gamma\mathbb{E}_{s_{t+1} \sim P}[V(s_{t+1})] \label{eq:bell}
    \end{gather}

    where $V(s_{t})$ is the soft state value function expressed in \autoref{eq:state-val}.
    \begin{gather}
    V(s_{t}) = \mathbb{E}_{a_{t} \sim \pi}[Q(s_{t},a_{t}) - \log (\pi (a_{t}|s_{t}))] \label{eq:state-val}
    \end{gather}

    We consider a parameterized state value function $V_{\psi}(s_{t})$, a soft Q-function $Q_{\phi}(s_{t},a_{t})$ and a policy $\pi_{\theta}(a_{t}|s_{t})$ which can be represented with nonlinear function approximators such as neural networks with $\psi, \phi$ and $\theta$ being the parameters of these networks.

    \subsection{Evolution Strategies}
    We review the Evolution Strategies \citep{es} framework which is motivated by natural evolution. ES is a heuristic search procedure in which a population of offsprings is mutated using random perturbations. Upon mutation, the fitness objective corresponding to each member of the population is evaluated and offsprings with greater scores are recombined to form the population for the next generation. Let $n$ be the number of offsprings in the population. The parameter vectors of the model can then be represented as $\theta_{es,(i)}$ such that $i=1,2,..n$. A total of $n$ random perturbations $\epsilon_{(i)}$, $i=1,2,..n$ are sampled from a Gaussian distribution $\mathcal{N}(0,1)$ in order to mutate $\theta_{es,(i)}$ and evaluate the fitness objective $\mathbb{E}_{\epsilon_{(i)} \sim \mathcal{N}(0,1)}[O(\theta_{es,(i)} + \sigma \epsilon_{(i)})] = \frac{1}{\sigma}\mathbb{E}_{\epsilon_{(i)} \sim \mathcal{N}(0,1)}[O(\theta_{es,(i)} + \sigma \epsilon_{(i)})\epsilon_{(i)}]$. Here, $\sigma$ is the mutation rate which controls the extent of mutation. In the case of RL, the fitness objecive $O(\theta_{es,(i)})$ is the episodic reward observed by members of the population.

    \section{A Motivating Example: The Discrete Cyclic MDP}

    \begin{figure}[H]
        \centering
        \includegraphics[width=4cm]{mdp.PNG}
        \caption{The long-horizon discrete Cyclic MDP. The agent observes a reward of +1 for moving clockwise and -1 otherwise. }
        \label{fig:mdp}
    \end{figure}

    We consider a long-horizon discrete cyclic MDP as our motivation for the work. The MDP has a state space $\mathcal{S}^{3}$ consisting of 3 states- $S0, S1$ and $S2$ and a discrete action space $\mathcal{A}^{3}$ consisting of 3 actions- clockwise, anticlockwise and stay. The agent starts in state $S0$. The reward function $r: \mathcal{S}^{3} \times \mathcal{A}^{3}$ assigns a reward of $+1$ for moving clockwise and $-1$ otherwise. Each episode lasts $2000$ timesteps and terminates if the agent reaches the end of horizon or incurs a negative reward. \autoref{fig:mdp} presents the long-horizon discrete cyclic MDP.

    The cyclic MDP, being a long-horizon problem, serves as a suitable benchmark for agent's behavior consisting of minimum computational overhead and is a small-scale replication of policy-search for scalable policy-based agents. The environment consists of a global objective which the agents can achieve if they solve the environment by obtaining the maximum reward of $+2000$. In order to assess evolution-based behavior, we compare the performance of a population of 50 offsprings utilizing ES with PPO \citep{ppo} and Deep Deterministic Policy Gradient (DDPG) \citep{ddpg}, an efficient off-policy RL method. Although DDPG is primarily a continuous control algorithm, we employ it as a result of the minimal nature of the problem. \autoref{fig:mdp_results} (left) presents the performance of ES in comparison to gradient-based agents in the cyclic MDP averaged over 3 runs. The ES population presents sample efficiency by solving the task within the first 100 episodes. DDPG, on the other hand, starts solving the task much latter during training. The use of a deterministic policy allows DDPG to continuously move left whereas in the case of ES, the population carries out exploration in the weight space and moves along the direction of the strong learners. Lastly, PPO finds a local solution and does not converge towards solving the task. Driven by clipped updates, PPO restricts the search horizon in policy space leading to a sub-optimal policy.

    ES has proven to be scalable to large-scale and high dimensional control tasks \citep{es}. We assess this property of ES in the cyclic MDP by varying the operational hardware (number of CPUs) \citep{seed} and algorithm overhead (population size). We measure the average wall-clock time per episode \cite{nas}. As shown in \autoref{fig:mdp_results} (center), ES is parallelizable in nature and can be scaled up to larger population sizes by reducing the computation time. The large-scale readily parallelizable nature of ES is a convincing characteristic for utilizing CPU-based hardware. However, ES relies on excessively sensitive hyperparameters such as mutation rate. \autoref{fig:mdp_results} (right) presents the sensitivity of ES to mutation rate within a small range with a constant population size of 50. Varying population size does not present a trend in sensitivity indicating that mutation rate is the dominant hyperparameter governing policy behavior among offsprings. Hyperparameter sensitivity requires attention in the case of RL applications such as for real-world continuous control \citep{robot}. These include excessive tuning of parameters and detailed ablation studies. The cyclic MDP highlights this sensitive nature of ES and serves as a motivating example for tackling sensitivity while preserving optimal performance in a scalable manner.

\begin{figure*}[ht]
    \centering
    \includegraphics[width=13cm,height=4cm]{mdp_results.png}
    \caption{Comparison of ES, DDPG and PPO in the discrete cyclic MDP. ES depicts sample-efficient behavior (left) due to the presence of strong-learners in the population. Scalable nature of ES (center) with readily available computational resources allows in reduction of average episode execution time. However, ES is sensitive to hyperparameters (right) which results inconsistency across different seeds and rigorous fine-tuning.}
    \label{fig:mdp_results}
\end{figure*}

\section{Evolution-based Continuous Control}

The motivation behind ESAC stems from translating the scalability and tackling the mutation sensitivity of ES observed in discrete cyclic MDP to continuous control tasks. ESAC combines the scalable nature of ES with the limited approximation bias of SAC to yield a CPU-friendly state-of-the-art equivalent algorithm.

\subsection{Evolution-based Soft Actor-Critic}
    \begin{figure}[H]
        \centering
        \includegraphics[width=6cm]{schematic.PNG}
        \caption{Workflow of ESAC combining ES with SAC. ESAC makes use of soft winner selections, hindsight crossovers and AMT for scalable performance.}
        \label{fig:schematic}
    \end{figure}
\subsubsection{Overview}
\autoref{fig:schematic} provides a high-level schematic of the ESAC algorithm and its components. The population is evaluated in the environment with the fitness metric as episodic rewards obtained by each offspring. Top $w$ winners are then segregated for mutation consisting of ES update followed by crossovers between perturbed offsprings and winners. The new population is formed using crossed-over offsprings and SAC agent. The SAC agent executes its own episodes at fixed timesteps and stores these experiences in a dedicated replay-buffer following policy update. During the SAC update timesteps, ESAC utilizes AMT which maximizes the mutation rate in a clipped region. SAC update timesteps are exponentially annealed to reduce entropy noise and abstract exploration in weight space.

\subsubsection{Algorithm}
Algorithm \ref{alg:algorithm1} presents the ESAC algorithm. We begin by initializing $\psi$, $\theta$, $\theta_{es}$, $\phi$ being the parameters of state-value function, SAC policy, ES policy and Q-function respectively. We then initialize learning rate for SAC agent $\alpha$, learning rate of ES population $\alpha_{es}$, mutation rate $\sigma$, $p_{sac}$ which is the probability of SAC updates, $\bar{\psi}$ is the parameter vector of the target value function, $\zeta$ is the clip parameter, $\tau$ is the target smoothing coefficient, $e$ is the fraction of winners and $g$ is the gradient interval. A population of $n$ actors $pop_{n}$ is initialized along with an empty replay buffer $R$. Following the main loop, for each offspring $i$ in the population, we draw a noise vector $\epsilon_{i}$ from $\mathcal{N}(0,1)$ and perturb the ES policy vector $\theta_{es}$ to yield the perturbed parameter vector $\theta_{es,(i)}$ as per the expression $\theta_{es,(i)} + \sigma \epsilon_{(i)}$. $\theta_{es,(i)}$ is then evaluated to yield the fitness $F_{(i)}$ as episodic rewards. These are collected in a normalized and ranked set $F$. We now execute soft winner selection wherein the first $w= (n*e)$ offsprings from $F$ are selected for crossovers by forming the set $W$. The soft winner selection allows dominant skill transfer between winners and next generation offsprings. Mutation is carried out using the ES update \citep{es}. SAC gradient updates are executed at selective gradient intervals $g$ during the training process. $p_{sac}$ is exponentially annealed to reduce entropy noise and direct exploration in the weight space. During each $g$, the agent executes its own episodes by sampling $a_{t} \sim \pi_{\theta}(a_{t}|s_{t})$, observing $r(s_{t}|a_{t})$ and $s_{t+1}$ and storing these experiences in $R$ as a tuple $(s_{t},a_{t},r(s_{t},a_{t}),s_{t+1})$. Following the collection of experiences, we update the parameter vectors $\psi$, $\phi$ and $\theta$ by computing $\nabla_{\psi}J_{V}(\psi)$,  $\nabla_{\phi}J_{Q}(\phi_{(i)})$ and $\nabla_{\theta}J_{\pi}(\theta)$ where $J_{V}(\psi)$, $J_{Q}(\phi_{(i)})$ and $J_{\pi}(\theta)$ are the objectives of the state-value function, each of the two Q-functions $i \in \{ 1,2\}$ and policy as presented in \cite{sac} respectively. Gradient updates are followed by AMT update(\autoref{AMT}) which leads to hindsight crossovers between winners in $W$ and ES policy parameter vector $\theta_{es}$. Crossovers are carried out as random replacements between elements of weight vectors. In the case of hindsight crossovers, replacements between weight vector elements of current \& immediate previous generations is carried out. This allows the generation to preserve traits of dominant offsprings in hindsight. Finally, the new population is formed using $\theta,\theta_{es}$ and $W$.
\begin{algorithm}[!h]
\caption{Evolution-based Soft Actor-Critic (ESAC)}
\label{alg:algorithm1}
\begin{algorithmic}[1]

  \State Initialize parameter vectors $\psi,\bar{\psi},\theta, \theta_{es},\phi$
  \State Initialize $\alpha$, $\alpha_{es}$, $\sigma$, $\zeta$, $\tau$, $e$, $g$, $p_{sac}$
  \State Initialize a population of $n$ actors $pop_{n}$ and an empty replay buffer $R$

  \For{generation=1,$\infty$}
    \For{$i \in pop_{n}$}
        \State sample $\epsilon_{(i)} \sim \mathcal{N}(0,1)$
        \State $F_{(i)} \leftarrow$ evaluate $(\theta_{es,(i)} + \sigma \epsilon_{(i)})$ in the environment
    \EndFor
    \State normalize and rank $F_{(i)} \in F$
    \State select the first $w = (n*e)$ offsprings from $F$ to form the set of winners $W$
    \State set $\theta_{es} \leftarrow \theta_{es} + \frac{\alpha_{es}}{n\sigma} \sum_{i=1}^{n} F_{(i)}\epsilon_{(i)}$

    \If{$generation$ mod $g$ == 0 \& $\mu \sim \mathcal{N}(0,1) < p_{sac}$}
        \For{each environment step}
            \State $a_{t} \sim \pi_{\theta}(a_{t}|s_{t})$
            \State observe $r(s_{t}|a_{t})$ and $s_{t+1} \sim P$
            \State $R \leftarrow R \cup {(s_{t},a_{t},r(s_{t},a_{t}),s_{t+1})}$
        \EndFor
        \For{each gradient step}
            \State $\psi \leftarrow \psi  - \alpha \nabla_{\psi}J_{V}(\psi)$
            \State $\phi \leftarrow \phi  - \alpha \nabla_{\phi}J_{Q}(\phi_{(i)})$ for $i \in \{1,2\}$
            \State $\theta \leftarrow \theta  - \alpha \nabla_{\theta}J_{\pi}(\theta)$
            \State $\bar{\psi} \leftarrow \tau \psi + (1 - \tau)\psi$
        \EndFor
        \State Update $\sigma$ using \autoref{eq:3}
    \EndIf

    \State crossover between $\theta_{es,(i)}$ and $\theta_{es}$ for $i=1,2,..w$
    \State Form new population $pop_{n}$ using $\theta, \theta_{es},W$
  \EndFor

\end{algorithmic}
\end{algorithm}

\subsection{Automatic Mutation Tuning (AMT)}
\label{AMT}
Maximization of randomness in the policy space is akin to maximization in the weight space as both formulations are a multi-step replica of generalized policy improvement algorithm. This allows one to leverage the more suitable weight space for parallel computations. Policy updates during execution of offsprings require tuning the exploration scheme. To this end, we automatically tune $\sigma$ with the intial value $\sigma_{(1)}$. $\sigma$ is updated at fixed timesteps in a gradient-ascent manner without making use of backpropagation updates. AMT motivates guided exploration towards the objective as a result of the expansion of the search horizon of population which in turn enables the agent to maximize rewards \textit{as randomly as possible}. AMT makes use of the SmoothL1 (Huber) \citep{huber} loss function provided in \autoref{eq:1} and the update rule is mathematically expressed in \autoref{eq:2}.
\begin{gather}
    SmoothL1(x_{i},y_{i}) =
    \begin{cases}
    0.5(x_{i} - y_{i})^{2}, \text{if}\ |x_{i} - y_{i}| < 1 \\
    |x_{i} - y_{i}| -0.5, \text{otherwise}
    \end{cases} \label{eq:1}
\end{gather}
\begin{gather}
    \sigma_{(t+1)} \xleftarrow[]{} \sigma_{(t)} + \frac{\alpha_{es}}{n\sigma_{(t)}}SmoothL1(R_{max},R_{avg})  \label{eq:2}
\end{gather}
Here, $R_{max}$ is the reward observed by winner offspring, $R_{avg}$ is the mean reward of the population with $\sigma_{(t)}$ and $\sigma_{(t+1)}$ the mutation rates at timesteps $t$ and $t+1$ respectively. While exploring in weight space, the SmoothL1 loss tends to take up large values. This is indicative of the fact that the deviation between the winner and other learners of the population is significantly high. In order to reduce excessive noise from weight perturbations $\epsilon_{i}$, we clip the update in a small region parameterized by the new clip parameter $\zeta$. Suitable values for $\zeta$ range between $10^{-6}$ to $10^{-2}$. The clipped update is mathematically expressed in \autoref{eq:3}.
\begin{gather}
    \sigma_{(t+1)} \xleftarrow[]{} \sigma_{(t)} + clip(\frac{\alpha_{es}}{n\sigma_{(t)}}SmoothL1(R_{max},R_{avg}),0, \zeta)  \label{eq:3}
\end{gather}
The update can be expanded recursively and written in terms of the initial mutation rate $\sigma_{(1)}$ as expressed in \autoref{eq:4} (derived in \autoref{sc:derivation}).
\begin{gather}
    \theta_{(t+1)} \xleftarrow[]{} \theta_{(t)} + \frac{\alpha_{es}}{n\sigma_{(1)}\hat{\Lambda}} SmoothL1(R_{max,(t)},R_{avg,(t)}) \label{eq:4}
\end{gather}
Here, $\hat{\Lambda}$ is defined as the Tuning Multiplier and can be mathematically expressed as in \autoref{eq:tuning}. We direct the curious reader to \autoref{sc:derivation} for a full derivation.
\begin{multline}
    \hat{\Lambda} = \prod_{t^{'}=1}^{t-1} (1 + \frac{\alpha_{es}}{n \sigma_{(t^{'})}^{2}} SmoothL1(R_{max,(t^{'})},R_{avg,(t^{'})})) \label{eq:tuning}

\end{multline}

It can be additionally shown that AMT, when combined with soft winner selection, leads to policy improvement with high probability among the set of winners. We defer this proof to \autoref{sc:policy}.

\section{Experiments}

\begin{table*}[ht]
\resizebox{\textwidth}{!}{
\centering
 \begin{tabular}{c c c c c c c}
 \hline
 Domain & Tasks & ESAC & SAC & TD3 & PPO & ES \\ [0.5ex]
 \hline
 {} & HalfCheetah-v2 & 10277.16$\pm$403.63 & \textbf{10985.90$\pm$319.56} & 7887.32$\pm$532.60 & 1148.54$\pm$1455.64 & 3721.85$\pm$371.36\\
 {} & Humanoid-v2 & 5426.82$\pm$229.24 & \textbf{5888.55$\pm$44.66} & 5392.89$\pm$363.11 & 455.09$\pm$213.88 & 751.65$\pm$95.64\\
 {} & Ant-v2 & 3465.57$\pm$337.81 & 3693.08$\pm$708.56 & \textbf{3951.76$\pm$370.00} & 822.34$\pm$15.76 & 1197.69$\pm$132.01\\
 {} & Walker2d-v2 & \textbf{3862.82$\pm$49.80} & 3642.27$\pm$512.59 & 3714.89$\pm$90.35 & 402.33$\pm$27.38 & 1275.93$\pm$243.78\\
 MuJoCo & Swimmer-v2 & \textbf{345.44$\pm$17.89} & 31.68$\pm$0.41 & 110.85$\pm$23.02 & 116.96$\pm$0.74 & 254.42$\pm$109.91\\
 {} & Hopper-v2 & \textbf{3461.63$\pm$118.61} & 3048.69$\pm$467.21 & 3255.27$\pm$184.18 & 1296.17$\pm$1011.95 & 1205.73$\pm$185.25\\
 {} & LunarLanderContinuous-v2 & \textbf{285.79$\pm$9.60} & 66.52$\pm$26.75 & 273.75$\pm$4.51 & 124.47$\pm$11.58 & 74.41$\pm$109.69\\
 {} & Reacher-v2 & -2.01$\pm$0.07 & -0.50$\pm$0.05 & -5.12$\pm$0.17 & \textbf{-0.21$\pm$0.07} & -4.43$\pm$2.06\\
 {} & InvertedDoublePendulum-v2 & \textbf{9359.35$\pm$0.60} & 9257.96$\pm$86.54 & 5603.72$\pm$3213.51 & 88.52$\pm$4.73 & 259.39$\pm$36.75\\
 \hline
 {} & HumanoidStand & \textbf{805.08$\pm$135.67} & 759.08$\pm$125.67 & 745.15$\pm$291.377 & 8.41$\pm$3.33 & 10.57$\pm$0.30\\
 {} & HumanoidWalk & \textbf{883.00$\pm$21.97} & 843.00$\pm$7.97 & 686.33$\pm$56.23 & 2.20$\pm$0.18 & 10.59$\pm$0.34\\
 {} & HumanoidRun & \textbf{358.82$\pm$101.12} & 341.45$\pm$18.14 & 291.82$\pm$2101.12 & 2.29$\pm$0.16 & 10.55$\pm$0.30\\
 {DeepMind Control Suite} & CheetahRun & \textbf{773.14$\pm$3.00} & 227.66$\pm$13.07 & 765.22$\pm$27.93 & 371.70$\pm$19.82 & 368.62$\pm$32.87\\
 {} & WalkerWalk & \textbf{971.02$\pm$2.87} & 175.75$\pm$15.51 & 941.45$\pm$27.01 & 316.54$\pm$79.54 & 308.94$\pm$44.36\\
 {} & FishUpright & 914.96$\pm$2.04 & 285.69$\pm$21.03 & 838.32$\pm$34.86 & 561.39$\pm$111.59 & \textbf{997.58$\pm$0.26}\\
 \hline
 \end{tabular}}
 \caption{Average returns on 15 locomotion tasks from MuJoCo \& DeepMind Control Suite. Results are averaged over 5 random seeds with the best performance highlighted in bold. ESAC demonstrates improved performance on 10 out of 15 tasks. Furthermore, ESAC presents consistency across different seeds in the case of large action spaces and sparse rewards indicating the suitability of evolutionary methods to RL and control tasks.}
\label{tab:table}
\end{table*}

\begin{figure*}[ht]
    \centering
    \includegraphics[width=2cm,height=2cm]{1.png}    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{2.png}
    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{3.png}
    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{4.png}
    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{5.png}
    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{6.png}
    \hspace{0.1cm}
    \includegraphics[width=2cm,height=2cm]{7.png}
    \caption{Robust behavior of ESAC observed on the WalkerWalk task. The ESAC policy prevents the walking robot from falling down when the robot loses its balance while walking. The robot successfully retains its initial posture within 10 timesteps. ESAC exhibits robust policies on complex tasks as a result of successive evolutions and hindsight genetic crossovers between winners and actors of the population.}
    \label{fig:robust}
\end{figure*}

Our experiments aim to evaluate performance, sample efficiency, scalability and mutation sensitivity of ESAC. Specifically, we aim to answer the following questions-
\begin{itemize}
\item How does the algorithm compare to modern-day RL methods for complex tasks?
\item How do evolutionary operations impact scalability in the presence of gradients?
\item Which components of the method contribute to sensitivity and scalability?
\end{itemize}

\subsection{Performance}
We assess performance and sample efficiency of ESAC with state-of-the-art RL techniques including on-policy and off-policy algorithms. We compare our method to ES \citep{es}; SAC \citep{sac}; Twin-Delayed Deep Deterministic Policy Gradient (TD3)\citep{td3} and PPO \citep{ppo} on a total of 9 MuJoCo \citep{mujoco} and 6 DeepMind Control Suite \citep{dm} tasks. We refer the reader to \autoref{sc:dm_results} for complete results. The tasks considered consist of sparse rewards and high-dimensional action spaces including 4 different versions of Humanoid. Additionally, we consider the LunarLander continuous task as a result of its narrow basin of learning. All methods were implemented using author-provided implementations except for ES in which Virtual Batch Normalization \cite{vbnorm} was omitted as it did not provide significant performance boosts and hindered scalability.

Agents were trained in OpenAI's Gym environments \citep{gym} framework for a total of 5 random seeds. Training steps were interleaved with validation over 10 episodes. For all agents we use nonlinear function approximators as neural networks in the form of a multilayer architecture consisting of 2 hidden layers of 512 hidden units each activated with ReLU \citep{relu} nonlinearity and an output layer with tanh activation. We use this architecture as a result of its consistency in baseline implementations. We use Adam \citep{adam} as the optimizer (refer to \autoref{sc:hyperparameters} for hyperparameters). For ESAC and SAC, we use a Diagonal Gaussian (DG) policy \cite{curl} without automatic entropy tuning for a fair comparison. Training of gradient-based methods was conducted on 4 NVIDIA RTX2070 GPUs whereas for ES and ESAC, a total of 64 AMD Ryzen 2990WX CPUs were used.

\autoref{tab:table} presents total average returns of agents on all 15 tasks considered for our experiments. ESAC demonstrates improved returns on 10 out of 15 tasks. ESAC makes use of evolution-based weight-space exploration to converge to robust policies in tasks where SAC often learns a sub-optimal policy. Moreover, utilization of evolutionary operations demonstrates consistency across different seeds for high-dimensional Humanoid tasks indicating large-scale suitability of the method to complex control.

\subsection{Behaviors}
Combination of RL and evolutionary methods provides suitable performance on control benchmarks. It is essential to assess the behaviors learned by agents as a result of weight-space exploration. We turn our attention to observe meaningful patterns in agent's behavior during its execution in the environment. More specfically, we aim to evaluate the robustness of ESAC scheme which promises efficacious policy as a result of effective exploration. We initialize a learned ESAC policy on the WalkerWalk task and place it in a challenging starting position. The Walker agent stands at an angle and must prevent a fall in order to complete the task of walking suitably as per the learned policy. \autoref{fig:robust} demonstrates the behavior of the Walker agent during its first 100 steps of initialization. The agent, on its brink of experiencing a fall, is able to gain back its balance and retain the correct posture for completing the walking task. More importantly, the agent carries out this manoeuvre within 50 timesteps and quickly gets back on its feet to start walking. \autoref{fig:robust} is an apt demonstration of robust policies learned by the ESAC agent. Dominant skill transfer arising from hindsight crossovers between winners and offsprings provisions effective exploration in weight space.

\subsection{Scalability}

\begin{figure*}[ht]
    \centering
    \includegraphics[height=10cm,width=13.5cm]{scale.png}
    \caption{Variation of average time per episode (in seconds) with the number of operational CPUs and population size (in legend) for locomotion tasks from the MuJoCo benchmark. ESAC demonstrates equivalent scalability as ES by providing an approximate $60\%$ reduction in episode execution time. Reduction in computational time is found to be approximately quadratic which is computationally efficient for RL in comparison to linear variations for high-end GPU machines.}
    \label{fig:all_scale}
\end{figure*}

We assess scalability of our method with ES on the basis of hardware resources and algorithm overhead. We vary the number of CPUs by keeping other training parameters constant. Parallelization on multiple CPU-based resources is readily available and cost-efficient in comparison to a single efficient GPU resource. We also vary number of offsprings in the population by fixing CPU resources. Out of mutation rate $\sigma$ and population size $n$, $n$ governs the computational complexity of ES with $\sigma$ being a scalar value. Thus, assessing variation w.r.t $n$ provides a better understanding of resource utility and duration. For both experiments, we train the population for $10^{6}$ steps and average the wall-clock time per episode. Another effective way to demonstrate scalability is by monitoring overall time taken to complete the training tasks \citep{es}. However, this often tends to vary as initial learning periods have smaller episode lengths which does not compensate for fixed horizons of $1000$ steps in MuJoCo.

\autoref{fig:all_scale} presents the scalable nature of ESAC equivalent to ES on the MuJoCo and LunarLanderContinuous tasks. Average wall-clock time per episode is reduced utilizing CPU resources which is found to be favourable for evolution-based methods. Moreover, the variation depicts consistency with the increasing number of members in the population indicating large-scale utility of the proposed method. A notable finding here is that although ESAC incorporates gradient-based backpropagation updates, it is able to preserve its scalable nature by making use of evolutions as dominant operations during the learning process. This is in direct contradiction to prior methods \cite{erl}which demonstrate reduced sample-efficiency and the need for significant tuning when combining RL with scalable evolutionary methods. Reduction in the number of SAC updates by exponentially annealing the gradient interval allows ESAC to reduce computation times and simultaneously explore using AMT.

\subsection{Ablation Study}

\begin{figure}[H]
    \centering
    \includegraphics[height=2.5cm,width=3.25cm]{clip.png}
    \label{fig:clip}
    \includegraphics[height=2.75cm,width=4cm]{update.png}
    \caption{Left: Mutation sensitivity of ESAC, Right: Number of backprop updates in ESAC compared to SAC}
    \label{fig:updates}
\end{figure}

\subsubsection{Mutation Sensitivity}
 ES presents sensitivity to $\sigma$ which is addressed by making use of AMT in ESAC. The AMT update gradually increases mutation rate $\sigma$ using clip parameter $\zeta$ as learning progresses. \autoref{fig:clip} presents the variation of average normalized rewards with different values of the new hyperparameter $\zeta$ for HalfCheetah-v2 and Ant-v2 tasks. Each experiment was run for 1 million steps. The population presents robustness and performance improvement for small values with the optimal range being $10^{-4}$ to $10^{-2}$. On the other hand, sensitivity is observed in the $10^{-1}$ to $1$ region which accounts for larger updates with high variance. Hyperparameter variation is limited to a smaller region, in contrast to a wider spread of $\sigma$ in the ES update. Offsprings remain robust to significantly large values of $\zeta$ due to early convergence of the population at the cost of poor performance among weak learners of the population. However, this is addressed by making use of hindsight crossovers which allow simultaneous transfer of dominant traits.

\subsubsection{Number of Updates}
The main computation bottleneck in SAC arises from the number of backprop updates. This is tackled by exponentially annealing these updates and increasing winner-based evolutions and crossovers for transferring skills between SAC agent and ES offspings. \autoref{fig:updates} presents a comparison between the number of backprop updates carried out using SAC and ESAC during the training phase of HalfCheetah-v2 and Ant-v2 tasks. Results for the updates are averaged over 3 random seeds. ESAC executes lesser number of updates highlighting its computationally efficient nature and low dependency on a gradient-based scheme for monotonic improvement. Complete results can be found in \autoref{sc:scale_results}.

\section{Conclusions}
In this paper, we introduce ESAC which combines the scalable nature of ES with low approximation bias and state-of-the-art performance of SAC. ESAC addresses the problem of mutation-sensitive evolutions by introducing AMT which maximizes the mutation rate of evolutions in a small clipped region as the SAC updates are exponentially decayed. ESAC demonstrates improved performance on 10 out of 15 MuJoCo and DeepMind Control Suite tasks including different versions of Humanoid. Additionally, ESAC presents scalability comparable to ES, reducing the average wall-clock time per episode by approximately $60\%$, hence depicting its suitability for large-scale RL tasks involving continuous control. On the other hand, high variance of the SAC agent under sparse rewards requiring consistent optimal behavior hurts the performance of ESAC. This can be addressed by combining the framework with a meta-controller or using a more sophisticated architecture such as a master-slave framework \citep{ms}. We leave this for future work.

redpajama_set_name: RedPajamaArXiv


text:
Second Judicial District Court/New Mexico Department of Correction's Native American Healing to Wellness Court
Summary: The Native American Healing to Wellness Court is a specialized track within The New Mexico Department of Corrections and TheSecond Judicial District Court's Drug Court Program. The track, offered to self-identified Native American offenders, provides an alternative to incarceration and an opportunity to receive culturally specific holistic treatment. It uses a holistic approach to provide traditional healing and treatment to Native American offenders in the Bernalillo County area. The Healing to Wellness Court's innovative approach is only the second of its kind in the Nation. It utilizes the existing capacity of the District Court and The New Mexico Department of Correction's Probation/Parole, as well as the robust social service environment in Albuquerque. Many of the services offered to the Healing to Wellness participants are provided through the track's community non-profit partner, First Nations.
Self-identified Native Americans from any tribein the State of New Mexico.
March 18, 2018 – present
Forrest Beard
Valerie Herrera
Second Judicial District Court Bernalillo County
400 Lomas Blvd. NW
The court is located within the urban setting of Albuquerque, New Mexico. The city itself is small to medium sized and is surrounded by desert. The Sandia Mountains sit to the east of the city and to the north, west, and south there are open lands with mesas. Many of the pueblo tribes, including Acoma, Laguna,Sandia and Isleta, are located within a 1.5-hour drive of Albuquerque.
The City of Albuquerque boasts a diverse population and is the largest city in the state. There is a heavy Hispanic and Native American influence, as well as a significant Caucasian population. As of 2016, there were just over 559,000 Albuquerque residents, 4.2% of whom identified as Native American.At the start of 2018 there were just over 662,000 Bernalillo County residents, 4% of whom identified as Native American.
The Second Judicial District Court Healing to Wellness (HTW) track was created to address the lack of specialty services available for urban Native Americans in New Mexico who are convicted of a crime related to or influenced by alcohol and/or drugs. It provides a culturally sensitive court system designed to provide holistic services to offenders at the district court level. Additionally, it is one of the only HTW programs in the nation that also serves felony offenders.The HTW track is intended to serve the Native Americanadult population residing within Bernalillo County who are convicted of a crime relating to or influenced by the use of and/or addiction to drugs or alcohol. There is no mandate to be an enrolled member of a federally recognized tribe. The track serves any self-identified Native American as long as their crime is directly related to a substance-abuse issue.The program began running in March of 2018. It was inspired by Judge Briana Zamora, the court's previous presiding judge, and her recognition of the need for culturally sensitive court services. Additionally, a similar track created by the Bernalillo County Metropolitan Court has been running for a number of years and is showing very positive results. The Bernalillo County Metropolitan Court track offers both inspiration and guidance for the creation of the HTW track in the Second Judicial District Court.The goal of the HTW track is to provide comprehensive and culturally sensitive services to convicted urban Native Americans who need treatment for substance abuse disorders and/or co-occurring disorders. It aims to strengthen and revive the spirit, reduce recidivism, and improve community safety.The HTW track uses a holistic defense approach to meet the medical, cultural, and treatment needs of its clients. It partners with a local social service agency, First Nations Community Healthsource, to provide a variety of services ranging from counseling and drug treatment to traditional sweat lodges and talking circles.
The 48-week program is run as an alternative to District Court's Drug Court and facilitated by the Second Judicial District Court and The New Mexico Department of Corrections. It is split into 4 phases:
PHASE 1 (12 weeks):Report to Probation Officer (PO) once a week, or as directed. Meet with the HTW Judge bi-weekly. Attend required treatment. Attend a minimum of three community support meetings each week, or other support groups. Attend one sponsor meeting every week or as directed. Complete a minimum of 8 hours of community service per week if unemployed and 4 hours per week if employed or attending school full-time.
PHASE 2 (12 weeks): Report to PO once a week or as directed. Meet with the HTW Judge once a month. Attend required treatment. Attend a minimum of two community support meetings per week or other approved support group meetings. Attend a minimum of one sponsor meeting per week or as directed. Maintain full-time employment/school, 30 hours of community service or a combination of both.
PHASE 3 (12 weeks): Report to PO once every two weeks or as directed. Meet with the HTW Judge once a month. Attend required treatment. Attend a minimum of one community support meeting per week. Attend a minimum of one sponsor meeting per week or as directed. Maintain full-time employment/school, 30 hours of community service or a combination of both.
PHASE 4 (12 weeks):Report to your PO once a month or as directed. Meet with the HTW Judge once a month. Attend required treatment. Attend at least one community support meeting each week or as directed. Attend a minimum of one sponsor meeting each week or as directed. Maintain full-time employment/school, 30 hours of community service or a combination of both.The HTW track is part of The New Mexico Department of Corrections and The Second Judicial District Drug Court and offered as an alternative track. Presiding Judge Jaramillo oversees the entire track including the programs Standard Drug Court and the Medication Assisted Treatment track. He works closely with the Second Judicial District's Judicial Supervision and Diversion Programs Director, Kelly Bradford, and the court's Project Manager, Valerie Herrera. Program Coordinator Forrest Beard has also been instrumental in integrating the track in the current Drug Court. Together, the team is responsible for the oversight of the track and the in-house coordination between different sectors of the Second Judicial District Court and The New Mexico Corrections Department.
The HTW track refers participants to First Nations. Lorenzo Jim, the Program Manager of the Traditional Wellness and Integrative Care Program, is responsible for the intake and subsequent tracking of offenders who come through the HTW Track.Eligibility Criteria: The HTW track is open to any self-identified Native American, age 18 and over, with a substance abuse disorder and a pending felony level charge. Violent offenders are accepted on a case by case basis, pending team approval. The HTW track does not accept offenders charged with capital or sexual offenses. The participant can be from any tribe but does not have to be enrolled.
Referral Process: Referrals can be submitted by attorneys, judges, and probation and parole. Referrals can be submitted on pre-indicted cases, as part of a plea agreement and during post-plea stages of the court proceedings. Referred participants are required to report to the Drug Court Office every Friday between 8:00 am-11:00 am while pending assignment to the Healing to Wellness Drug Court Officer. The Healing to Wellness Drug Court Officer will then contact the referred participant to schedule the screening intake and to begin the treatment referral process.
Supervision and Compliance: Clients in the Healing to Wellness track are required to attend court two times per month. In addition, their attendance and participation in treatment programs and meetings with POs is also required.
Termination Criteria:Termination in this track is based upon a failure to meet with POs, failure to appear in court, or if an offender commits another crime. Additionally, if an offender poses a real threat to any of the staff or fellow clients at First Nations they will be asked to leave. Upon termination, the offender will return to the regular drug court track.The funding for this program came from a grant provided by Substance Abuse and Mental Health Services Administration. The grant began in 2017 and will run through 2020. The goal of The Second Judicial District Court and The New Mexico Department of Corrections is for the HTW track to continue after the grant period.The HTW track follows the example of its fellow court, the Bernalillo County Metropolitan Court, which runs the only other non-tribal HTW track in the nation. Many of the District Court's HTW track resources and program outlines are modeled after the Bernalillo County Metropolitan Court's current program. The treatment services that are provided through First Nations have been developed and proven successful throughout the years.Since the program's inception, the Native American Healing to Wellness track has worked alongside First Nations, a local non-profit social service program focused on Native American health and wellness. First Nations provides all the outpatient services including counseling, a food pantry, and job assistance. In addition, they offer cultural support through sweat lodges, talking circles and case management services.A strong partnership with First Nations and a dedicated team are the key factors contributing to the success of the program. Judge Zamora's leadership was instrumental in the creation of the track, and Judge Jaramillo commitment to the program has continued to strengthen the the program. Additionally, the success that has been seen through the Metropolitan Court's HTW track hashelped pave the way for the introduction of such an innovative program into the District Court's current system.One of the largest challenges has been learning how to coordinate with the necessary departments within the Second Judicial Court to make sure the HTW track is offered as an option. Other challenges have included:
Retraining many of the involved parties, such as parole officers on how and why the track was adopted.
Promoting the track in the community so that more self-identified Native Americans are aware of it and choose to participate.
Coordinating with First Nations Community Healthsource to ensure proper mechanisms are developed for tracking participant's progress throughout the program.
The Healing to Wellness team has learned how important it is for the participants to receive culturally specific treatment to assist in their journey of recovery. It is vitally important to find individuals who identify as Native American and who are interested in participating in the HTW track as early on in the intake process as possible is important.The recent creation of the HTW track impacts the total numbers served. Currently, there are two clients enrolled in the program. The Court is investing more effort into the promotion of the HTW track so that more Native American offenders are aware of the program.The effectiveness of the track is yet to be evaluated. However, because the track has been created in a spirit of shared partnerships with a focus on holistic services, there are high hopes for its success. Additionally, because the HTW track in The Bernalillo County Metropolitan Court has been extremely successful, and is the model for the District Court's track, it is anticipated that the District Court's HTW track will be equally successful.A launch event took place in May of 2018 in an effort to promote the new Healing to Wellness track within the community. The event was well attended with those present including the general community, community treatment providers, Native American treatment providers and members from the Public Defender's and District Attorney's offices, as well as members of the news media. Community response to the track has been very positive and supportive, which is exciting to see.Participants in the track are very interested in incorporating culture and traditions into the journey of recovery. The first participant in the HTW track recently advanced to phase 2 and has selected to participate in additional treatment beyond whatis required because of the cultural connection and individualized support .
The Second Judicial District Court will highlight the HTW track on it's website at the beginning of 2019.


With strategic headquarters in Vila Nova de Famalicão, the RNM Group bet on a strong presence and radius of intervention in the Iberian Peninsula.
Being the innovation a vector of permanent bet, infrastructure, always in evolution, allow a high storage capacity, support resources, logistics and laboratory support.
These spaces are endowed with industrial and licensing of the safety management system for the prevention and control of serious accidents, in accordance with the Policy Seveso, for effective response in limiting the consequences for the environment.
The appeal to the latest equipment in the laboratory area enables the development of solutions geared to the needs of each customer, through dilutions, mixtures and products tailor-made, adapted to each industrial reality, as well as the monitoring and validation of requirements of products available.
With around 25.000 m2 of infrastructures the RNM Group has been investing, strongly, in increasing its productive capacity and storage in the local market, being nowadays, the largest national distributor of chemical products.


One of France's most visited monuments, Mont-Saint-Michel, has now become an island again. French President Francois Hollande inaugurated a bridge on Saturday that restores the status of the famous site on France's Northern coast as an island.
The gigantic project, which cost 209 million euros, allows the sea to surround the Mont Saint-Michel for almost half of the year.
Speaking on the occasion, Hollande praised the success of the project that was initiated a decade ago following a project study that lasted for 10 years.
Accorded the coveted World Heritage Site status by Unesco in 1979, Mont-Saint-Michel is one of the most visited tourist sites in France, attracting an average of 2.5 million visitors each year.
Hollande, who was accompanied by Interior Minister Bernard Cazeneuve and Minister of Culture Fleur Pellerin, termed the abbey which was built in the ninth century as a "living image" of France.
"This is a natural pyramid topped by a cathedral. It is one of the highest places of European spirituality," he said.
As a result of this project, the old causeway and the visitor car which was located at the base of the monument have been removed.
The new parking area is now 2.5 km away from the monument. Visitors can travel to the site either on foot or by using a shuttle service.


In addition, the SNF supports a Scholarship Program, through which 156 children have received a scholarship to attend CTY Greece's specialized educational programs.
CTY Greece began its operation in 2013, in collaboration with the internationally renowned Johns Hopkins University, with a founding grant from the SNF. The Center, unique in its kind across Southeastern Europe, provides educational extracurricular programs, specifically designed for academically advanced students with a love of learning, through high quality and innovative teaching methods.
For more information regarding CTY-Creece, please visit the official website at www.cty-greece.gr.


Our Phlebotomy Program is designed to train individuals to collect blood for laboratory analysis necessary for the diagnosis and care of the patient. This one-semester program trains the phlebotomist to perform venipuncture, capillary puncture, and CPR.
Phlebotomy is the study or art of drawing blood for diagnosing and treating health problems or for blood donation. People who specialize in this study are called phlebotomy technicians. Phlebotomy technicians work in hospitals, clinics, blood banks, clinical laboratories and diagnostic centers. Phlebotomy technicians play an important role in health care. The primary job of a phlebotomy technician is to take blood samples from patients. The blood samples might be taken either for diagnosing illness or for blood donation. Blood analysis is one of the most important diagnostic tools available to medical clinicians, so it is vitally important that a blood sample is obtained in a way that does not compromise it in any way. If more than one test has been ordered, blood collection tubes must be drawn in a very specific order, and the phlebotomist is responsible for knowing that order. Additionally, venipuncture is an invasive procedure that can be frightening or even painful to the patient if performed incorrectly.
Becoming a phlebotomist is definitely a career worth considering if you want to work in health care or other related fields. The mean salary for a phlebotomist is $29,730 per year. A phlebotomist's salary can vary greatly due to size of institute and assignment of duties. Phlebotomy is one of the most lucrative careers in the field of health care. Not only is the job fun and rewarding, it is a great stepping stone to other career possibilities in the health care arena.


text:
Meet Lilly
Even after being diagnosed with brain cancer at the age of 8, patient Lilly continues to live out her love of music and dance, one day at a time.
"Lilly has always loved music," said her mom, Katie. "We used to perform at retirement homes as a family. Her daddy would play guitar, and I would sing, and Lilly would dance."
But in 2015, Lilly developed headaches and started having trouble balancing in ballet.
"In fact, the night before she was diagnosed we played a retirement home," Katie continued. "She wasn't feeling well, but she danced for an hour straight that night because she didn't want to let her elderly friends down."
The next day, an MRI showed Lilly had a mass in her brain. Following surgery near her home, Lilly received a diagnosis of medulloblastoma — brain cancer — and was quickly referred to St. Jude Children's Research Hospital.
Treatments invented at St. Jude have helped push the overall childhood cancer survival rate from 20% to more than 80% since it opened more than 50 years ago.
When Lilly came to St. Jude, she couldn't walk, talk or swallow. St. Jude music therapist Amy Love began visiting her hospital room to engage her through sound.
Lilly tapping the drum with a St. Jude staff member while working to regain movement and motor skills.
Though Lilly could not speak or even hold her head up much, Amy placed bells on her wrists and beat her hands on the drums, and you could just tell Lilly felt comfort and familiarity in that moment. She was smiling and happy!
Katie, Lilly's mom
St. Jude patient Lilly playing a speech therapy game
Now 10 years old, sweet Lilly has completed her treatment, which included radiation therapy and chemotherapy, and has returned home. Not only is she talking and walking, she's back to practicing ballet and even appeared in the spring recital.
Katie said, "She surprised everyone when she came out on stage and walked away from her walker. She performed without it pretty much the whole time and everyone was in tears."
Lilly and her dad perform an original dance that tells her story from diagnosis to present day.
"She's taught me what it's really like to persevere," said Katie. "She has taught me what it's like to fight even when you don't feel like you can go on any further."
Her father, Ben, added, "She's my hero. A lot of people don't get to meet theirs. Mine calls me Dad."
Your donation helps give kids like Lilly the chance to grow up.
Meet more patients
Meet Luna
When St. Jude patient Luna started to feel sick, her parents became concerned. Doctors in Guatemala confirmed what her parents had feared, Luna had leukemia.
Meet Griffin
As an Air Force family stationed abroad, Griffin's family was living in Germany when he was diagnosed with acute lymphoblastic leukemia on Veteran's Day 2019.
Meet Krew
What started out as a seemingly innocent bump on the head was later heartbreakingly revealed as cancer. Krew was diagnosed with leukemia at just 7 months old.
Meet Jean Pierre
At 18, Jean Pierre is headed to university. At three years old, he was treated at St. Jude Children's Research Hospital® for craniopharyngioma, a slow-growing brain tumor.
Meet Ava
Five-year-old Ava had a cancerous brain tumor called medulloblastoma. Referred to St. Jude, she underwent a second surgery, proton therapy and chemotherapy.
Meet Sariyah
In the summer of 2020, photos taken of St. Jude patient Sariyah by her mother showed signs of cancerous tumors in both eyes at the age of 18 months.
Meet Patient Lucas
Around Thanksgiving of 2020, three-year-old Lucas was complaining of headaches. His parents thought he just wanted attention. An MRI indicated something more serious.
Meet Eri'Elle
Eri'Elle was just two years old when her troubles started. Her mother took her to a pediatrician and insisted they run tests to get to the bottom of her baby's symptoms.
Meet Scarlet
At St. Jude, Scarlet and her mom lived at Target House for half a year while Scarlet was in treatment for acute myeloid leukemia. Her family never received a bill from St. Jude for treatment, travel, housing or food. .
Meet Gabe
When the lights on the Christmas tree hurt Gabe's eyes, his mom knew something was wrong.
Meet Calvin
At St. Jude, Calvin underwent chemotherapy, proton therapy and radiation treatments for rhabdomyosarcoma, a cancer of the soft tissue. He continues to receive treatment at St. Jude, having experienced a recurrence of cancer in 2020.
Meet Lydia
Lydia has an unusual hobby for a kid her age: reading scholarly articles in medical journals. But not just any articles. She's interested in those authored by Dr. Gajjar, the man – along with nurses and researchers and others at St. Jude – working to save her life.
Meet Gracie
Gracie was eight months old when she was diagnosed with blood cancer. She underwent chemotherapy and physical, occupational and speech therapies at St. Jude.
Meet Patient Mikayla
Over Christmas 2020, something was wrong with then one-year-old Mikayla. Referred to St. Jude, her cancer was identified as acute myeloid leukemia. Mikayla successfully underwent chemotherapy.
Meet Weiming
Weiming was diagnosed with a rare disease, an immunodeficiency known as bubble boy disease and came to St. Jude at just four months from his home country of China.
Fundraise at work
Start a workplace giving program at your office. Learn how to make your workplace fundraiser for St. Jude a success and get your employees engaged.
How can I sign up for St. Jude emails?
Learn more about how you can stay connected with St. Jude Children's Research Hospital.
Fundraise at school
Participate or organize a fundraising event for St. Jude in your school to help kids fighting cancer and other life-threatening diseases.


The dinoflagellate Peridinium aciculiferum forms blooms underneath the ice in temperate lakes and has previously been shown to have an allelopathic effect on a natural competitor as well as being haemolytic. Hence, we investigated whether P. aciculiferum is allelopathic towards a wide range of different freshwater phytoplankton species. We also tested the hypothesis that nutrient (N and P) deficiency enhances its allelopathic effect. In addition, we explored how target cell density affected the extent of the allelopathic effect. Our findings showed that P. aciculiferum had a negative effect (mortality through lyses) on Synura petersenii (Chrysophyceae), Peridinium inconspicuum (Dinophyceae), Cyclotella sp. (Bacillariophyceae), Cryptomonas sp. and Rhodomonas lacustris (Cryptophyceae). Only Chlamydomonas reinhardtii (Chlorophyceae) cells were not negatively affected. In semi-continuously grown nutrient deficient cultures, we detected no allelopathic activity, in contrast to the findings for batch cultures. Finally, we found that, at donor-target cell ratios above 6:1, the algicidal effect of P. aciculiferum did not increase. We concluded that the allelopathic activity of P. aciculiferum has a wide span of action due to either a single chemical or a cocktail of allelochemicals, but that allelopathic activity is highly variable depending on growth conditions.


These are some designs I have been working on. It is just a personal work, no client related. But I did made some decals and logos for the Fiat Bravo Club in Argentina which the client wanted to be as these.
For ages I've been in love with old cars and trucks, I would say I think they are much more "beautiful" than the ones you see around nowadays. I can't deny the technology though, I think the best thing is when old cars are combined with today's looks and technology.
That's why I started working on these series, and of course the name of the series defines it. In some models you will notice they were modded. I started working on American muscles, but then figured I like other cars too.
I simply traced the shapes in Adobe Ilustrator from pictures I found online*. I used different fonts in some models because of their nationality, but I'm not quite sure they'll remain the same.
I'll be surelly adding more models as I know there are lots of cars I think are iconic and that I love.
You are welcome to contact me for any reason, if you would like to buy any of these designs, or work with me.
* I do not hold the rights for the pictures used for the tracing. If there is a problem please contact me and we will solve it.
This was my second design but my favourite so far. I will ilustrate the process below.
This is more or less the process of modeling of the cars.


5 Tips For Finding a Name That Goes With Last Name: User Rating: 14 This technique will only work with certain last names, but incorporating the last name in the first name can yield a cool and unique name! Some examples of this baby naming practice include John Johnson, Max Maxwell, David Davidson or Rob Robertson.
Gran bella fica, un applauso a madre natura per quel che t'ha dato!
I'm using a fake name .
Hey , I would love to work with you?
feeel me smell ur neck.slowly whispering in ur ear. feel my lips all arnd ur neck. feel my fingers on ur fingers feel my lips all along ur spine.


Should you have a question that are not covered in the items below, please feel free to contact us.
Why should I join Galliard?
Recognised as one of the leading names in the London property market, Galliard has been building homes in and around London for over 20 years. We are able to offer our employees with unbeatable industry knowledge, the opportunity to work on varied and exciting projects, comprehensive training and an attractive benefits and rewards package. Explore the Galliard journey here.
Discover what our current employees have to say about a career at Galliard here.
Am I eligible to work at Galliard?
Galliard Homes is an equal opportunities employer; we encourage applications from all backgrounds, ethnic groups and age brackets. The minimum age to apply is 18. You must be a British citizen, an EC/EEA national or commonwealth citizen, or foreign national with no restrictions on your stay in the United Kingdom.
What job roles does Galliard offer?
Galliard recruit across a range of employment levels from Directors to Assistants in disciplines including Architecture, Construction, Finance, Legal, Marketing, and Sales.
Find out more information on our departments here.
How can I search and apply for jobs?
In order for us to deal with your application in the most efficient way, we ask that all applications are submitted via our website. You can search for positions by Job Title, Department, Location and Salary here.
Once you have found a position which satisfies your requirements you are able to find out further information and apply by following the links.
We ask that all online applications are accompanied by a CV. Unfortunately we are unable to consider any job application without a CV. You will be prompted to attach your CV as a word document as part of the online application.
If you cannot find a job position that suits your requirement you are invited to send us your CV to keep on file for future consideration. Upload your CV to our CV pool here.
We keep all CVs for two years and will contact you if anything suitable becomes available.
When I search for a specific job, it doesn't generate any results. What should I do?
If you're searching for a specific role with no results, it may be that we have no vacancies in that department at this time. You may therefore wish to broaden your search results. You can browse all current vacancies here.
If you cannot find a job position that suits your requirements you are invited to send us your CV to keep on file for future consideration. Upload your CV to our CV pool here.
Once you have submitted your application, please allow up to five working days for us to respond. If your application has been successful, our HR department will be in touch and you will be invited for an interview. In the event that your application is not successful, we will keep your CV on file for two years and contact you if anything suitable becomes available.
The type of interview or possible assessment will vary depending on the role that you have applied for. Prior to an interview we will be in contact to confirm the interview date, time, location and details of what the interview will entail.
In most instances you will meet with our recruitment officer and the hiring manager. We will expect you to have a good understanding of Galliard as a company. You may wish to prepare by exploring our website and researching any related news.
The interview process is the best opportunity for you to prove your industry knowledge and employment potential. You will be asked appropriate questions about your academic and professional background, your area of expertise and the property and construction industry. You should demonstrate a good sense of what value you could bring to the company and be prepared to use examples of past achievements and success to highlight this.
You will be given plenty of opportunities to ask us questions that will help you decide whether Galliard is the right company for you. For full information on what experience and qualifications you will need for your chosen role, please consult the job description.
Will I be able to get feedback on my interview?
We endeavor to provide all candidates with relevant feedback within three to five days of the date of the interviews concluding. Any candidates invited for a second interview will be notified and we will provide details of assessments or project requirements which may form part of the second interview process.
If your application is successful, written confirmation of your appointment will be posted and emailed to the address details provided on your CV.
Does Galliard offer any benefits?
At Galliard we offer our employees a wide range of benefits, including excellent pension plans, a fair and inclusive promotion policy, private healthcare and more.
View our full benefit package here.
What training can I expect in my new role?
As part of our long term commitment to our employees and their careers, we work hard to create an environment of continuous improvement tailored to our people, their role and needs. Subject to annual review, our Learning and development programme has been developed to advance the depth and breadth of our employee's professional knowledge and Galliard experience.


text:
Popularising Greece
Zorba the Greek: changing production companies and the Oscars
Walter Lassally	Film-maker
111. Zorba the Greek: the cast 98 03:57
112. Zorba the Greek: working with Anthony Quinn 118 03:44
113. Zorba the Greek: choosing to shoot in black and white 1 139 02:12
114. Zorba the Greek: film stock 82 01:28
115. Comparisons between Tom Jones and Zorba the... 71 01:57
116. Operating the camera on Zorba the Greek 55 00:46
117. Zorba the Greek: managing the lighting 86 04:58
118. Zorba the Greek: the finale 93 02:13
119. Zorba the Greek: changing production companies and the... 111 04:08
120. Popularising Greece 89 00:49
Initially the film was going to be a United Artists film, who had been the producers of Electra, the financiers of Electra. And then a problem appeared with Tony's stop-date. Tony had a contract, Tony Quinn that is, he had a contract with Fox to make High Wind in Jamaica, I think it was. That implied a stop-date that he couldn't go; he couldn't overshoot the schedule of Zorba by more than a week or so, and they thought that's a bit dicey, that we won't take that risk. So there was a problem. So, the solution to the problem was that Fox took over the film, took over Zorba, in the end and they produced the film. So there weren't any overlap. But we didn't go over schedule, so it wouldn't have been a problem, but they weren't, they weren't willing to risk that, so it became a Fox picture. Of course Fox drew all the benefit from it winning the Oscars and all that. To my considerable surprise I won the Oscar for that along with... along with Lila Kedrova and the art director, Vassilis Photopoulos. And, I was busy on a long documentary job for the National Film Board of Canada the next year. And, I didn't even realise I'd been nominated. And, many years later when Fox gave up their office in... they had a sort of a production office in London at that time, and many years later when they closed that all down, I received the Nomination Certificate which they put in some drawer somewhere, but it never reached me. And the Oscar itself, because I couldn't go, and I wouldn't go to Hollywood, because you have to go on speculation. You might win or you might not. It might be one of the other five nominees who win it. Also, that was the last but one year where there were two Oscars for photography. There was a black and white Oscar and a colour Oscar. I think it was My Fair Lady who won the colour Oscar that year. But two years later they abolished all that because there weren't anymore black and white films, as simple as that. Anyway, I wouldn't have gone to Hollywood anyway, and I couldn't go because I was busy, so the Oscar was received by Arlene Dahl, and there's wonderful photograph somewhere of... which says, 'Zorba photographer wins Oscar with Ilford stock', and above that is a photograph of two men shaking hands, neither of which is me. Because they panicked. They tried to get hold of me and they couldn't get hold of me and they panicked and they published this photograph. When you read the text it says, 'The Managing Director of Mole Richardson, Rome congratulates the Managing Director of Ilford, Italy, on Walter Lassally winning the Oscar for Zorba the Greek'. And, then the upshot of that was, afterwards, of course, I got a lot of offers. I got quite a few offers, all of which I turned down for various reasons. But I was offered Bunny Lake is Missing by Otto Preminger where I didn't like the script, and I was offered Arabesque by Stanley Donen where, again, I didn't like the script. And, there were some other films where Kate said no, he's busy, which was true. I was making than and they said well no he's not doing anything, we've checked. And she said yes, he's doing this long documentary for the National Film Board, and they said, 'A documentary?' They couldn't believe that you'd turn down a feature for a... because you were working on a documentary. Later I regretted it slightly. Occasionally I, sort of, said, well this would've been an entry into the American mainstream, but I was never really... I was never really keen on that. I wanted to make the kind of films, mainly intimate films. In that respect, Zorba is already an except... no, it is an intimate film. Zorba is less of an epic than Tom Jones. And... and I wanted to make that kind of... continue making that kind of movie. I didn't want to enter the American mainstream. But sometimes I have a, sort of, twinge of regret that I let that opportunity slip by a bit cavalierly, one could say.
Born in Germany, cinematographer Walter Lassally (1926-2017) was best known for his Oscar-winning work on 'Zorba the Greek'. He was greatly respected in the film industry for his ability to take the best of his work in one area and apply it to another, from mainstream to international art films to documentary. He was associated with the Free Cinema movement in the 1950s, and the British New Wave in the early 1960s. In 1987 he published his autobiography called 'Itinerant Cameraman'.
Title: "Zorba the Greek": changing production companies and the Oscars
Listeners: Peter Bowen
Peter Bowen is a Canadian who came to Europe to study and never got round to heading back home. He did his undergraduate work at Carleton University (in Biology) in Ottawa, and then did graduate work at the University of Western Ontario (in Zoology). After completing his doctorate at Oxford (in the Department of Zoology), followed with a year of postdoc at the University of London, he moved to the University's newly-established Audio-Visual Centre (under the direction of Michael Clarke) where he spent four years in production (of primarily science programs) and began to teach film. In 1974 Bowden became Director of the new Audio-Visual Centre at the University of Warwick, which was then in the process of introducing film studies into the curriculum and where his interest in the academic study of film was promoted and encouraged by scholars such as Victor Perkins, Robin Wood, and Richard Dyer. In 1983, his partner and he moved to Greece, and the following year he began to teach for the University of Maryland (European Division), for which he has taught (and continues to teach) biology and film courses in Crete, Bosnia, and the Middle East.
Tags: schedule, production, Oscar, documentary, film
Date story recorded: June 2004