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Field of Research (FoR)
SEO tags
Bio: Fiacre Rougieux has a PhD from the Australian National University in the field of photovoltaics and semiconductor materials. His current work lies at the intersection of materials science, nanotechnology, solid-state chemistry, solar cells and semiconductor physics. Between 2012 and 2015, he was an ARENA Post-doctoral Fellow at the ANU where he developed high-efficiency and low-cost solar cell concepts. One of the outcomes of my research...view more
Bio: Fiacre Rougieux has a PhD from the Australian National University in the field of photovoltaics and semiconductor materials. His current work lies at the intersection of materials science, nanotechnology, solid-state chemistry, solar cells and semiconductor physics. Between 2012 and 2015, he was an ARENA Post-doctoral Fellow at the ANU where he developed high-efficiency and low-cost solar cell concepts. One of the outcomes of my research was three consecutive world efficiency records for solar cells made with Updgraded Metallurgical Grade silicon. This technology now widely is used in the industry blended with other materials. Between 2016 and 2018, he was an ARC DECRA fellow at the ANU where he explored the physics of defects in high efficiency devices and successfully developed a wide range of processes to remove defects in solar cells and improve their efficiency. Typical process: oxygen dissolution, tabula rasa, vacancy-defect dissociation. Example of outcomes: 20% increase in relative efficiency. Fiacre is currently a Senior Lecturer at UNSW. He has published and co-authored more than 70 papers. His research interests include solar grade silicon, growth-related defects and advanced solar cells processes.
My main research interest is to generate the knowledge and techniques required for the development of very high efficiency silicon solar cells above 26%. I am looking for students enthusiastic about producing technologies to mitigate the negative impacts of defects on high-efficiency solar cells. The student would contribute to the development of novel solar cell processes to enable defect-free silicon and the development of new characterization techniques to image defects in silicon wafers allowing high efficiency solar cells to overcome their current limits and reach their true potential.
Solar Cells SOLA3507
Applied PV SOLA2540
My Grants
Publications and grants:
Take a look at my publication and citation record at Google Scholar or ORCIDID.
I have a been awarded more than 10 grants as first (two personal fellowships) and/or partner investigators in the past 5 years.
My Research Activities
Semiconductor materials: Solar grade feedstocks - Transport and Recombination in silicon - Fundamental semiconductor properties - Impact of growth and feedstock related defects - Defect Engineering - Optoelectronic device
Solar cells: High efficiency solar cells - High efficiency silicon solar cells - Characterization - Modelization - Advanced solar cells processes - Low cost solar cells - High efficiency n-type solar cells - Hybrid Solar Cells
Research projects:
We have a range of exciting PhD projects, Masters projects and undergraduate research projects available. Please contact me directly regarding PhD supervision, PhD scholarships and potential PhD projects. There are scholarship opportunities for international and domestic students. Example of PhD projects include:
- Breaking the materials-related limit of the highest efficiency solar cells
- Engineering the next generation of high efficiency solar cell materials
- Unravelling the nature of defects in high efficiency solar cells
- Developing advanced defect engineering for ultra-high efficiency solar cells
- Building an advanced theory of defect recombination in optoelectronic materials
- Demonstrating the impact of light element (oxygen and carbon) and point defect (interstitial and vacancies) on solar cells efficiency
See the research project section below for additional available research projects.
Previous and currently supervised PhD students:
Abhinav Sharma
Yalun Cai
Zhuangyi Zhou
Tan Xingru
Manjula Siriwardhana
Chang Sun
Hieu Nguyen
Peting Zheng
The University of New South Wales
Sydney, NSW, 2052
Map reference (Google map)
ORCID as entered in ROShttps://orcid.org/0000-0002-9879-1554
Research Activities
Nature of defects in silicon for very high efficiency solar cells
Recent studies have shown that defects limiting the lifetime of high-quality n-type silicon were unlikely to be related to metals or oxygen precipitates. These promising results were demonstrated using room-temperature passivation or photoluminescence lifetime imaging. In fact, these two techniques open a whole new range of possibilities in the study of very dilute defects in silicon which a student could capitalize-on. Applications include imaging of defects in as-grown ingots and determination of…


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Six for Star youngsters
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We value our content, so access to our full site is only available on subscription.
Your subscription entitles you to 7-day-a-week access to our website, plus a full digital copy of that week's paper to read on your pc/mac or mobile device.
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A fine end to 2019 saw Red Star 2004s travel to Cathcart and a superb display secured all three points, with a 6-1 win.
After a few slow starts to recent games the Star youngsters played with intensity right from the first whistle to the last.
Star raced into an early lead as McMurchy finished a fine move. A second followed when a nice cross field pass left Aitken clear and he coolly put the ball behind the keeper.
Cathcart tried to stage a fight back and pulled a goal back as Star stuttered for a few minutes but McMurchy restored Star's two goal lead when he finished from a tight angle. Halftime 3-1 for Star.
The second half was again dominated by the Lochgilphead boys. The fourth goal was an excellent solo effort from Caskie and effectively killed the game as a contest.
Aitken added goal number five when his fierce strike from wide zipped into the net. The sixth and final goal came in the final minutes when the Cathcart keeper parried a long range effort which was pounced on by West who placed the rebound beyond the unlucky goalie.
A great result against second placed Cathcart which hopefully sets Star up nicely for the rest of the season.

subset: commoncrawl


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Now that all my applications for grad school are completed, I am turning back to updating my website. I'd like to be happy with it soon so that once school starts I can just blog about my research and latest needlework projects. The learning curve is steep! But I think I'm almost there. Thank you for being patient with my work-in-progress and for finding me on Pinterest and Etsy. <3
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Jammu: A truck driver and his helper were killed when their vehicle skidded off the road and fell into a deep gorge in Kishtwar district of Jammu and Kashmir, police said on Tuesday.
The Jammu-bound truck plunged into the gorge near Dharmshalla on Kishtwar-Batote road around 10 pm on Monday, resulting in the death of the driver Kewal Krishan of Udhampur and helper Gulzar Ahmad of Nagrota, a police officer.
He said the bodies were recovered from the scene on Tuesday morning and were handed over to their families. (PTI)
Amitabh Bachchan, son Abhishek test positive for COVID-19, hospitalised
Renowned neuro physician Dr Sushil Razdan on digital platform
We lost another gem: Amitabh Bachchan remembers co-star Jagdeep
Director Bhansali reaches police station to record statement
Ajay Devgn's "Maidaan" to release in August 2021
© 2020 State Times Daily Newspaper


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- Product Type
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Displaying brilliant pastels and deep golden colors, labradorite features a spellbinding "black rainbow" of color. When appreciating the iridescent play of colors known as labradorescence, observe the strength and intensity by viewing from different angles, as different colors or even a range of colors may be visible from different positions.
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Comments (4)
Yes, thank you one and all.
Thank you all so much.
gmatx zone 6
A very deep and strong felt thank you to all of our veterans. It is always an extra special day for our family as it is also my father's birthday. He served in WWI and would have been 125 years old today. A large majority of my mother's family served in the military. Her baby brother was a prisoner of war during the Korean War and one of the older brothers was killed in WWII. I am so grateful that both of our sons served and returned home safely. Do you ever think of where we/this country would be if not for our veterans?
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Lucky grandchildren get a camp all to themselves, thanks to tents and a bathhouse with beds in their grandparents' backyardFull Story


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Azure, microsoft's cloud platform
Azure is the cloud platform from microsoft and in it's simplest way it's just a bunch of services wich is offered over the internet. Azure has to compete with the Google App engine and Amazon's EC2 service. Microsoft's own definiton of Azure is:
Windows Azure is an open and flexible cloud platform that enables you to quickly build, deploy, and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool, or framework. And you can integrate your public cloud applications with your existing IT environment.
Cloud computing categories
There are several cloud computing categories offered by azure:
IAAS: infrastructure as a service the customer is responsible for configuring and managing the virtual machines
PAAS: platform as a service customer develops and deploys applications and is resposible for it.
SAAS: software as service Like hotmail, customer use the application and is responsible for noting.
Why use azure
- There are three reasons to use cloud
With azure you have a new Virtual Machine running in minutes. With your on premise hardware a new Virtual Machine can take some day's,
No hard- en software differences. You can build your OTAP wicht exactly the same environment. No hard en software differences mean you can really test your software in the test-environment
The ability to scale up AND down your environment
No costs in buying and maintaining soft en hardware
No cost in maintaining knowledge to maintain soft en hardware
Azure services
So if azure is just a bunch of services, what services are there?
Compute services
Compute services is the base of microsoft azure and the base of the computing services is the virtual machine. You just have to go to the portal enter some data push the button and there you have your compute service. There are four different flavours of azure computing service and with this four azure is very flexible.
Virtual machines
You can easily create a virtual machine with azure portal. Just login fill in some details, click on create, and within two minutes there is your virtual machine ready to go. Important feature here is speed. Azure offers you default some ready to go virtual machines with, for example, SQL-server pre-installed.
As easily as creating a virtual machine you can create a website. Just fil in the details, click and there is your website. It's easier and faster as creating a virtual machine and then create your website. The maintenance on the server used for this website and IIS used to host this website is done by Azure. So ease of use is the key-point here next to lack of control on IIS or and the server.
Cloud services
Azure cloud services offers the easiness of azure websites but with a bit more control on IIS of the server. It's meant for dedicated front-end or middle-tier applicatins on IIS with a bit more control on the server. Cloud services offers two roles:
- Web role: a dedicated IIS server used for front-end or middl-end applications
- Work role: for applications that asynchroneously and generally perform long runnig data processing task independent of the user interaction
Mobile services
Mobile services helps you the accelerate the process of developing mobile apps. The mobile services can be divided in four segments
- Data, store and retrieve data
- Users, validate user credentials
- Push, send notifactions
- Services, schedule jobs
Network services
Virtual network
Azure virtual network helps you to connect your on premise applications to the cloud. Therefor are to kinds of vpn-connections available:
- Point to point
- Site to site
There is somewhere a list of supported vpn devices
Traffic manager
The traffic manager let's you load balance incomming traffic accross multiple hosted windows azure services.
- you can use traffic manager for
- performance
- round robbin, just equally distribute
- failover, transfer the traffic to a backup device
Data services
SQL server
With SQL server you have, again, the two options
- Use a virtual server, install SQL and do all the maintenance
- Use a azure database, maintenance is done by microsoft azure
Data recovery
Azure offers a backup-manager wich will backup your data. You can specify the regions where you want your data to be stored. The recovery managers just works like a general recovery manager and gives yo the option to restore your data. SQL server is default daily backuped and saved for thirty day's.
Azure active directory
windows azure active directory services is a subset of the active directory services installed on the Windows2012 server. With windows azure active directory services you can control the access to all azure applications in the cload, it takes care of a single sign on situation in the whole company. Azure AD just, only stores user-information, groups e.d. in comparision to a full blown AD store allso hardware information, computers, servers, printers e.d If you want a complete cloud solution AD has to be installed on a virtual machine. Or of course you use the on-premise AD server.
Windows azure queue
If applications has to talk to each other on windows azure you can use the windws azure queue. The sending application place a message in the queue and has to know nothing of the receiving application. The receiving app process the message. It's easy to implement, scales perfectly and can be a good choise for use as application has to interact wich each other.


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Essential Information
A bachelor's degree in clinical laboratory science is the typical requirement to obtain state licensure as a medical lab technician or technologist. This degree is also usually sufficient to qualify for an optional certification in laboratory sciences. Students in the degree program will study many topics, among them genetics, biochemistry, immunology and serology. Students may also choose a specialization such as transfusion medicine, clinical chemistry, histology and hematology. Completion of a clinical practicum, in addition to standard coursework, is typically required so that students may get the professional field experience needed for their career.
Students must hold a high school diploma, have a satisfactory GPA, college entrance examination scores and undergo a criminal background check in order to be accepted into most programs. Additionally, there may be course prerequisites covering topics like biology, microbiology, chemistry and organic chemistry, immunization.
Bachelor of Science Degree in Clinical Laboratory Science
A bachelor of science degree in clinical laboratory science provides the theoretical framework and hands-on experience needed to attain excellence in the field. Students take a number of medical science courses in addition to lab-specific coursework. Classes generally include:
- Molecular biology
- Clinical laboratory administration and management
- Human anatomy
- Physiology
- Microbiology
- Cell biology
Career Outlook and Salary Information
According to the U.S. Bureau of Labor Statistics (BLS), job growth for clinical and medical laboratory technologists and technicians will increase by a projected 11% for the years 2018 through 2028.
The BLS reported in May 2018 that clinical and medical laboratory technologists and technicians earned median annual salaries of $52,330.
Continuing Education
Some states require licensure for clinical laboratory scientists, and such credentials generally require continuing education credits. A number of professional organizations, such as ASCLS, the American Society for Clinical Pathology and American Medical Technologists, offer annual symposiums and regular continuing education opportunities. Graduate programs also exist for those seeking to pursue research or academic careers.
Clinical and medical laboratory technologists and technicians must compete a bachelor's degree program in clinical laboratory science. Upon graduation, they can look forward to a positive job growth over the next decade, as well as a competitive salary.


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Please, NO DOGS at the Summer Plant Sale or any special event.
Jul 2020
Annual Plant Sale
Summer is our favorite time of year in Flagstaff! The summer plant sale includes hundreds of native plants (grown by us) for purchase. This year our plant sale will be […]Find out more »


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Though brick and mortar retail has gone on the decline, there are some positive signs for this industry. A number of companies have have rebounded remarkably from retail's struggles through the adaptation of new technology. This article covers some of the basics of how technology has transformed retail for the better.
Read the full article here:
The Retail TouchPoints Blog • Brick-And-Mortar Retail: Death Or Resurrection By Technology?


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Can You Complete These Cardi B Lyrics?
Welcome to the Cardi B lyrics quiz.
How well do you really know Cardi B songs? Are you ready to test your knowledge?
We all know the lyrics to the I Like it, but what about the other songs?
Find out how well you know the lyrics to these 10 Cardi B's songs!
This is a medium difficulty quiz that has 10 questions about Cardi B lyrics.


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Hierarchical Foresight:
Self-Supervised Learning of Long-Horizon
Tasks via Visual Subgoal Generation
Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor scalability of sampling-based planning optimizers, one significant limitation of these approaches is the ability to plan over long horizons to reach distant goals. To that end, we propose a framework for subgoal generation and planning, hierarchical visual foresight (HVF), which generates subgoal images conditioned on a goal image, and uses them for planning. The subgoal images are directly optimized to decompose the task into easy to plan segments, and as a result, we observe that the method naturally identifies semantically meaningful states as subgoals. Across three out of four simulated vision-based manipulation tasks, we find that our method achieves nearly a 200% performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes.
1 Introduction
Developing robotic systems that can complete long horizon visual manipulation tasks, while generalizing to novel scenes and objectives, remains an unsolved and challenging problem. Generalization to unseen objects and scenes requires robots to be trained across diverse environments, meaning that detailed supervision during data collection in not practical to provide. Furthermore, reasoning over long-horizon tasks introduces two additional major challenges. First, the robot must handle large amounts of uncertainty as the horizon increases. And second, the robot must identify how to reach distant goals when only provided with the final goal state, a sparse indication of the task, as opposed to a shaped cost that implicitly encodes how to get there. In this work, we aim to develop a method that can address these challenges, leveraging self-supervised models learned using only unlabeled data, to solve novel temporally-extended tasks. ††Work completed at Google Brain ††Videos and code are available at: https://sites.google.com/stanford.edu/hvf
Model-based reinforcement learning has shown promise in generalizing to novel objects and tasks, as learned dynamics models have been shown to generalize to new objects (Finn and Levine, 2016; Ebert et al., 2018b), and can be used in conjunction with planning to reach goals unseen during training. However, planning to reach temporally distant goals is difficult. As the planning horizon increases model error compounds, and the cost function often provides only a noisy or sparse signal of the objective. Both of these challenges are both exacerbated when planning in visual space.
In this work, the key insight that we leverage is that while model error and sparse cost signals can make long horizon planning difficult, we can mitigate these issues by learning to break down long-horizon tasks into short horizon segments. Consider, for example, the long horizon task of opening a drawer and putting a book in it, given supervision only in the form of the final image of the open drawer containing the book. The goal image provides nearly no useful cost signal until the last stage of the task, and model predictions are likely to become inaccurate beyond the first stage of the task. However, if we can generate a sequence of good subgoals, such as (1) the robot arm grasping the drawer handle, (2) the open drawer, and (3) the robot arm reaching for the book, planning from the initial state to (1), from (1) to (2), from (2) to (3), and from (3) to the final goal, the problem becomes substantially easier. The subgoals break the problem into short horizon subsegments each with some useful cost signal coming from the next subgoal image.
Our main contribution is a self-supervised hierarchical planning framework, hierarchical visual foresight (HVF), which combines generative models of images and model predictive control to decompose a long-horizon visual task into a sequence of subgoals. In particular, we propose optimizing over subgoals such that the resulting task subsegments have low expected planning cost. However, in the case of visual planning, optimizing over subgoals corresponds to optimizing within the space of natural images. To address this challenge, we train a generative latent variable model over images from the robot's environment and optimize over subgoals in the latent space of this model. This allows us to optimize over the manifold of images with only a small number of optimization variables. When combined with visual model predictive control, we observe that this subgoal optimization naturally identifies semantically meaningful states in a long horizon tasks as subgoals, and that when using these subgoals during planning, we achieve significantly higher success rates on long horizon, multi-stage visual tasks. Furthermore, since our method outputs subgoals conditioned on a goal image, we can use the same model and approach to plan to solve many different long horizon tasks, even with previously unseen objects. We first demonstrate our approach in simulation on a continuous control navigation task with tight bottlenecks, and then evaluate on a set of four different multi-stage object manipulation tasks in a simulated desk environment, which require interacting with up to 3 different objects. In the challenging desk environment, we find that our method yields at least a 20% absolute performance improvement over prior approaches, including model-free reinforcement learning and a state of the art subgoal identification method. Finally, we show that our approach generates realistic subgoals on real robot manipulation data.
2 Related Work
Developing robots that can execute complex behaviours from only pixel inputs has been a well studied problem, for example with visual servoing (Mohta et al., 2014; Espiau et al., 1992; Wilson et al., 1996; Yoshimi and Allen, 1994; Jagersand et al., 1997; Lampe and Riedmiller, 2013; Sadeghi et al., 2018; Sadeghi, 2019). Recently, reinforcement learning has shown promise in completing complex tasks from pixels (Ghadirzadeh et al., 2017; Levine et al., 2015; Kalashnikov et al., 2018; Lange et al., 2012; OpenAI et al., 2018; Schenck and Fox, 2016; Matas et al., 2018; James et al., 2017; Singh et al., 2019), including in goal-conditioned settings (Kaelbling, 1993; Schaul et al., 2015; Andrychowicz et al., 2017; Sadeghi et al., 2018; Sadeghi, 2019; Nair et al., 2018a). While model-free RL approaches have illustrated the ability to generalize to new objects (Kalashnikov et al., 2018) and learn tasks such as grasping and pushing through self-supervision (Pinto and Gupta, 2015; Zeng et al., 2018), pure model-free approaches generally lack the ability to explicitly reason over temporally-extended plans, making them ill-suited for the problem of learning long-horizon tasks with limited supervision.
Video prediction and planning have also shown promise in enabling robots to complete a diverse set of visuomotor tasks while generalizing to novel objects (Finn and Levine, 2016; Kalchbrenner et al., 2016; Boots et al., 2014; Byravan and Fox, 2016). Since then, a number of video prediction frameworks have been developed specifically for robotics (Babaeizadeh et al., 2017; Lee et al., 2018; Ebert et al., 2017), which combined with planning have been used to complete diverse behaviors (Nair et al., 2018b; Ebert et al., 2018b; Paxton et al., 2018; Xie et al., 2019). However, these approaches still struggle with long horizon tasks, which we specifically focus on.
One approach to handle long horizon tasks is to add compositional structure to policies, either from demonstrations (Krishnan et al., 2017; Fox et al., 2018), with manually-specified primitives (Xu et al., 2017; Huang et al., 2018), learned temporal abstractions (Neitz et al., 2018), or through model-free reinforcement learning (Sutton et al., 1999; Barto and Mahadevan, 2003; Bacon et al., 2016; Nachum et al., 2018; Levy et al., 2019). These works have studied such hierarchy in grid worlds (Bacon et al., 2016) and simulated control tasks (Nachum et al., 2018; Eysenbach et al., 2018; Levy et al., 2019) with known reward functions. In contrast, we study how to incorporate compositional structure in learned model-based planning with video prediction models. Our approach is entirely self-supervised, without motion primitives, demonstrations, or shaped rewards, and scales to vision-based manipulation tasks.
Classical planning methods have been successful in solving long-horizon tasks (LaValle, 2006; Choset et al., 2005), but make restrictive assumptions about the state space and reachability between states, limiting their applicability to complex visual manipulation tasks. Similarly, completing long horizon tasks has also been explored with symbolic models (Toussaint et al., 2019) and Task and Motion Planning (TAMP) (Kaelbling and Lozano-Perez, 2011; Srivastava et al., 2014). However, unlike these approaches our method requires no additional knowledge about the objects in the scene nor any predefined symbolic states. Recently, there have been several works that have explored planning in learned latent spaces (Kurutach et al., 2018; Ichter and Pavone, 2018; Watter et al., 2015; Srinivas et al., 2018). This has enabled planning in higher dimensional spaces, however these methods still struggle with long-horizon tasks. Furthermore, our hierarchical planning framework is agnostic to state space, and could directly operate in one of the above latent spaces.
A number of recent works have explored reaching novel goals using only self-supervision (Finn and Levine, 2016; Eysenbach et al., 2019; Kurutach et al., 2018; Wang et al., 2019; Jayaraman et al., 2019; Nair et al., 2018a). In particular, time-agnostic prediction (TAP) (Jayaraman et al., 2019) aims to identify bottlenecks in long-horizon visual tasks, while other prior works (Nair et al., 2018a; Finn and Levine, 2016) reach novel goals using model-free or model-based RL. We compare to all three of these methods in Section 5 and find that HVF significantly outperforms all of them.
3 Preliminaries
We formalize our problem setting as a goal-conditioned Markov decision process (MDP) defined by the tuple where is the state space, which in our case corresponds to images, is the action space, governs the environment dynamics, represents the set of goals which is a subset of possible states, represents the cost of being in state given that the goal is , and is the discount factor. In practice, acquiring cost functions that accurately reflect the distance between two images is a challenging problem (Yu et al., 2019). We make no assumptions about having a shaped cost, assuming the simple yet sparse distance metric of distance in pixel space in all of our experiments. Approaches that aim to recover more shaped visual cost functions are complementary to the contributions of this work.
In visual foresight, or equivalently, visual MPC (Finn and Levine, 2016; Ebert et al., 2018b), the robot collects a data set of random interactions from a pre-determined policy. This dataset is used to learn a model of dynamics through maximum likelihood supervised learning. Note the states are camera images, and thus is an action-conditioned video prediction model. Once the model is trained, the robot is given some objective and plans a sequence of actions that optimize the objective via the cross entropy method (CEM) (Rubinstein and Kroese, 2004). In this work, we will assume the objective is specified in the form of an image of the goal , while CEM aims to find a sequence of actions that minimize the cost between the predicted future frames from and the goal image. While standard visual foresight struggles with long-horizon tasks due to uncertainty in as the prediction horizon increases and sparse for CEM to optimize, in the next section we describe how our proposed approach uses subgoal generation to mitigate these issues.
4 Hierarchical Visual Foresight
Overview: We propose hierarchical visual foresight (HVF), a planning framework built on top of visual foresight (Finn and Levine, 2016; Ebert et al., 2018b) for long horizon visuomotor tasks. We observe that when planning to reach a long horizon goal given only a goal image, standard planning frameworks struggle with (1) sparse or noisy cost signals and (2) compounding model error. Critically, we observe that if given the ability to sample possible states, there is potential to decompose a long horizon planning task into shorter horizon tasks. While this idea has been explored in classical planning (Betts, 2010), state generation is a significant challenge when dealing with high dimensional image observations.
One of our key insights is that we can train a deep generative model, trained exclusively on self-supervised data, as a means to sample possible states. Once we can sample states, we also need to evaluate how easy it is to get from one sampled state to another, to determine if a state makes for a good subgoal. We can do so through planning: running visual MPC to get from one state to another and measuring the predicted cost of the planned action sequence. Thus by leveraging the low-dimensional space of a generative model and the cost acquired by visual MPC, we can optimize over a sequence of subgoals that lead to the goal image. In particular, we can explicitly search in latent image space for subgoals, such that no segment is too long-horizon, mitigating the issues around sparse costs and compounding model error.
In the following sections, we will describe more formally how HVF works, how we learn the generative model, how we learn the dynamics model, how goal-conditioned planning with visual MPC is executed, and lastly how subgoals are optimized.
Hierarchical visual foresight: Formally, we assume the goal conditioned MDP setting in Section 3 where the agent has a current state , goal state , cost function , and dataset of environment interaction . This data can come from any exploration policy; in practice, we find that interaction from a uniformly random policy in the continuous action space of the agent works well. From this data, we train both a dynamics model using maximum likelihood supervised learning, as well as a generative model .
Now given and , the objective is to find subgoals that enable easier completion of the task. Our hope is that the subgoals will identify steps in the task such that, for each subsegment, the planning problem is easier and the horizon is short. While one way to do this might be to find subgoals that minimize the total planning cost, we observe that this does not necessarily encourage splitting the task into shorter segments. Consider planning in a straight line: using any point on that line as a subgoal would equally minimize the total cost. Therefore, we instead optimize for subgoals that minimize the worst expected planning cost across any segment. This corresponds to the following objective:
where is the cost achieved by the planner when planning from to , which we compute by planning a sequence of actions to reach from using and measuring the predicted cost achieved by that action sequence111We compare max/mean cost in Section 5.4. Once the subgoals are found, then the agent simply plans using visual MPC (Finn and Levine, 2016; Ebert et al., 2018b) from each to until a cost threshold is reached or for a fixed, maximum number of timesteps, then from to , until planning to the goal state, where is the actual state reached when running MPC to get to . For a full summary of the algorithm, see Algorithm 1. Next, we describe individual components in detail.
Generative model: To optimize subgoals, directly optimizing in image space is nearly impossible due to the high dimensionality of images and the thin manifold on which they lie. Thus, we learn a generative model such that samples represent possible futures in the scene, and optimize in the low-dimensional latent space . In settings where aspects of the scene or objects change across episodes, we only want to sample plausible future states, rather than sampling states corresponding to all scenes. To handle this setting, we condition the generative model on the current image, which contains information about the current scene and the objects within view. Hence, in our experiments, we use either a variational autoencoder (VAE) or a conditional variational autoencoder (CVAE), with a diagonal Gaussian prior over , i.e. . In the case of the CVAE, the decoder also takes as input an encoding of the conditioned image, i.e. . In practice, we use the inital state as the conditioned image. We train the generative model on randomly collected interaction data. The architecture and training details can be found in Appendix A.3
Dynamics model: The forward dynamics model can be any forward dynamics model that estimates . In our case states are images, so we use an action conditioned video prediction model, stochastic variational video prediction (SV2P) (Babaeizadeh et al., 2017) for . We train the dynamics model on randomly collected interaction data. Architecture and training parameters are identical to those used in (Babaeizadeh et al., 2017); details can be found in Appendix A.4.
MPC & planning with subgoals: When optimizing subgoals, we need some way to measure the ease of planning to and from the subgoals. We explicitly compute this expected planning cost between two states by running model predictive control 222Pseudocode for MPC can be found in Appendix A.1 as done in previous work (Finn and Levine, 2016; Nair et al., 2018b; Ebert et al., 2018b). This uses the model to compute the optimal trajectory between and , and returns the optimal action and associated cost . Note this does not step any actions in the real environment, but simply produces an estimated planning cost. Details on this procedure can be found in Appendices A.1 and A.2. Once the subgoals have been computed, the same procedure is run until is reached (measured by a cost threshold) or for a fixed, maximum number of timesteps, then from , …, (Alg. 1 Lines 17:21), where represents the state actually reached after trying to plan to . In this case, the best action at each step is actually applied in the environment until the task is completed, or the horizon runs out. Note we only compute subgoals once in the beginning calling , the plan with those subgoals. In principle HVF can be called at every step, but for computational efficiency we only call HVF once.
Subgoal optimization: Since we need to search in the space of subgoals to optimize Equation 1, we perform subgoal optimization using the cross entropy method (CEM) (Rubinstein and Kroese, 2004) in the latent space of our generative model . Note this subgoal optimization CEM is distinct from the CEM used for computing the planning cost, which we use as a subroutine within this outer level of CEM. At a high-level, the subgoal optimization samples a list of subgoals, evaluates their effectiveness towards reaching the goal, and then iteratively refines the subgoals through resampling and reevaluation. We begin by drawing samples from a diagonal Gaussian distribution where the dimensionality of is , where is the number of subgoals and is the size of the latent dimension of the generative model (Alg. 1 Line 6). Thus, one sample from gives us latents each of size , each of which is decoded into a subgoal image (Alg. 1 Line 8). Then, as described in Equation 1, the cost for one sample (one set of subgoals) is computed as the maximum planning cost across the subsegments (Alg. 1 Lines 9:12). The samples are ranked by this cost, and then is refit to the latents of the best samples (Alg. 1 Lines 14:15). In all of our experiments we use , and .
5 Experiments
In our experiments, we aim to evaluate (1) if, by using HVF, robots can perform challenging goal-conditioned long-horizon tasks from raw pixel observations more effectively than prior self-supervised approaches, (2) if HVF is capable of generating realistic and semantically significant subgoal images, and (3) if HVF can scale to real images of cluttered scenes. To do so, we test on three domains: simulated visual maze navigation, simulated desk manipulation, and real robot manipulation of diverse objects. The simulation environments use the MuJoCo physics engine (Todorov et al., 2012). We compare against three prior methods: (a) visual foresight (Finn and Levine, 2016; Ebert et al., 2018b), which uses no subgoals, (b) RIG (Nair et al., 2018a) which trains a model-free policy to reach generated goals using latent space distance as the cost, and (c) visual foresight with subgoals generated by time-agnostic prediction (TAP) (Jayaraman et al., 2019), a state-of-the-art method for self-supervised generation of visual subgoal, which generates subgoals by predicting the most likely frame between the current and goal state. Lastly, we perform ablation studies to determine the effect of various design choices of HVF333Videos and code are available at http://sites.google.com/stanford.edu/hvf.
5.1 Maze Navigation
First, we study HVF in a 2D maze with clear bottleneck states, as it allows us to study how HVF compares to oracle subgoals. In the the maze navigation environment, the objective is for the agent (the green block) to move to a goal position, which is always in the right-most section. To do so, the agent must navigate through narrow gaps in walls, where the position of the gaps, goal, and initial state of the agent are randomized within each episode. The agent's action space is 2D Cartesian movement, and the state space is top down images. We consider three levels of difficulty, "Easy" where the agent spawns in the rightmost section, "Medium" where the agent spawns in the middle, and "Hard" where the agent spawns in the leftmost section. The environment and these levels are illustrated in Figure 3. Further details are in Appendix B.1.
Results: In Figure 2, we observe that using HVF subgoals consistently improves success rate over prior methods, TAP (Jayaraman et al., 2019) and visual foresight (Finn and Levine, 2016) without subgoals, indicating that it is able to find subgoals that make the long-horizon task more manageable. Additionally, we compare to the "Ground Truth Bottleneck" that uses manually-designed subgoals, where the subgoal is exactly at the gaps in the walls (or the midpoint between states in the "easy" case). We find that while using the oracle subgoals tends to yield the highest performance, the oracle subgoals do not perform perfectly, suggesting that a non-trivial amount of performance gains are to be had from improving the consistency of the video prediction model and cost function for short-horizon problems, as opposed to the subgoals. We observe that while TAP gets similar performance to HVF on the "Easy" and "Medium" cases, it has significantly lower performance in the longest horizon "Hard" setting. Additionally we see that Recursive TAP with 2 subgoals has lower performance across all difficulties as the subgoals it outputs are very close to current/goal state (See Figure 9).
We next qualitatively examine the subgoals discovered by HVF in Figure 3, and find empirically that they seem to correspond to semantically meaningful states. In this task there are clear bottlenecks - specifically reaching the gaps in the walls and find that HVF outputs close to these states as subgoals. For example, when the agent starts in the leftmost section, and has to pass through two narrow gaps in walls, we observe the first subgoal goal corresponds to the agent around the bottleneck for the first gap and the second subgoal is near the second gap.
5.2 Simulated Desk Manipulation
We now study the performance improvement and subgoal quality of HVF in a challenging simulated robotic manipulation domain. Specifically, a simulated Franka Emika Panda robot arm is mounted in front of a desk (as used in (Lynch et al., 2019)). The desk consists of 3 blocks, a sliding door, three buttons, and a drawer. We explore four tasks in this space: door closing, 2 block pushing, door closing + block pushing, and door closing + 2 block pushing. Example start and goal images for each task are visualized in Figure 5, and task details are in Appendix B.2. The arm is controlled with 3D Cartesian velocity control of the end-effector position. Across the 4 different tasks in this environment, we use a single dynamics model and generative model . Experimental details are in the Appendix B.2.
Results: As seen in Figure 4, we find that using HVF subgoals dramatically improves performance, providing at least a 20% absolute improvement in success rate across the board. In the task with the longest horizon, closing the door and sliding two blocks off the table, we find that using no subgoals or 1 subgoal has approximately performance, but 2 subgoals leads to over 42% success rate. We compare to subgoals generated by time agnostic prediction (TAP) (Jayaraman et al., 2019) and find that while it does generate plausible subgoals, they are very close to the start or goal, leading to no benefit in planning. We also compare against RIG (Nair et al., 2018a), where we train a model free policy in the latent space of the VAE to reach "imagined" goals, then try and reach the unseen goals. However, due to the complexity of the environment, we find that RIG struggles to reach even the sampled goals during training, and thus fails on the unseen goals. Qualitatively, in Figure 5, we also observe that HVF outputs meaningful subgoals on the desk manipulation tasks. For example, it often produces subgoals corresponding to grasping the door handle, sliding the door, or reaching to a block.
5.3 Real Robot Manipulation
Lastly, we aim to study whether HVF can extend to real images and cluttered scenes. To do so, we explore the qualitative performance of our method on the BAIR robot pushing dataset (Ebert et al., 2018a). We train and on the training set, and sample current and goal states from the beginning and end of test trajectories. We then qualitatively evaluate the subgoals outputted by HVF. Further implementation details are in the Appendix B.3.
5.4 Ablations
In the ablations, we explore three primary questions: (1) What is the optimal number of subgoals?, (2) Is there a difference between HVF using the max and mean cost across subsegments?, and (3) Is HVF as valuable when the samples used for visual MPC is significantly increased? We evaluate these questions in the maze navigation task on the "Hard" difficulty. All results report success rates over 100 trials using random initialization/goals.
|Success %
Number of Subgoals: We explore how HVF performs as we increase the number of subgoals in Table 1. Interestingly, we observe that as we scale the number of subgoals beyond 2, the performance starts to drop, and with 5 or more subgoals the method fails. We conclude that this is due to the increasing complexity of the subgoal search problem: the sampling budget allocated for subgoal optimization is likely insufficient to find a large sequence of subgoals.
|Mean Cost
|Max Cost
Max vs Mean: In our HVF formulation, we define the subgoal optimization objective as finding the subgoals which minimize the max cost across subsegments. In Table 2 compare additionally to using the mean cost, and find that using the max cost is marginally better.
Sample Quantity: In Table 3, we examine how the number of samples used in visual MPC affects the relative improvement of HVF. Using more samples should also mitigate the challenges of sparse costs, so one might suspect that HVF would be less valuable in these settings. On the contrary, we find that HVF still significantly outperforms no subgoals, and the improvement between using 0 and 1 subgoals is even more significant.
|200 Samples
|1000 Samples
Additional Ablations: Additionally we explore how performance of HVF scales as the planning horizon of visual MPC is increased. For longer horizons 10 or 15 steps instead of 5, we find that HVF still has the highest performance, but that using 1 subgoal outperforms using 2. Exact numbers can be found in Appendix C.1.1. We also use distance in the VAE latent space as planning cost instead of pixel cost. We find that pixel cost performs notably better; details are in Appendix C.1.2.
6 Conclusion and Limitations
We presented an self-supervised approach for hierarchical planning with vision-based tasks, hierarchical visual foresight (HVF), which decomposes a visual goal into a sequence of subgoals. By explicitly optimizing for subgoals that minimize planning cost, HVF is able to discover semantically meaningful goals in visual space, and when using these goal for planning, perform a variety of challenging, long-horizon vision-based tasks. Further, HVF learns these tasks in an entirely self-supervised manner without rewards or demonstrations.
While HVF significantly extends the capabilities of prior methods, a number of limitations remain. One limitation of the current framework is its computational requirements, as the nested optimization procedure requires many iterations of MPC with expensive video prediction models. We expect that this can be mitigated by reformulating the subgoal prediction problem as policy inference, and training a subgoal generation policy on a distribution of tasks in the loop of training, an interesting direction for future work. Further, while the development of accurate visual cost functions and predictive models were not the main aim of this work, the performance with oracle subgoals suggests this to be a primary bottleneck in furthering the performance of HVF. Lastly, development of better generative models that can capture the scene and generate possible futures remains an open and challenging problem. Work in generative models that enable better generation of cluttered scenes with novel objects could improve the applicability of HVF in real-world settings.
We would like to thank Sudeep Dasari, Vikash Kumar, Sherry Moore, Michael Ahn, and Tuna Tokosz for help with infrastructure, as well as Sergey Levine, Abhishek Gupta, Dumitru Erhan, Brian Ichter, and Vincent Vanhouke for valuable discussions. This work was supported in part by an NSF GRFP award. Chelsea Finn is a CIFAR Fellow in the Learning in Machines & Brains program.
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- Between mdps and semi-mdps: a framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112, pp. 181–211. Cited by: §2.
- MuJoCo: a physics engine for model-based control.. See conf/iros/2012, pp. 5026–5033. External Links: ISBN 978-1-4673-1737-5 Cited by: §5.
- Differentiable physics and stable modes for tool-use and manipulation planning – extended abstract. Note: Sister Conference Best Paper Track – Extended abstract of the R:SS'18 paper Cited by: §2.
- Tensor2Tensor for neural machine translation. CoRR abs/1803.07416. External Links: Link Cited by: Figure 7.
- Learning robotic manipulation through visual planning and acting. CoRR abs/1905.04411. External Links: 1905.04411 Cited by: §2.
- Embed to control: A locally linear latent dynamics model for control from raw images. CoRR abs/1506.07365. External Links: 1506.07365 Cited by: §2.
- Relative end-effector control using cartesian position based visual servoing. IEEE Transactions on Robotics and Automation 12 (5), pp. 684–696. External Links: Document, ISSN 1042-296X Cited by: §2.
- Improvisation through physical understanding: using novel objects as tools with visual foresight. CoRR abs/1904.05538. External Links: 1904.05538 Cited by: §2.
- Neural task programming: learning to generalize across hierarchical tasks. CoRR abs/1710.01813. External Links: 1710.01813 Cited by: §2.
- Active, uncalibrated visual servoing. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, Vol. , pp. 156–161 vol.1. External Links: Document, ISSN Cited by: §2.
- Unsupervised visuomotor control through distributional planning networks. CoRR abs/1902.05542. External Links: 1902.05542 Cited by: §3.
- Learning synergies between pushing and grasping with self-supervised deep reinforcement learning. CoRR abs/1803.09956. External Links: 1803.09956 Cited by: §2.
Appendix A Method Details
a.1 Visual MPC
In HVF, evaluating the cost of potential subgoals, as well as actually taking actions given computed subgoals uses visual MPC. One step of visual MPC is described in Algorithm 1. Given the current state and goal state, MPC samples action trajectories of length , then feeds them through the model . Then the cost of the output images are computed relative to the goal image, which is then used to sort the actions and refit the action distribution. After 5 iterations or convergence the best action is returned.
a.2 Planning with Subgoals
In the main text we describe how given, and , HVF produces subgoal images . Given these subgoals, planning with them is executed as follows. For an episode starting at of maximum length , the agent plans using visual MPC from to until some cost threshold or for some maximum number of steps . Once this criteria is reached, the agent plans from its current state to the next subgoal , again until the cost threshold or some maximum number of steps , and so until the agent is planning form to the true goal , at which point it simply plans until the environment returns that the task has been a success or the total horizon runs out.
a.3 Generative Model
The generative model we use is either a Variational Autoencoder or a Conditional Variational Autoencoder .
In the VAE, training is done by sampling images from the dataset, and each image is encoded using an image encoder into the mean and standard deviation of a normal distribution of dimension 8. Then a sample from the distribution are decoded back to the original input image. This is trained with a maximum likelihood loss as well as a KL penalty on the normal distribution restricting it to be a unit gaussian .
In the conditional VAE, training is done by sampling pairs of images from the dataset, specifically images from random episodes as well as the corresponding first image of that episode . Then both and are encoded using the same image encoder, which are then flattened and concatenated. This is then encoded into the mean and standard deviation of a normal distribution of dimension 8. A sample from the resulting distribution is then fed through fully connected layers and reshaped into the output shape of the encoder, concatenated with the spatial embedding of the conditioned image from the encoder and then decoded. This is done to enable easier conditioning on the scene in the decoding. The resulting image is trained with a maximum likelihood loss as well as a KL penalty on the normal distribution restricting it to be a unit gaussian .
In the Simulated Maze Navigation experiments, we use a Conditional VAE which encodes the 64x64 RGB image with 4 convolutional layers ([16, 32, 64, 128] filters, kernel size 3x3, stride 2), as well as 3 fully connect layers of size 256 for the generated image and 3 fully connected layers of size [512, 512, 256] for the conditioned image, and a decoder with the same architecture as the encoder inverted. In the Simulated Desk Manipulation experiments, we use a VAE which encodes the 64x64 RGB image with 7 convolutional layers ([8, 16, 32, 32, 32, 64, 128] filters, kernel size 3, stride alternating between 1 and 2), as well as a single fully connected layer of size 512 before mapping the latent distribution, which is decoded with the inverted encoder architecture. In the Real Robot Manipulation experiments we use a Conditional VAE which encodes the 48x64 RGB image with 9 convolutional layers ([8, 16, 32, 64, 64, 128, 128, 256, 512] filters, with kernel size 3, and stride sizes [1,1,1,1,1,1,2,2,2]), as well as 3 fully connect layers of size 256 for the generated image and 3 fully connected layers of size [512, 512, 256] for the conditioned image, and a decoder which is the inverted architecture of the encoder. The three experiments use Adam optimizer with learning rate 1e-4, 1e-3, 1e-4 respectively.
a.4 Dynamics Model
The dynamics model is an action conditioned video prediction model, stochastic variational video prediction (SV2P) (Babaeizadeh et al., 2017). See architecture below:
For all experimental settings the dynamics models are trained for approximately 300K iterations.
Appendix B Experiment Details
b.1 Maze Navigation
Data Collection: For the maze navigation environment data is collected through random policy interaction. That is, for each action we sample uniformly in the delta action space of the green block. We collect 10000 episodes, each with random initialization of the walls, block, and goal. Each episode contains 100 transitions. The images are of size 64x64x3.
Cost Function: In this experiment, the cost function is simply the squared pixel distance between the images, that is
Planning Parameters: When planning in the maze navigation environment, MPC samples action trajectories of length . Additionally, the cost of a sequence of 5 actions is the cost of the last of the subsequent frames, where the cost of each frame is the temporal cost described above. Lastly, we use and in these experiments.
b.2 Simulated Desk Manipulation
Task Details: Door: The first task is reaching to and closing the sliding door. From the initialization position of the arm, it needs to reach around the door handle into the correct position, then slide the door shut. The position of the door and distractor blocks on the table are randomized each episode. The agent is given 50 timesteps to complete the task. 2 Block: The second task requires the agent to push two different blocks off the table, one located on the left end of the table and one located in the middle. It requires pushing first one block, then re positioning to push the other block off the table. The agent is given 50 timesteps to complete the task. Door + Block: The agent must both slide the door closed from a random position, as well as push a block off of the table. This requires both grasping and sliding the door from the previous task, as well as positioning the end effector behind the block to slide it off the table. The agent is given 100 timesteps to complete the task. Door + 2 Block: A combination of the 2 Block and Door + Block task. The agent must close the door and slide both blocks off the table within 100 timesteps.
Data Collection: For the simulated desk manipulation environment data is again collected through random policy interaction. That is, for each action we sample uniformly in the delta action space of the robot end effector. We collect 10000 episodes, each with random initialization of the desk door/drawer and blocks. Each episode contains 100 transitions. The images are of size 64x64x3.
Cost Function: In this experiment, the cost function is simply the squared pixel distance between the images, that is
Planning Parameters: When planning in the desk manipulation environment MPC samples action trajectories of length , which actually consists of 5 actions, each repeated 3 times. Additionally, the cost of a sequence of 15 actions is the pixel cost of the last frame only. Note this is distinct from the maze environment. Lastly, we use or and in these experiments, depending on the task.
b.3 Real Robot Manipulation
Data: We use the BAIR robot manipulation dataset from (Ebert et al., 2018a), which consists of roughly 15K trajectories split into a train/test split. We train and on the train set and display qualitative results on the test set.
Cost Function: Like the desk manipulation set up, the cost function is the squared pixel distance between the images, that is
Planning Parameters: When planning in the desk manipulation environment MPC samples action trajectories of length , which actually consists of 5 actions, each repeated 3 times. Additionally, the cost of a sequence of 15 actions is the pixel cost of the last frame only.
Appendix C Additional Results
c.1 Additional Ablations
c.1.1 Planning Horizon
|5 Step Planning
|10 Step Planning
|15 Step Planning
|VAE Latent Cost
|Pixel Cost
In the maze environment, on the hard difficulty, we study how increasing the planning horizon of visual MPC impacts the benefit of using HVF (See Table 5). Interestingly, we find that for longer planning horizons, performance does not necessarily improve (as a longer planning horizon constitutes a harder search problem). This supports the idea that simply using longer planning horizon is not necessarily the solution to doing long-horizon tasks. Further, we find that even with longer planning horizons, HVF outperforms standard visual foresight, but that performance with 2 subgoals is worse than with 1 subgoal.
c.1.2 VAE Latent Space Cost
To address the sparsity of pixel cost, we explore using distance in the latent space of the VAE as a cost signal. However, we find that this cost actually performs worse across all difficulties. In Table 5 we show the success rate of standard visual foresight for either pixel cost or VAE latent space cost. We find that the reason for the poor performance of the latent space cost is that it provides close to zero cost anytime the green blocks are reasonably close together, leading to CEM often getting stuck close to the goal but not actually at the goal.


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Novartis Said to Draw Interest for Vaccines Business
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Novartis Said to Draw Interest for Vaccines Business
- Tuesday, February 4, 2014
Novartis AG (NOVN) is drawing interest from potential bidders for its human-vaccines unit as talks to swap its animal-health business for assets from Merck & Co. (MRK) slow down, people with knowledge of the matter said.
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6 Sectors Ripe for Business Consolidation in 2014
- Tuesday, February 4, 2014
Since hitting a low during the recession, merger-and-acquisition activity is picking up again as corporations look outside their walls to expand.
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How to Sell a Business as an Individual Seller
- Monday, February 3, 2014
Ok, so you want to sell your business. Would you rather sell it yourself, as opposed to contacting a Business Broker?
Selling a business
can be complex. Each business is different, as well as the markets they exist within. That said, if you choose to do it yourself, we are about to share the simple Steps on How to sell a Business by Individual Sellers.
To get started, we cannot sell a business without finding buyers. We cannot find buyers without marketing the business for sale. We cannot find buyers without determining a reasonable asking price. We cannot determine a proper valuation without getting a few pieces of financial data together. So, let's get started.
Determining Key Financial Numbers
We are going to determine the SDE (Seller's Discretionary Earnings). Depending on who you talk to, this number may be referred to by different titles. (e.g. True Free Cash Flow, Owner's Benefit).
To determine the SDE, we will need the following:
Pre-Tax Net Profit:
The Net Profit you report after all expenses.
Any Property or Asset which you have purchased and are depreciating, on an annual basis. The total annual reported depreciation will be needed here.
Amortization & Interest:
Amortization & Interest paid by the Business.
Owner Salary and Perks:
Owner Draws, Incomes or Other Perks not required as a regular Expense for the Business, itself. Also any draws or perks paid to spouse or other family. In some cases, an annual country club membership or cell phone may be examples of 'Other Perks'.
This is a straight forward and simplified method. More complex and detailed analysis can be performed by a Certified Business Appraisers. So, here it is. The formula is pretty simple. By applying your Key Financial Numbers this formula, you will get a good estimate of the SDE (Seller Discretionary Earnings).
Pre-Tax Net Profit + Depreciation + Amortization & Interest + Owner Salary and Perks
Pre-Tax Net Profit
Amortization & Interest
Owner Benefits & Perks
Selecting an Appropriate Multiple
This is a judgement call and may be viewed differently by others. Sometimes a business owner has high visions of his business and it's position, while another individual may see things a little more black and white. Try to be honest here and select a number beside the closest description of your business.
Select Multiple That Best Fits
1 - 2
New or Small Business. Personal Services Business. The Owner is the only or one of the only Service Providers in the Company.
3 - 4
Established Small Business. No significant advantages. Little hard assets. Stiff competition. Heavy dependency on proper management for profitability.
5 - 7
Established Small/Medium Business. Admirable market position. Mild competition. Swings in revenue. Requires continual good management.
8 - 10
Well Established Business. Solid market position. Revenue will reliably continue without regard of strong management.
As you can see, this is not an extremely deep view or determiner of a company's multiple. It does give a fairly close estimate, for those who are honest about their business. For a more specific multiple, you will need to know how the multiples run on various industries and business sectors. Often times a good Business Appraiser will work from your SIC code and select the best industry/business sector and it's multiples. In every case, it is a judgement call, so being honest to yourself or a Business Appraiser is key. Be aware that a potential buyer may also consider his/her view of your business situation and multiplier.
All Purpose, Simple, not Perfect but Good Business Valuation
SDE Total
= Potential Asking Price
How to List the Business
Now that you've determined a possible Asking Price, it's time to assemble a Business For Sale Listing and post your listing online. Listen, if you want to find buyers, then you need to get your business in front of as many as you can. Now, there you do need to consider being discrete, while reaching as many people as you can.
Discretion, Keeping it Quiet
Employees and existing customers get a little nervous when word hits the street of a business for sale. Sometimes the competition fuels the message in order to take a bit of your market-share. First, it is seldom a good idea to give your actual business name and address in a public Business For Sale Listing. Save that for your Potential Buyer Packet, which you do not let out until you've qualified the potential buyer and secured an NDA. Often a seller will list the county and state of a business for sale, holding the city and street address out for serious inquiries. That said, you can be very creative and informative, as to the business title, description and financial data. Also, it's a good idea to use a Contact Name, Phone and Email different from your own. Listings of Businesses for Sale often contain contact information or provide a form to reach you. Any personal identifiable information could be an issue. Often an account for selling a business will need your true name and address, but that should be completely separate from the Contact Name, Phone and Email associated with the listing, if you wish to remain Discrete!
Where do you go? Go to
Sell Your Business
Visit BizLocal.com and Click "
" or Sell as "Individual Seller" to begin.
Create a Seller Account with only a Name, Email and Phone.
Enter your Listing Details & Upload an Image
Select your Package and Checkout
While creating your listing, you will notice a couple key items. Select relevant Categories. Give it a strong title that attracts buyers. Give it a Sub-Title/Quick Sales Pitch. Enter as much detail as you can. Keyword suggestions are in a box, but you can add more keywords that relate to your business or product. Keywords are important as they aid internal and external search engines. Enter your financial information and other questions, including Reasons for Selling.
After completion, you will notice a Back Office link in the upper right header, giving you access to an easy Back Office suite to manage your listing. You can manage your Account, Listings and Contacts. An important feature is the ability to edit your listing, at any time. You can even put it into Draft Mode, moving it from public access, if you feel you need to put more attention in the detail of it. Also, each time your listing Card is viewed, we track it. When it is clicked on... well that is tracked as well. If the visitor is logged in or has an account with us, their email/username will be seen in your analytics. The system also tracks referring url's. This enables you to determine where your visitors are coming from. In some cases, they may have found your business for sale from Facebook, Twitter, Google+, or a Blog.
Do some Self Promotion
That's right! Now that your listing is live, you should promote it a bit. From BizLocal's BackOffice, you can select your listing and push to Facebook, Twitter, Google+ or Pinterest. Visit the public side of BizLocal.com and go to
Buy a Business
to find your business. View it and copy the link from your browser. You will notice a 'Search Engine Friendly' url. Now drop that in the signature live of your email account and include in various email correspondences, or simply use it in the body of an email to someone you would like to share it with.
Happy Selling!
DISCLAIMER: The author does not provide legal, taxation or investment advise. The author provides business services, in areas of marketing, advertising, web-presence and business consulting.
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Selling a Business in Florida
- Monday, February 3, 2014
Wow! Businesses for Sale in Florida can be exciting! Yes, the economy suffered, housing crashed, but a lot of businesses thrived. Why? Lower overhead and extremely pro-business State Taxes!
So, if you want to Sell a Business in Florida and desiring top-dollar from the buyer of your business, read on! Yes, you've probably seen some tough times. In fact, your mind may only think about the hardships. That said, you have some GREAT things to offer potential buyers, above and beyond your business!
Owners seeking to sell a business in Florida should take time to highlight some of these pro-business facts, along with the details of their business for sale.
Limited Corporate Taxes
Reasonable Consumer Sales Tax
No Personal Income Tax
Road and highway infrastructure makes it easy to travel
The state puts a lot of energy in attracting tourism
Housing is extremely affordable
Awesome night life for an Owner to relax
Great Dining experiences from Sidewalk to Fine Dining
...and... Who doesn't love the beach?!
Of course, your business should be marketable. If you are selling a business, make sure you've prepared your financial documents for the past 3 years, assembled some history on the business, it's founding and the strong points it enjoys in the market. Buyers love it when the current owner expresses things they would have done differently. This is due to your intimate knowledge about your business, it's weaknesses and market potentials.
You will need a reliable valuation for the business for sale, so buyers are confident in the asking price you are trying to sell the business for. This is a point when a Broker, Consultant or Business Appraiser will come in handy.
Once complete, take some great photos and visit
to build your first advertisement of the business for sale.
Buy A Business
Sell A Business
Get Help! Call 1-800-785-2925
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These NEW art greeting cards are the perfect way to say thank you while also appreciating the local beauty of Victoria. This art card includes a legend of the doors on the back. How many do you recognize?
I am happy to mix and match art cards! Maybe you want 3 of this card and 2 of another. For the 5 and 10 pack cards, please let me know in the order notes which cards you would like swapped, and how many.
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CBN has got enough chances, let's elect Jagan: Rajasekhar
Dr. Rajasekhar has on Tuesday said that he initially didn't trust Jaganmohan Reddy but, over the years, has had a change of mind. The 'Kalki' actor had recently joined YCP with his wife Jeevitha.
"I did not believe in Jagan, I misunderstood him but when I met him, after all these years and saw the honest change in him.. I began to believe, I believe that he will be the leader Andhra deserves now. I stand by Jagan and I ask you all to give him one chance.
We have given Chandrababu Naidu garu enough chances, let's give Jagan a chance for all the work he's put in these 10 years, for his passion. Please vote for YSRCP. GIVE HIM A CHANCE. I say this with all honesty, not for my family or my own benefits or some big package," the senior actor said.
The 'PSV Garuda Vega' actor added that the "allegations" against his family are totally untrue. "There are many rumours and allegations about my family. I have already answered them all and I believe that time will explain things much better. To conclude it all in short, everything terrible and disrespectful being said about us is absolute bull*hit," he said.


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But it wasn't a heartattack though, if you go by reports, it was cardiac arrest which is not the same thing. I saw an interview with a doctor about the difference. I don't know if that changes any of your theories just thought I'd add that
The bit that gets me is, in the first one she writes"Jeff said they used everything in their paramedic kits and worked on him for 45 minutes taking turns doing chest compressions. He's pretty sure he was gone before they got there."How could he have been pretty sure he was gone before they got there? Also this is now in the present tense (he's), so this is now after the fact, in which case why would he and the crew worked on him for 45 minutes. Besides, we know that he wasn't "gone" because his death certificate would have indicated this. Also she makes it seem as if he was the chief paramedic - Jeff's ambulance, he was fielding calls, Jeff's crew, if this is the case and all these agency's kept phoning his fire station, they would obviously be able to find a list of personnel at the station (we know what the media are like), why hasn't his name been thrown all over the place?? HEADLINE "the man who tried to save Michael Jackson!". "They all had cellphones with cameras, but not one took a photo" - Firstly how would she know this, and secondly, no-one took a photo in the house, in the grounds, of the comotion outside or inside the hospital, which seems strange since they were there for 5 hours. But the only photo that came about was taken inside the ambulance of the computer screen which shows details of the emergency, so obviously one of them was out on a mission to take photos, so why didn't he take more, was there anything inside the house worth taking a picture of? He also managed to send her a text message 12 minutes after Michael was pronounced dead and 6 Minutes BEFORE TMZ officially broke the news! Also at 2:35pm it was reported that a Fire Official told the LA Times Michael Jackson was in the hospital and this was reported by AP, but this is 9 minutes after he was pronounced, and also 3 minutes before Mrs. Jeff got her text, so I don't really understand this situation. (Besides I didn't think you were supposed to use cellphones in a hospital, especially if you are a paramedic? :lol: )In the second one, how could you laugh off an accusation and make fun about selling "the last photo"??!! "Jeff and the guys laughed off the accusation... the first one who turns up with a new boat will be the prime suspect" - Yeah thats real funny... HOWEVER, she then writes - "...how frustrating it must be for celebrities to read all the lies printed about them in tabloids" Is she now saying she thinks its fake??!Also there's something about people not mentioning Michaels name in all of this, the 911 call, Jeff's wife, and even Los Angeles Fire Department spokesman Devin Gales would not confirm Michael's identity but said paramedics went to an address corresponding to the star's home at 12:21 pm and the person was taken to UCLA Medical Center (Reported by AFP)


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WILD Summit II nears sell-out for April 4 event, Rally Software among 32 underwriting organizations
Monday March 31, 2014 0 commentsBOULDER - WILD Summit II, a women's leadership conference set for April 4 in Westminster, is rapidly selling out, organizers said.
The event is a one-of-a-kind venue for bringing together women across all industries, career levels and interests to share, network and learn from each other.
The event is hosted by The Women's Council at CU-Boulder's Leeds School of Business.
Organizers also announced Rally Software is among 32 organizations who are underwriting this year's summit.
"We are proud to support this wonderful event," said Angela Tucci, Rally Software's CMO. "The WILD event truly provides attendees with a one-of-a-kind venue for women in leadership. It offers a unique setting that fosters growth, development and learning for women of all ages and backgrounds."
More than 500 Front Range leaders - from students through highly successful and nationally recognized professionals - are expected to attend this year's conference to discuss trends driving tomorrow's business opportunities and how women are influencing change.
Sheila Paxton, founder and president of International Relationships, will serve as closing motivational speaker. Paxton's company helps people develop authentic and purposeful relationships in all facets of their lives.
WILD Summit II will be held from 7:30 a.m. to 2 p.m. at the Westin Hostel in Westminster with a networking hour to follow.
To register or for more information, visit http://leedswomenscouncil.org/wild-ii.html.