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In today's increasingly mobile business world, there is always a risk of losing important data to malware attacks, accidents or end-user negligence. With advanced technology, however, businesses can minimize their risk of data loss by identifying and monitoring data and putting in place certain safeguards to help protect it from loss.
Your data are the actual lifeblood of your company. Most businesses require storage of sales, customer information, company proprietary information, pricing and patents, plans, personnel, etc. Not only is this data confidential, but it's constantly growing and changing and it is often being accessed, altered and stored in several different and diverse places. In this mobile-driven IT world, the problem of keeping up with what data are being accessed, where it is being stored and what is being done with it has increased exponentially and has created quite a high risk of misuse and loss.
Businesses that fail to protect their data from theft, leakage or other disclosures have the potential of exposing the enterprise to dangerous and expensive consequences.
One of the challenges in trying to assess and mitigate the risks of data loss is that the threats are just about everywhere, with the increased number of devices attached to the corporate network. Not only are there multiple points of entry, but also the way in which businesses and individuals use technology can create even more threats, with such innovations as cloud computing and social networking. Today's IT professionals must address an increased and ever-changing amount of vulnerabilities to their corporate data.
Therefore, in assessing the problem of data loss, it is necessary that IT measure the amount of damage those threats could be to the overall organization. IT professionals must not only understand the threats but also the potential impact in order to plan on countermeasures to data loss. Not only do IT professionals need to understand the vulnerabilities but also those who have access to the data must be educated on the vulnerabilities as well.
Any Data Loss Prevention strategy needs to start with a risk assessment. This step is necessary to create awareness at the executive level, in order to complete a successful project and alter any processes that may put data at risk. What this, basically, means is that IT must first determine what data are most critical to the business, i.e. what data the business can least afford to lose, and then assess where the data are being stored and how it moves.
This assessment is usually best handled through a third-party provider, in order to uncover all possible data leaks and possible methods that data may be exported and downloaded, uploaded and otherwise moved through a corporate environment. A third party will help your organization understand exactly where the most important data are and where they are going, and will help to give executives the necessary information to understand and manage the risks that might be inherent in their current data structure. At this point, business leaders should be ready to undertake a serious Data Loss Prevention strategy.
The hardest part of putting together the Data Loss Prevention (or DLP) strategy is the assessment of where the data can be compromised and which data are most important. Once you have completed this step, the next step is setting up a strategy to keep this data safe and secure. Several different tools are available that will help in this step. Of course, a comprehensive policy is essential, but this alone is not going to ensure your data are safe from loss. This is where DLP software comes into play. There are many third party solutions that you can use to help in keeping your data safe and secure. If you know what data are crucial and you have identified the areas where this data can possibly be breached, you can easily set up the software to monitor this data and prevent the data from being lost.
First and foremost, the software needs to automatically backup your data so that in the case of a disaster, you can easily access the data with little to no loss. A good backup strategy is to automatically backup all crucial data as they are accessed and changed. In this manner, even if all of the important data are wiped out, you can simply pull the data from the backup. Most DLP software solutions will provide the necessary tools to keep your data backed up, either to the cloud or to a dedicated backup server. Some offer redundant backups, to add an extra layer of security.
Another function of a good DLP software solution is to monitor the crucial data, so that they can provide you with an overview of not only what data are being accessed, but who is accessing them, where they are being sent and how they are being used. This is very important as you can find and eliminate any security breaches early on, to eliminate unauthorized transfers.
By having the right tools in place, the right policies in place, and most importantly by knowing exactly what the most crucial data are and where they reside, you are enabling yourself to stay a step ahead of your most important data. This will give you the peace of mind to know that no matter what happens, you will be prepared for any contingency and can handle any type of data loss in the future, regardless of whether it is from accidental deletion or from a security breach or malware attack. In any contingency. The best Data Loss Prevention strategy requires safeguarding your data, monitoring your data and keeping your data backed up.


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Getting there: Head west into Fredericksburg on Highway 290. In the middle of town turn right onto RR 965 and head north. After traveling approximately 18 miles look for the entrance to Enchanted Rock State Natural Area on the left.
The Park: All ages are relative. A single human's age measures up to a fraction of a sliver of the Earth's. The collective written history of Texas adds up to only a handful of generations. To measure truly significant periods of time one has to do so geologically. At 1 billion years old, Enchanted Rock provides one of the most striking examples of old to be found in Texas.
Enchanted Rock State Natural Area consists of numerous peaks, each of them a separate highpoint of the granite bubble, most of which continues to reside beneath the surface of the Earth. It's estimated that the granite bubble encompasses some 90 square miles, mostly to the north of Enchanted Rock.
When viewing Enchanted Rock from a distance, it's easy to draw a conclusion that the large masses of bare rock provide little in the way of resources for animal and plant life. However, those conclusions bely the wide abundance of life on and around Enchanted Rock, sometimes in the most unlikely of places.
Summit Trail: The path to the summit of Enchanted Rock is the most popular trail in Enchanted Rock State Natural Area and one of the more well known hikes in the state. The trail starts at the northernmost parking area, and heads north. A restroom and water fountains are available nearby. Concrete steps make crossing Sandy Creek easy as the path leads up to the base of Enchanted Rock.
As the path begins to ascend the rock it picks its way around a boulder field. The rocks here started life farther up the mountain. In a process of exfoliation these layers of rock that used to form the surface of the dome broke apart and slid down the new smooth underlying layer of rock and ended up here. Keep an eye open for trail markers uphill as sometimes it is difficult to differentiate the official path from other routes.
The trail on the dome is occasionally steep and can be slippery when wet. It also makes for a great stair master workout replacement. Once you've reached the smooth dome surface trail signs become moot. Just head uphill in whichever direction you prefer. A short ridge of rock provides the only obstacle. Just pick which way you'd rather go around it.
The summit is not a peak, but merely the highest point of the dome of rock. From the top the mountain slopes off gently in all directions, offering 360 degree views of the surrounding terrain. The winds can be gusty at the top and it certainly was when I visited. Even with a chin strap I ended up stowing my hat in my backpack to keep it from flapping about and being ripped from my head.
The trail backtracks to the trailhead to complete the journey. Though solitude is not a feature of the Summit Trail it does provide a nice climbing workout and great views from the top of the mountain. No wonder it's one of the best known hikes in Texas.
Our hike began at the southernmost parking area in the park. The trail here, as through most of the hike, consists of crushed granite and is quite wide. Part of the trail follows old jeep trails from the land's ranching days and the wider stretches of the trail are occasionally patrolled by rangers in a truck.
Many of the rocks found along the trail resemble mushrooms, with wide tops. The shape of these rocks is due to the pattern of erosion, typically freeze-thaw weathering. At the waypoint "Window" one can see a different result of erosion. A rock here has a hole bored straight through it.
There are a couple of side trails encountered on the hike, many of which are not official. One official trail veers off of the mail loop opposite two huge boulders. The short spur heads north to the waypoint "Overlook", which normally provides a panoramic view of the area. Due to the overcast day of our visit there was not much to see, however.
With Freshman Mountain and Buzzard's Roost rising before us we saw what from a distance appeared to be trash on the trail. What resembled shredded newspaper from afar was actually an explosion of quartz. From a little mound above the trail, a vein of quartz had weathered away and broken into pieces that littered the path with nuggets of all sizes and scattered them downhill and into a creek bed.
After passing the group shelter we happened upon a White Tailed Deer crossing the creek to put some distance between herself and us. Only after watching the deer disappear over a ridge did we turn around and notice three more deer, just a short distance behind us. Once they saw our attention focused on them they also vacated the area.
The Loop Trail officially crosses the creek again before intersecting with the Summit Trail and running into the northernmost parking area in the park. We missed that crossing though, perhaps due in large part to the higher waters in the creek. Instead, we continued to follow a marked trail on the eastern side of the creek until it ran into the opposite side of the long the parking lot from our trailhead. Not counting the traverse across the parking lot at the end of the day we covered 4.5 miles in a slightly modified loop around the mountains.
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got a bit hot today, but, i did up and around
5-year old grandaughter's first trip to the top! Been a while for me and I felt it afterwards! Son and I enjoyed taking the hike with her on a gorgeous winter's day!
I did a circular walk around the eastern part of the park, including an"off track" descent down the back of the dome. A fun few hours here; after haf a day I felt I'd seen enough!https://hikingtheworld.blog/2016/10/22/enchanted-rock/
exhausted, but hat a great day
I did this as a birthday hike over the Christmas holidays on a weekday. We arrived around 10:30/11 and there was a line of cars out the park and a mile or more down the road. We were told the park was full and no new guests were being accepted, try coming back after 1.
So we went and noodled around Frederickburg and returned a little before 1. Again we waited in a huge line, but the cutoff for who got in was about 6 cars behind us. Phew. Moral of the story: visit E-Rock on a weekday when there are no holidays in sight, and get there early.
When we finally got in, we only had the mojo for the single hike up to the top, but it was beautiful, and we had a terrific time spotting weird plants and looking at the tiny fairy shrimp in the vernal pools. There were tons of people, but it was worth it. The last section of the hike looks steeper than it is--if heights give you the collywobbles, just focus on your feet and don't look around too much till you reach the summit and you'll be fine.
I love this place. I wandered around all day, first climbing Enchanted Rock itself and finding the cave on the backside, then I went and climbed Turkey Peak (I chose the harder but shorter route where I had to climb up rocks-- at the top I learned from others there that there is also a longer path that is easier), then I climbed all around and on Freshman Mountain.
Summit Trail and Enchanted Rock proper is the busiest area by far, but there were other places I explored where I saw no other people.
This place is great because there are easy trails that still have nice views if you ust want to wander around leisurely but there are also tougher trials or you can try climbing on rocks if you wnat a challenge. It is a place I keep returning to (I go at least once a year).
My friend and I went to Enchanted Rock for some more elevation training. We linked up with a Houston hiking group and went peak bagging. This is one park that'll give you a good cardio workout.
The weather was very windy with near-gale strength gusts, which made activities a little more treacherous, but everyone had fun and no one got hurt.
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subset: commoncrawl


text: Yotkom Uganda is registered in Australia with the Australian Charities and Non for Profits Commission. Yotkom is an acholi word meaning "health". This web site is designed to provide information on the health needs of the acholi community of Kitgum, Northern Uganda. It is also an opportunity to share about the people working as health professionals there and the projects which are being undertaken in the Kitgum community in an effort to improve the quality and availability of health care .


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71% of African CEOs say they are concerned about cyber threats |
Africa's CEOs are confident that the outlook for business on the continent remains positive notwithstanding the unpredictable economic and socio-political climate. PwC's (www.PwC.com) Africa Business Agenda report shows that 85% of African CEOs (Global: 85%) are confident in their own company's prospects for revenue growth over the next 12 months. Despite the fact that only 30% of CEOs in Africa (Global: 29%) believe the global economy will improve in the next year, no less than 97% (Global: 91%) are confident about the prospects for their own company's growth in the medium term. Hein Boegman, CEO for PwC Africa, says: "This level of optimism is the highest recorded since we started our research on Africa CEOs in 2012. However, in the past year we have seen a change in the outlook for some countries as external developments impact many of the drivers of Africa's growth. "As countries around the globe try to make sense of the increased levels of risk and uncertainty that have gripped the world, Africa needs to continue rising by capitalising on all the opportunities that lie ahead." The report suggests that one of the reasons for such optimism on the Africa continent is that CEOs have learned to look for the upside and seize on opportunities that may arise in the face of uncertainty. In the wake of climate of muted growth, CEOs have also acknowledged that while they focus on organic growth and cost reductions, they also need to prioritise investment in new strategic alliances and joint ventures to expand their markets and grow their customer bases. According to the survey, organic growth (Africa: 80%; Global: 79%) and new alliances (Africa: 69%; Global: 48%) are the top activities CEOs are planning in order to drive corporate growth or profitability. The Agenda compiles results from 80 interviews with CEOs across 11 countries in Africa and includes insights from business. The results are benchmarked against the findings of PwC's 20th Annual Global CEO survey of 1 379 CEOs in 79 countries conducted during the 4th quarter of 2016. The Agenda provides an in-depth analysis and insights into how businesses are adopting to meet the challenges of operating in Africa. Notwithstanding the current climate and challenges, it is notable that there remains a significant amount of potential to unlock more growth on the continent. African CEOs are looking to international markets for opportunities, with the US (31%), China (28%) and the UK (24%) considered the top three countries for growth. Johannesburg (36%), Lagos (16%) and Cape Town (14%) are considered the top three African cities for growth opportunities. Main risks to doing business in Africa Although the returns for doing business on the continent can be high, so too can the risks. Africa's CEOs are working in difficult times – finding the right talent for their business, dealing with hurdles that come with working with governments, and managing expansion plans across the continent. In addition, infrastructure remains a challenge as it lags well behind that of the rest of the world. More than two-thirds of African CEOs (69%) are concerned about inadequate basic infrastructure (Global: 54%) and a stronger focus on expanding power supply is required to solve one of the biggest challenges in the business environment. Other clouds on the business horizon include exchange rate volatility (Africa: 90%; Global: 70%); social instability (Africa: 85%; Global: 68%); geopolitical instability (Africa: 79%; Global: 74%); unemployment (Africa: 79%; Global: 45%); and climate change and environmental damage (Africa: 64%; Global: 50%). For most of these factors, the level of concern among African CEOs is higher than the global average. In addition, over-regulation features on the list of concerns this year, with almost half (46%) (Global: 42%) of African CEOs saying they are "extremely concerned". CEOs also believe social instability resulting from inequality, an increasing tax burden, a lack of economic diversity with an overdependence on natural resources, and corruption remain problems in many countries. Globalisation Overall, globalisation has benefitted connectivity, trade and mobility. However, just over half of African business leaders say globalisation has done nothing to promote equality, in particular in closing the gap between rich and poor – in fact, this gap may well be widening. A number of CEOs think it is vital to address social challenges. CEOs believe the corporate community can assist in spreading the benefits of globalisation more widely. The majority say the best way is to collaborate, particularly with government. "While Africa's potential is undoubted, its achievement remains in question. Business, government and civil society will need to work harder to turn potential into tangible gains against the backdrop of a rapidly changing world," Dion Shango, CEO of PwC Southern Africa adds. Talent and technology The forces of globalisation and technology are increasingly transforming the workplace. Over half of African CEOs (53%) are exploring the benefits of humans and machines working together in the workplace. Over a third of African CEOs (36%) are considering the impact of artificial intelligence on future skills needs. In some sectors, automation has already replaced some jobs entirely. "As automation takes deeper root in the workplace, companies in Africa will have to increasingly focus on achieving the right cognitive re-apportionment between man and machine," Shango adds. However, as CEOs develop their services, they are finding that human interaction in the workplace is still important and place the investment in talent as a top business priority. Just over half of African CEOs (51%) plan to increase their headcount in the next 12 months. Conversely, 23% plan to cut their company's headcount over the coming year, with more than two-thirds of expected reductions being attributed to automation and other technologies. According to the survey results, no less than 80% of African CEOs (Global: 77%) see the availability of key skills as the biggest threat to growth (ahead of volatile energy costs and cyber threats). They are finding it particularly difficult to source soft skills – adaptability, problem solving, creativity and leadership. Technology & trust Technology has brought about a number of advancements in efficiency and the ease of doing business in Africa. No less than 91% of African respondents (Global: 90%) believe technology has changed competition in their industry in the past five years. While the digital era offers a host of opportunities, it also creates significant challenges and constraints in the arena of privacy and security. Organisations are holding increasingly large volumes of personal data about their customers, suppliers and employees. According to the survey results, 71% of African CEOs (Global: 61%) say they are concerned about cyber threats. Furthermore, the vast majority of African CEOs (93%) (Global: 91%) believe that cybersecurity breaches affecting personal information or critical systems will negatively impact stakeholder trust levels in their organisations in the next five years. A high 96% of business leaders are also concerned that IT outages and disruptions could impair trust in their respective industries over the next five years. As disruptions gain more speed, the ability to ensure trust, security and privacy across all interactions will become critical to businesses' competitiveness. But almost two-thirds of African CEOs (61%) (Global: 59%) are concerned that they are not prepared to respond to a crisis in their business, should one arise. "In the face of economic and socio-political uncertainty, we remain confident that the outlook for business in Africa remains positive. But to succeed, businesses need to adapt swiftly to change," Shango concludes. |


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Welcome to Coggins and Sons, Inc.
Since 1959 Coggins and Sons has provided dependable, quality services in the foundation support industry throughout the entire Rocky Mountain Region and beyond. Relying upon basic business ideals such as quality, honesty, integrity and excellent service, we have established a reputation second to none. Coggins works with the very largest of contractors and developers in the country while catering to smaller clients with equal care and dedication. Coggins and Sons is a design/build contractor with in-house engineering services to design virtually all types of foundation support elements and earth retention systems. We assist our clients with design concepts, budgeting and scheduling to ensure projects run smoothly from start to finish. With many combined years of experience, we provide ideas and engineering alternatives that save time, capital and energy. Our professional team is buttressed by a modern and meticulously maintained fleet of specialized equipment, one of the largest in the Rocky Mountain region. We are very proud of our long track record of success. Our durable and long-term client relationships are testimony to the integrity and professionalism with which Coggins and Sons has been serving our region for over a half century. |


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Sebenza means "work" in Zulu and these knives are intended to do just that. Built with handcrafted care at a production level, the Sebenza 21 has earned a worldwide reputation for rock solid performance and a "bank vault" feel. Offered with a CPM S35VN hollow-ground blade at a 59-60 RC, the Sebenza 21's blade will retain a sharp edge with easy maintenance. The Sebenza 21 uses the Reeve Integral Lock, which is a lock bar integral to the back handle. Pre-bending at the end of the slot causes the spring to move to the base of the blade, thereby locking the blade open. All models are available with a dedicated left or right-handed orientation in a Large or Small size.
Free Shipping
on all orders over $100


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Search Off the Record takes you behind the scenes of Google Search and its inner workings! In each episode, the folks from the Search Relations team will give you background info on the decision-making behind launches, feature prioritization in Search Console, and the projects Google Search teams are working on. They will share fun stories from the many conferences they attend as well as from their day-to-day working life at Google. They will also dive into the currently trending conversations in the SEO community at large. Have a listen!
May 5, 2022
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This seems fairly interesting:
Language and archeology: some methodological problems.
1. Indo-European and Altaic landscapes
Anna Dybo
The article is the first part of a larger work that represents an attempt to systematize our
ideas on the natural environment and material culture of the Proto-Indo-Europeans. It is
based on a more or less complete selection of reconstructed words from the appropriate semantic areas and on their comparison with a similar selection performed for a protolanguage of similar time depth, whose speakers evidently inhabited a territory that was not
in contact with the Proto-Indo-European one — Proto-Altaic. In this part, only the words that
belong to the semantic field of landscape terms are analyzed. The main conclusion is that thehypothesis of a steppe environment is more applicable for the Proto-Altaic population,whereas for Proto-Indo-Europeans a mountainous region seems more appropriate. As for
the water bodies, for Proto-Indo-Europeans we should suppose the existence of a sea (or of a
very big lake), and for speakers of Proto-Altaic, the existence of very big rivers with season
Mallory's article is also interesting as the latest public take on the PIE origins issue by the prominent champion of the Pontic-Caspian steppe hypothesis. It also alerted me to a study in Russian on the problem of Tocharian origins by Leonid Sverchkov. Sadly, I don't read Russian, but Mallory has a nice review of it in the Journal of Indo-European Studies, from which comes the following excerpt:
The second section of the book investigates CentralIt seems that the PIE urheimat debate is alive and well.
Asia as a cultural historical region. It briefly summarizes the
major Palaeolithic influences, then provides more detail
about the Mesolithic cultures of the region before settling
down to a much more thorough description of the
Neolithic cultures, among which much time is devoted to
the Kelteminar culture that occupied a broad area of
Central Asia and which many earlier authors saw as critical
in explaining the origins of many of the neighboring
cultures, among which would be included the Afanasievo
culture of the Altay and Minusinsk Basin. The author
continues laying out the cultural-historical development of
Central Asia up to the early Middle Ages.
The third part is titled 'Tokharians and the IndoEuropean problem" and the archaeological evidence seen
earlier is then recast to provide arguments for a Central
Asian homeland for the Indo-Europeans. One of the
perennial problems with searching out the origins of any
particular Indo-European group is that all too often
proponents of a particular theory provide an isolated
'solution' divorced from the fact that it is only part of a
larger puzzle and its pieces must make joins with the rest
of the Indo-European world. I have termed this the 'total
distribution principle' and it is one of the tests of how
serious we should deal with any partial solution to IndoEuropean expansions. In attempting to meet this
principle, one can hardly criticize the author as his final
section is essentially a very detailed proposal for a 'new'
Indo-European homeland in Central Asia. Geographically
situated not far from the earlier proposals of Gamkrelidze
and Ivanov, it does provide some legs to their general
positioning of the Indo-European homeland but in a novel
fashion. Sverchkov's solution also embraces a series of
earlier suggestions or models but is truly his own in terms
of its implementation.
Sverchkov's solution is fundamentally a rejection of
those who would normally dismiss Central Asia as merely a
transition zone across which migrating populations passed
through. During the transition between the Mesolithic
and Neolithic we find a vast Keltiminar culture occupying
the entire region from the Urals and Caspian east to the
Altay, and north of the Kopetdagh and northern
Afghanistan. This region matches at least in areal extent
the type of homelands anchored in Europe such as those
who have sought the Urheimat in the area of the
Linearbandkeramik. To these Sverchkov also includes the
southern agricultural regions of Jarmo and later Jeitun
which would appear to lie outside the area normally
ascribed to other non-Indo-European languages (Semitic,
Elamite, Dravidian, Altaic, Uralic). This entire region then
functions as a broad Indo-European homeland. He suggests
that the westward movement of the Halaf culture accounts
for the separation of the Anatolian branch. The ProtoTocharians begin within the Jeitun region and moved
eastwards to arrive in Ferghana by the Bronze Age. The
archaeological discussion emphasizes the presence of
painted wares in both the Tarim Basin, especially in the
region where we find Tocharian B, and Ferghana, and
these persistent contacts are seen as indicating the spread
of the Tokharian languages. This pattern of Central Asian
contacts is seen even in the earliest cemeteries of the
region (Xiaohe, Gumagou) which, although lacking
ceramics altogether, possessed abundant evidence for bagshaped baskets which have been compared to the shape
and decorations of Kelteminar vessels. It might be noted
that precisely the same pot-to-basket argument has been
employed by those who support a connection between the
Tarim Basin and the steppelands.
The other Indo-European languages are accounted
for by very early (Neolithic) movements from Central Asia
into the Pontic-Caspian region. The Ayderbol culture of
Kazakhstan, for example, is proposed as underlying the
formation of the Dnieper-Donets culture of the Ukraine
and as seen as the initial wave (roughly in the sense of
Marija Gimbutas) of the Italo-Celtic-Illyrians. The Neolithic
and Eneolithic developments of the Volga-Ural region are
under the Kelteminar aegis and yield the later GermanicBalto-Slavic branches. Out of the steppe cultures (Sredny
Stog and Khvalynsk) and the neighboring Maykop culture
he derives the Yamnaya which in the guise of the
Andronovo culture sets off the Aryanization of southern
Central Asia. Throughout this archaeological discussion the
author relates his theories to a variety of linguistic
proposals, e.g., Henning's famous argument tying the
names of cultures on the frontiers of Mesopotamia with
those of the Tarim Basin


text:
That's No Monkey Business.
What do you get when you cross a Roland DG SOLJET with a businessman passionate about printing wall decals? A $2-million-a-year business on the rise. That's the story of WallMonkeys CEO Jason Weisenthal, who transformed his home basement into a lean, mean, decal-printing machine in just under five years.
Read the full article on Yahoo! here. And learn more about the Roland DG SOLJET that helped make it all happen.


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Whipple Superchargers, known for years of innovation, have now developed the new Gen 3 Whipple 2.9L supercharger that features both updated rotor design and rotor housing to unleash even more power from your engine. The Gen 3 works with nearly all 2.9L Gen 1 and 2 applications and is a simple upper half swap. No tuning required, just more power by increased supercharger efficiency.
Gen 3 features all new 3/5 rotors that feature a new updated rotor profile and new advanced manufacturing methods, allowing more accurate and tighter tolerances along with increased efficiency. Each Gen 3 rotor also features a later generation robotically sprayed conformal rotor coating, allowing the rotor clearance to conform to the operating conditions and application. The Gen 3 also features upgraded double lip PTFE rotor seals for higher RPM, higher temp and higher pressure ratios.
The Gen 3 also features a new rotor housing with the new angled support ribs for a more aggressive appearance, along with revised port shape for increased rotor filling and decreased turbulence, resulting in increased overall supercharger efficiency. What does all this mean? More power, more power, more power! By decreasing leakage, discharge temperature and power consumption lower, resulting in increased power to the flywheel, all while maintaining the same boost levels.
During extensive testing on multiple applications, both Whipple and 3rd party testing found gains from 30RWHP up to 150RWHP depending on blower speed and engine specifications. Standard pump gas applications can easily make another 30RWHP with no other changes. Because boost maintains, tuning is not required. Just bolt on and unleash the power of your stock application.
Each swap kit comes with all the necessary parts/hardware to swap the Gen 2 SC for the Gen 3. No cutting or grinding required.
W175/2.9L GEN 3 Applications available for:


text:
The new Flag Building dedicated in November 2013 as the spiritual center of the Scientology religion is unique because the religion is unique—the culmination of thousands of years of Man's quest for spiritual freedom combined with 21st-century science and technology.
The Flag Building
A beacon shining brightly from downtown Clearwater, the Flag Building lights up the skyline and welcomes Scientologists from around the world.
A Turning Point for the Religion
When Scientology ecclesiastical leader Mr. David Miscavige announced Golden Age of Tech Phase II to more than 10,000 Scientologists, he heralded a new era of expansion for the religion.


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At the recent R/Finance 2014 conference in Chicago I gave a talk on Smooth Transition AR models and a new package for estimating them called twinkle. In this blog post I will provide a short outline of the models and an introduction to the package and its features.
Financial markets have a strong cyclical component linked to the business cycle, and may undergo temporary or permanent shifts related to both endogenous and exogenous shocks. As a result, the distribution of returns is likely to be different depending on which state of the world we are in. It is therefore advantageous to model such dynamics within a framework which can accommodate and explain these different states. Within time series econometrics, the switching between states has been based either on unobserved components, giving rise to the Markov Switching (MS) models introduced by Hamilton (1989), or observed components leading to Threshold type Autoregressive (TAR) models popularized by Tong (1980). This post and the accompanying package it introduces considers only observed component switching, though there are a number of good packages in R for Markov Switching.
The Smooth Transition AR Model
The smooth transition AR model was introduced in Teräsvirta (1994), and has been used in numerous studies from GDP modelling to forex and equity market volatility. See the presentation from the talk above which includes a number of selected references.
The s-state model considered in the twinkle package takes the following form:
{y_t} = \sum\limits_{i = 1}^s {\left[ {\left( {{{\phi '}_i}y_t^{\left( p \right)} + {{\xi '}_i}{x_t} + {{\psi '}_i}e_t^{\left( q \right)}} \right){F_i}\left( {{z_{t – d}};{\gamma _i},{\alpha _i},{c_i},{\beta _i}} \right)} \right]} + {\varepsilon _t}
y_t^{\left( p \right)} = {\left( {1,\tilde y_t^{\left( p \right)}} \right)^\prime },\quad \tilde y_t^{\left( p \right)} = {\left( {{y_{t – 1}}, \ldots ,\quad {y_{t – p}}} \right)^\prime },{\phi _i} = {\left( {{\phi _{i0}},{\phi _{i1}}, \ldots ,\quad {\phi _{ip}}} \right)^\prime } \\
\varepsilon _t^{\left( q \right)} = {\left( {{\varepsilon _{t – 1}}, \ldots ,\quad {\varepsilon _{t – q}}} \right)^\prime },{{\psi '}_i} = {\left( {{\psi _{i1}}, \ldots ,{\psi _{iq}}} \right)^\prime }\\
{x_t}{\text{ }} = {\left( {{x_1}, \ldots ,{x_l}} \right)^\prime },\quad {{\xi '}_i}{\text{ }} = {\left( {{\xi _{i1}}, \ldots ,{\xi _{il}}} \right)^\prime } \\
and we allow for a variance mixture so that \( {\varepsilon _t} \sim iid\left( {0,{\sigma _i},\eta } \right) \) with \( \eta \) denoting any remaining distributional parameters which are common across states. The softmax function is used to model multiple states such that:
{F_i}\left( {{z_{t – d}};{\gamma _i},{\alpha _i},{c_i},{\beta _i}} \right) = \frac{{{e^{{\pi _{i,t}}}}}}
{{1 + \sum\limits_{i = 1}^{s – 1} {{e^{{\pi _{i,t}}}}} }} \\
{F_s}\left( {{z_{t – d}};{\gamma _i},{\alpha _i},{c_i},{\beta _i}} \right) = \frac{1}
{{1 + \sum\limits_{i = 1}^{s – 1} {{e^{{\pi _{i,t}}}}} }}\\
where the state dynamics \( \pi_{i,t} \) also include the option of lag-1 autoregression:
{\pi _{i,t}} = {\gamma _i}\left( {{{\alpha '}_i}{z_{t – d}} – {c_i}} \right) + {{\beta '}_i}{\pi _{i,t – 1}},\quad \gamma_i>0
with initialization conditions given by:
\[\pi _{i,0} = \frac{{{\gamma _i}\left( {{{\alpha '}_i}\bar z – {c_i}} \right)}}{{1 – {\beta _i}}},\quad \left| \beta \right| < 1\]
The parameter \( \gamma_i \) is a scaling variable determining the smoothness of the transition between states, while \( c_i \) is the threshold intercept about which switching occurs.
The transition variable(s) \( z_t \) may be a vector of external regressors each with its own lag, or a combination of lags of \( y_t \) in which case the model is 'rudely', as one participant in the conference noted, called 'self-exciting'. Should \( z_t \) be a vector, then for identification purposes \( \alpha_1 \) is fixed to 1. Additionally, as will be seen later, it is also possible to pass a function \( f\left(.\right) \) which applies a custom transformation to the lagged values in the case of the self-exciting model. While it may appear at first that the same can be achieved by pre-transforming those values and passing the transformed variables in the switching equation, this leaves you dead in the water when it comes to s(w)imulation and n-ahead forecasting where the value of the transition variable depends on the lagged simulated value of that same variable.
Finally, the transition function \( F_i \) has usually been taken to be either the logistic or exponential transform functions. As the 2 figures below illustrate, the logistic nests the TAR model as \( \gamma_i\to \infty \) and collapses to the linear case as \( \gamma_i\to 0 \). The exponential on the other hand collapses to the linear case as \( \gamma_i \) approaches the limits and has a symmetric shape which is sometimes preferred for exchange rate modelling because of the perceived symmetric exchange rate adjustment behavior. Currently, only the logistic is considered in the twinkle package.
I follow a similar construction paradigm for the twinkle package as I have in my other packages. Namely, an S4 framework which includes steps for specifying the model, estimation, inference and tests, visual diagnostics, filtering, forecasting, simulation and rolling estimation/forecasting. I consider these to be the minimum methods for creating a complete modelling environment for econometric analysis. Additional methods are summarized in the following table:
Method | Description | Class | Example |
setfixed<- | Set fixed parameters | [1] | >setfixed(spec)<-list(s1.phi0=0) |
setstart<- | Set starting parameters | [1] | >setstart(spec)<-list(s1.phi0=0) |
setbounds<- | Set parameter bounds | [1] | >setbounds(spec)<-list(s1.phi0=c(0,1)) |
nonlinearTest | Luukkonen Test | [1][2] | >nonlinearTest(fit, robust=TRUE) |
modelmatrix | model matrix | [1][2] | >modelmatrix(fit, linear=FALSE) |
coef | coef vector | [2][3] | >coef(fit) |
fitted | conditional mean | [2][3][4][5][6] | >fitted(fit) |
residuals | residuals | >residuals(fit) | |
states | conditional state probabilities | [2][3][4][5][6] | >states(fit) |
likelihood | log likelihood | [2][3] | >likelihood(fit) |
infocriteria | normalized information criteria | [2][3][7] | >infocriteria(fit) |
vcov | parameter covariance matrix | [2] | >vcov(fit) |
convergence | solver convergence | [2][7] | >convergence(fit) |
score | numerical score matrix | [2] | >score(fit) |
sigma | conditional sigma | [2][3][4][5][6] | >sigma(fit) |
as.data.frame | conditional density | [7] | >as.data.frame(roll) |
quantile | conditional quantiles | [2][3][4][5][6][7] | >quantile(fit) |
pit | conditional probability integral transformation | [2][3][7] | >pit(fit) |
show | summary | [1][2][3][4][5][6][7] | >fit |
The classes in column 3 are: [1] STARspec, [2] STARfit, [3] STARfilter, [4] STARforecast, [5] STARsim, [6] STARpath, [7] rollSTAR.
Model Specification
The model specification function has a number of options which I will briefly discuss in the section.
args(starspec) # ## function (mean.model = list(states = 2, include.intercept = c(1, ## 1), arOrder = c(1, 1), maOrder = c(0, 0), matype = 'linear', ## statevar = c('y', 's'), s = NULL, ylags = 1, statear = FALSE, ## yfun = NULL, xreg = NULL, transform = 'logis'), variance.model = list(dynamic = FALSE, ## model = 'sGARCH', garchOrder = c(1, 1), submodel = NULL, ## vreg = NULL, variance.targeting = FALSE), distribution.model = 'norm', ## start.pars = list(), fixed.pars = list(), fixed.prob = NULL, ## ...) ##
Mean Equation
Upto 4 states may be modelled, with a choice of inclusion or exclusion of an intercept in each state (include.intercept), the number of AR (arOrder) and MA (maOrder) parameters per state and whether to include external regressors in each state (xreg should be a prelagged xts matrix). Note that the default for the moving average terms is to include them outside the states, but this can be changed by setting matype='state'. The statevar denotes what the state variable is, with "y" being the self-exciting model and "s" a set of external regressors passed as a prelagged xts matrix to s. If the choice is "y", the ylags should be a vector of the lags for the variable with a choice like c(1,3) denoting lag-1 and lag-3. Finally, the yfun option was discussed in the previous section and is a custom transformation function for y returning the same number of points as given.
Variance Equation
The variance can be either static (default) or dynamic (logical), in which case it can be one of 3 GARCH models ('sGARCH','gjrGARCH' or 'eGARCH') or 'mixture' as discussed previously. The rest of the options follow from those in the rugarch package in the case of GARCH type variance.
Other options
The same distributions as those in rugarch are implemented, and there is the option of passing fixed or starting parameters to the specification, although the methods 'setfixed< –' and 'setstart<-' allow this to be set once the specification has been formed. There is also a 'setbounds<-' method for setting parameter bounds which for the unconstrained solvers (the default to use with this type of model) means using a logistic bounding transformation. Finally, the fixed.probs option allows the user to pass a set of fixed state probabilities as an xts matrix in which case the model is effectively linear and may be estimated quite easily.
Parameter naming
It is probably useful to have the naming convention of the parameters used in the package should starting, fixed or bounds need to be set. These are summarized in the list below and generally follow the notation used in the representation of the model in the previous section:
- All state based variables are preceded by their state number (s1.,s2.,s3.,s4.)
- Conditional Mean Equation:
- intercept: phi0 (e.g. s1.phi0, s2.phi0)
- AR(p): phi1, …, phip (e.g. s1.phi1, s1.phi2, s2.phi1, s2.phi2)
- MA(q): psi1, …, psiq (e.g. s1.psi1, s1.psi2, s2.psi1, s2.psi2). Note that in the case of matype='linear', the states are not used.
- X(l): xi1, …, xil (e.g. s1.xi1, s2.xi2, x3.xi1)
- State Equation:
- scaling variable: gamma (e.g. s1.gamma)
- Threshold: c (e.g. s1.c)
- Threshold Variables (k): alpha2, …, alphak (e.g. s1.alpha2, s1.alpha3). Note that the first variable (alpha1) is constrained to be 1 for identification purposes so cannot be changed. This will always show up in the summary with NAs in the standard errors since it is not estimated.
- Threshold AR(1): beta (e.g. s1.beta)
- Variance Equation:
- sigma (s): If dynamic and mixture then s1.sigma, s2.sigma etc. If static then just sigma.
- GARCH parameters follow same naming as in the rugarch package
- Distribution:
- skew
- shape
I'll define a simple specification to use with this post and based on examples from the twinkle.tests folder in the src distribution (under the inst folder). This is based on a weekly realized measure of the Nasdaq 100 index for the period 02-01-1996 to 12-10-2001, and the specification is for a simple lag-1 self-exciting model with AR(2) for each state.
require(quantmod) data(ndx) ndx.ret2 = ROC(Cl(ndx), na.pad = FALSE)^2 ndx.rvol = sqrt(apply.weekly(ndx.ret2, FUN = 'sum')) colnames(ndx.rvol) = 'RVOL' spec = starspec(mean.model = list(states = 2, arOrder = c(2, 2), statevar = 'y', ylags = 1))
Before proceeding, I also check the presence of STAR nonlinearity using the test of Luukkonen (1988) which is implemented in the nonlinearTest method with an option for also testing with the robust assumption (to heteroscedasticity):
tmp1 = nonlinearTest(spec, data = log(ndx.rvol)) tmp2 = nonlinearTest(spec, data = log(ndx.rvol), robust = TRUE) testm = matrix(NA, ncol = 4, nrow = 2, dimnames = list(c('Standard', 'Robust'), c('F.stat', 'p.value', 'Chisq.stat', 'p.value'))) testm[1, ] = c(tmp1$F.statistic, tmp1$F.pvalue, tmp1$chisq.statistic, tmp1$chisq.pvalue) testm[2, ] = c(tmp2$F.statistic, tmp2$F.pvalue, tmp2$chisq.statistic, tmp2$chisq.pvalue) print(testm, digit = 5) ## ## F.stat p.value Chisq.stat p.value ## Standard 3.7089 0.0014366 21.312 0.0016123 ## Robust 2.5694 0.0193087 15.094 0.0195396
We can safely reject the linearity assumption under the standard test at the 1% significance level, and under the robust assumption at the 5% significance level. Note that this example is taken from the excellent book of Zivot (2007) (chapter on nonlinear models) and the numbers should also agree with what is printed there.
Model Estimation
Estimating STAR models is a challenging task, and for this purpose a number of options have been included in the package.
args(starfit) # ## function (spec, data, out.sample = 0, solver = 'optim', solver.control = list(), ## fit.control = list(stationarity = 0, fixed.se = 0, rec.init = 'all'), ## cluster = NULL, n = 25, ...) ## NULL
The data must be an xts object with the same time indices as any data already passed to the STARspec object and contain only numeric data without any missing values. The out.sample is used to indicate how many data points to optionally leave out in the estimation (from the end of the dataset) for use in out-of-sample forecasting later on when the estimated object is passed to the starforecast routine. Perhaps the most important choice to be made is the type of solver to use and it's control parameters solver.control which should not be omitted. The following solvers and 'strategies' are included:
- optim. The preferred choice is the BFGS solver. The choice of solver is controlled by the method option in the solver.control list. All parameter bounds are enforced through the use of a logistic transformation.
- nlminb. Have had little luck getting the same performance as the BFGS solver.
- solnp. Will most likely find a local solution.
- cmaes. Even though it is a global solver, it requires careful tweaking of the control parameters (and there are many). This is the parma package version of the solver.
- deoptim. Another global solver. May be slow and require tweaking of the control parameters.
- msoptim. A multistart version of optim with option for using the cluster option for parallel evaluation. The number of multi-starts is controlled by the n.restarts option in the solver.control list.
- strategy. A special purpose optimization strategy for STAR problems using the BFGS solver. It cycles between keeping the state variables fixed and estimating the linear variables (conditional mean, variance and any distribution parameters), keeping the linear variables fixed and estimating the state variables, and a random re-start optimization to control for possibly local solutions. The argument n in the routine controls the number of times to cycle through this strategy. The solver.control list should pass control arguments for the BFGS solver. This is somewhat related to concentrating the sum of squares methodology in terms of the estimation strategy, but does not minimize the sum of squares, opting instead for a proper likelihood evaluation.
The strategy and msoptim solver strategies should be the preferred choice when estimating STARMA models.
I continue with the example already covered in the specification section and estimate the model, leaving 50 points for out of sample forecasting and filtering later on:
mod = starfit(spec, data = log(ndx.rvol), out.sample = 50, solver = 'strategy', n = 8, solver.control = list(alpha = 1, beta = 0.4, gamma = 1.4, reltol = 1e-12)) show(mod) plot(mod) # ## ## *---------------------------------* ## * STAR Model Fit * ## *---------------------------------* ## states : 2 ## statevar : y ## statear : FALSE ## variance : static ## distribution : norm ## ## ## Optimal Parameters (Robust Standard Errors) ## ------------------------------------ ## Estimate Std. Error t value Pr(>|t|) ## s1.phi0 -3.54380 0.034260 -103.43760 0.000000 ## s1.phi1 -0.64567 0.426487 -1.51393 0.130043 ## s1.phi2 0.10950 0.319605 0.34262 0.731886 ## s2.phi0 -2.51982 0.849927 -2.96475 0.003029 ## s2.phi1 0.10902 0.214009 0.50944 0.610444 ## s2.phi2 0.17944 0.062210 2.88447 0.003921 ## s1.gamma 3.22588 1.941072 1.66190 0.096532 ## s1.c -2.52662 0.347722 -7.26620 0.000000 ## s1.alpha1 1.00000 NA NA NA ## sigma 0.39942 0.019924 20.04776 0.000000 ## ## LogLikelihood : -126.3 ## ## Akaike 1.0738 ## Bayes 1.1999 ## Shibata 1.0714 ## Hannan-Quinn 1.1246 ## ## r.squared : 0.3167 ## r.squared (adj) : 0.2913 ## RSS : 40.2 ## skewness (res) : -0.235 ## ex.kurtosis (res) : 0.4704 ## ## AR roots ## Moduli1 Moduli2 ## state_1 1.274 7.170 ## state_2 2.076 2.684
Model Filtering
Filtering a dataset with an already estimated set of parameters has been already extensively discussed in a related post for the rugarch package. The method takes the following arguments:
args(starfilter) ## ## function (spec, data, out.sample = 0, n.old = NULL, rec.init = 'all', ...) ##
The most important argument is probably n.old and denotes, in the case that the new dataset is composed of the old dataset (on which estimation took place) and the new data, the number of points composing the original dataset. This is so as to use the same initialization values for certain recursions and return the exact same results for those points in the original dataset. The following example illustrates:
specf = spec setfixed(specf) < - as.list(coef(mod)) N = nrow(ndx.rvol) - 50 modf = starfilter(specf, data = log(ndx.rvol), n.old = N) print(all.equal(fitted(modf)[1:N], fitted(mod))) ## [1] TRUE print(all.equal(states(modf)[1:N], states(mod))) ## [1] TRUE
Model Forecasting
Nonlinear models are considerable more complex than their linear counterparts to forecast. For 1-ahead this is quite simple, but for n-ahead there is no closed form solution as in the linear case. Consider a general nonlinear first order autoregressive model:
{y_t} = F\left( {{y_{t – 1}};\theta } \right) + {\varepsilon _t}
The 1-step ahead forecast is simply:
{{\hat y}_{t + 1\left| t \right.}} = E\left[ {{y_{t + 1}}\left| {{\Im _t}} \right.} \right] = F\left( {{y_t};\theta } \right)
However, for n-step ahead, and using the Chapman-Kolmogorov relationship \( g\left( {{y_{t + h}}\left| {{\Im _t}} \right.} \right) = \int_{ – \infty }^\infty {g\left( {{y_{t + h}}\left| {{y_{t + h – 1}}} \right.} \right)g\left( {{y_{t + h – 1}}\left| {{\Im _t}} \right.} \right)d{y_{t + h – 1}}} \), we have:
\[E\left[ {{y_{t + h}}\left| {{\Im _t}} \right.} \right] = \int_{ – \infty }^\infty {E\left[ {{y_{t + h}}\left| {{y_{t + h – 1}}} \right.} \right]g\left( {{y_{t + h – 1}}\left| {{\Im _t}} \right.} \right)d{y_{t + h – 1}}}\]
where there is no closed form relationship since \( E\left[ {F\left( . \right)} \right] \ne F\left({E\left[ . \right]} \right) \).
The trick is to start at h=2:
{{\hat y}_{t + 2\left| t \right.}} = \frac{1}{T}\sum\limits_{i = 1}^T {F\left( {{{\hat y}_{t + 1\left| t \right.}} + {\varepsilon _i};\theta } \right)}
and using either quadrature integration or monte carlo summation obtain the expected value. Use that value for the next step, rinse and repeat.
In the twinkle package, both quadrature and monte carlo summation are options in the starforecast method:
args(starforecast) # ## function (fitORspec, data = NULL, n.ahead = 1, n.roll = 0, out.sample = 0, ## external.forecasts = list(xregfor = NULL, vregfor = NULL, ## sfor = NULL, probfor = NULL), method = c('an.parametric', ## 'an.kernel', 'mc.empirical', 'mc.parametric', 'mc.kernel'), ## mc.sims = NULL, ...) ## NULL
with added options for either parametric, empirical or kernel fitted distribution for the residuals. The method also allows for multiple dispatch methods by taking either an object of class STARfit or one of class STARspec (with fixed parameters and a dataset). The example below illustrates the different methods:
forc1 = starforecast(mod, n.roll = 2, n.ahead = 20, method = 'an.parametric', mc.sims = 10000) forc2 = starforecast(mod, n.roll = 2, n.ahead = 20, method = 'an.kernel', mc.sims = 10000) forc3 = starforecast(mod, n.roll = 2, n.ahead = 20, method = 'mc.empirical', mc.sims = 10000) forc4 = starforecast(mod, n.roll = 2, n.ahead = 20, method = 'mc.parametric', mc.sims = 10000) forc5 = starforecast(mod, n.roll = 2, n.ahead = 20, method = 'mc.kernel', mc.sims = 10000) show(forc1) par(mfrow = c(2, 3)) plot(forc1) plot(forc2) plot(forc3) plot(forc4) plot(forc5) # ## ## *------------------------------------* ## * STAR Model Forecast * ## *------------------------------------* ## Horizon : 20 ## Roll Steps : 2 ## STAR forecast : an.parametric ## Out of Sample : 20 ## ## 0-roll forecast [T0=2000-10-27]: ## Series ## T+1 -2.684 ## T+2 -2.820 ## T+3 -2.948 ## T+4 -3.061 ## T+5 -3.157 ## T+6 -3.231 ## T+7 -3.286 ## T+8 -3.324 ## T+9 -3.350 ## T+10 -3.368 ## T+11 -3.379 ## T+12 -3.387 ## T+13 -3.392 ## T+14 -3.395 ## T+15 -3.397 ## T+16 -3.398 ## T+17 -3.399 ## T+18 -3.400 ## T+19 -3.400 ## T+20 -3.400
The nice thing about the monte carlo method is that the density of each point forecast is now available, and used in the plot method to draw quantiles around that forecast. It can be extracted by looking at the slot object@forecast$yDist, which is list of length n.roll+1 of matrices of dimensions mc.sims by n.ahead.
Model Simulation
Simulation in the twinkle package, like in rugarch, can be carried out directly on the estimated STARfit object else on a specification object of class STARspec with fixed parameters. Achieving equivalence between the two relates to start-up initialization conditions and is always a good check on reproducibility and code correctness, and shown in the example that follows:
sim = starsim(mod, n.sim = 1000, rseed = 10) path = starpath(specf, n.sim = 1000, prereturns = tail(log(ndx.rvol)[1:N], rseed = 10) all.equal(fitted(sim), fitted(path)) ## TRUE all.equal(states(sim), states(path)) ## TRUE
The fitted method extracts the simulated series as an n.sim by m.sim matrix, while the states method extracts the simulated state probabilities (optionally takes "type" argument with options for extracting the simulated raw dynamics or conditional simulated mean per state) and can be passed the argument sim to indicate which m.sim run to extract (default: 1). Passing the correct prereturns value and the same seed as in starsim, initializes the simulation from the same values as the test of equality shows between the 2 methods. Things become a little more complicated when using external regressors or GARCH dynamics, but with careful preparation the results should again be the same.
Rolling estimation and 1-step ahead forecasting
The final key modelling method, useful for backtesting, is that of the rolling estimation and 1-step ahead forecasting which has a number of options to define the type of estimation window to use as well as a resume method which re-estimates any windows which did not converge during the original run. This type of method has already been covered in related posts of rugarch so I will reserve a more in-depth demo for a later date.
Final Thoughts
This post provided an introduction to the use of the twinkle package which should hopefully make it to CRAN from bitbucket soon. It is still in beta, and it will certainly take some time to mature, so please report bugs or feel free to contribute patches. The package departs from traditional implementations, sparse as they are, in the area of STAR models by offering extensions in the form of (MA)(X) dynamics in the conditional mean, (AR) dynamics in the conditional state equation, a mixture model for the variance, and a softmax representation for the multi-state model. It brings a complete modelling framework, developed in the rugarch package, to STAR model estimation with a set of methods which are usually lacking elsewhere. It also brings, at least for the time being, a promise of user engagement (via the R-SIG-FINANCE mailing list) and maintenance.
Download and installation instructions can be found here.
[1] Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 357-384.
[2] Tong, H., & Lim, K. S. (1980). Threshold Autoregression, Limit Cycles and Cyclical Data. Journal of the Royal Statistical Society. Series B (Methodological), 245-292.
[3] Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical association, 89(425), 208-218.
[4] Luukkonen, R., Saikkonen, P., & Teräsvirta, T. (1988). Testing linearity against smooth transition autoregressive models. Biometrika, 75(3), 491-499.
[5] Zivot, E., & Wang, J. (2007). Modeling Financial Time Series with S-PLUS® (Vol. 191). Springer.


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News Story
Clark School Researchers Help Decode Speech Recognition
Clark School Professor Shihab Shamma (electrical and computer engineering/Institute for Systems Research [ISR]), former ISR postdoctoral researcher Stephen David*, and alumnus Nima Mesgarani** (Ph.D. '08, electrical engineering) are three of the authors of a new study on how the human auditory system processes speech, published in the Jan. 31, 2012, edition of PLoS Biology.
"Reconstructing Speech from Human Auditory Cortex" details recent progress made in understanding the human brain's computational mechanisms for decoding speech. The researchers took advantage of rare neurosurgical procedures for the treatment of epilepsy, in which neural activity is measured directly from the brain's cortical surface—a unique opportunity for characterizing how the human brain performs speech recognition. The recordings helped researchers understand what speech sounds could be reconstructed, or decoded, from higher order brain areas in the human auditory system.
The decoded speech representations allowed readout and identification of individual words directly from brain activity during single trial sound presentations. The results provide insights into higher order neural speech processing and suggest it may be possible to readout intended speech directly from brain activity. Potential applications include devices for those who have lost the ability to speak through illness or injury.
Brian N. Pasley, Helen Wills Neuroscience Institute, University of California Berkeley is the paper's lead author. In addition to the Clark School-affiliated co-authors, additional co-authors include Robert Knight, University of California San Francisco and University of California Berkeley; Adeen Flinker, University of California Berkeley; Edward Chang, University of California San Francisco; and Nathan Crone, Johns Hopkins University.
* Stephen David is now an assistant professor at Oregon Health & Science University, where he heads the Laboratory of Brain, Hearing, and Behavior in the Oregon Hearing Research Center.
** Nima Mesgarani is currently a postdoctoral researcher in the Neurological Surgery Department of the University of California, San Francisco School of Medicine.
| Read a story about this research in USA Today |
Published February 6, 2012


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Friday, November 3, 2006
On November 13, Torontonians will be heading to the polls to vote for their ward's councillor and for mayor. Among Toronto's ridings is Don Valley West (Ward 26). Four candidates responded to Wikinews' requests for an interview. This ward's candidates include Muhammad Alam, Bahar Aminvaziri, Orhan Aybars, Michele Carroll-Smith, Mohamed Dhanani, Abdul Ingar, Geoff Kettel, Debbie Lechter, Natalie Maniates, John Masterson, John Parker, David Thomas, Csaba Vegh, and Fred Williams.
For more information on the election, read Toronto municipal election, 2006.


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Bogie and I are headed to a birthday party this afternoon and his excitement is a little intense. I'm thinking we should arrive separately?
Happy BIRTHDAY CHUNKY!!!
This birthday boy deserves every lick of his cake. Put your paws together for our favorite @bigchunkymonkey and his St. Patrick's birthday!Morty Exposes The Industrial Chew Toy Complex
If humans got one thing right, it's the IRS. Wonderful profession. More of a calling than a job really. If I understand Mr. Baxter correctly, it's an entire agency that sneaks around making sure everybody else is on the up and up. Newsflash puppies: That's what I do everyday—have been for the better part of a decade. Don't get me started. Of course sometimes I don't know why I bother. Most puppies are hopeless. It's all entitlement and excess with these goofballs. Doggles®? Are you serious!? Ha! I can see fine without "stylish protective eyeware" thank you very much. Who comes up with this junk? When I was a puppy I had one tennis ball. One. Kept it for three years and it wasn't even new to begin with. That's how we did things back then. Don't get me started. Did I complain? Heck no! I was grateful and took damn good care of my belonging—dropped it in my water bucket every now and then, kept it clean as best I could. No telling when I'd get another one if God forbid something happened to it. Of course that was all before the meteoric rise of the Chew Toy Industrial Complex. That's right puppies, I'm talking about a shadow pack of elites—probably Huskies—who control everything. But don't expect to read about it in The Droolitzer, I'm pretty sure Bogie is in on their mainstream agenda. Canine social engineering and whatnot. Newsflash puppies: life's not all biscuits and belly rubs. Don't say I didn't warn you. And whatever happened to carefully burying your bones for a rainy day? Lemme guess? You don't wanna get your wittle paws durtie. Ha! Anyway, who's funding this surplus of assets in the canine community? That's what I want to know. Half these puppies are underemployed at best. Unbelievable. Which remind me of the time…


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Learn the post-COVID-19 essentials needed to demonstrate your commitment to putting your staff and guest's well-being first in this comprehensive course for servers and waitstaff. Students will discover how to prevent the spread of COVID and obtain key industry cleaning and disinfecting requirements.
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With the looming fear of contracting COVID-19, customers want to know that hotels, restaurants, and food service businesses are doing everything possible to maintain a safe and sanitized environment.
Enrolling in the COVID Readiness for Servers and Waitstaff course drives home the message that you are committed to prioritizing the wellbeing of your staff and guests.
The Academy for Hospitality Arts is seated in the belief that exceptional service starts with intentional planning. With 130 years of combined experience across an array of settings– including hotels, restaurants, county clubs, fine dining, and entertainment complexes–we are the heartbeat of hospitality. Our mission is to educate, enrich and inspire hospitality professionals leveraging our collective knowledge managing more than 10,000 events across the globe.
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Michael J. DiPeri
AHA President and Founder Michael J. DiPeri continuously leverages his 38+ years of experience to educate, enrich, and inspire hospitality professionals. Following a series of sales and service roles, Michael created an event resource company in 1997, Yipeee, Inc. Establishing his own business enabled him to pour his experience into consulting, staffing, and event management services for industry leaders and celebrity chefs. Since then, he has successfully launched an enterprise event software and premier event staffing company.
From developing groundbreaking training programs to leading multi-day trainings for 1000+ students, Michael is dedicated to helping those around him exceed expectations. He is a graduate of Farmingdale State College SUNY in Long Island.
Richard Hawk
Richard Hawk's 30+ year career is seated in safety and service excellence. A graduate of the University of Iowa, his portfolio includes more than 4,000 events and roles such as event captain, event planner, and director of service. Noteworthy professional experiences include serving dignitaries such as President Clinton, President Obama and Mikhail Gorbachev.
As Corporate Captain at AHA, Richard continuously invests in learning opportunities to elevate course content and training concepts for students. He recently completed more than a dozen certifications, including COVID-19 food handling, hospitality leadership, DE&I communications, COVID-19 safety, and contact tracing and dining precautions.
Stephen Ruggieri
Stephen Ruggieri is an accomplished event manager and training leader with 25 years of career experience. Focused on business and development, Stephen has pursued various roles to nurture his passion, including positions in event planning and production, training and development, and serving as head of hospitality for Bloomberg L.P. He is a graduate of the University of Rhode Island.
Stephen's career highlights include serving the Dali Lama, The Duke and Duchess of Cambridge, Donna Karen, Dianna Ross, Dolce & Gabbana, Diane von Fürstenberg, Giorgio Armani, and working on the grand opening of Atlantis in Dubai. He currently serves as Director of Training and Development for AHA, where he leverages his background to create accessible learning frameworks that instill compassion and confidence in hospitality professionals.
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Have you ever looked in your kitchen cabinets and thought, "Huh, I wonder if I can put that in soap?" If so, you're in good company. It's a pretty common thought among soapmakers. Also, you are in luck – there are plenty of fresh ingredients you can use in your cold process recipes. In addition to setting your soap apart and attracting customers, fresh ingredients add something special to your recipes. For instance, avocado gives soap a luxurious feeling. Find a roundup of recipes made with fresh ingredients here. Before getting started, there are a few things to know. The tips below will ensure your experiments turn out well so you can focus on the fun part – getting creative and trying something new!
Research Before Getting Started
Each ingredient you add to your recipe will affect how the soap performs. If you add an ingredient with natural sugar, it can cause acceleration and overheating. If you add an ingredient with a lot of liquid, it can make your batch too wet. That's why research is important. Find out how the ingredient performs beforehand so you know what to expect and can account for any quirks. The How to Add Purees to Cold Process Soap has plenty of information to help get you started.
Honey can overheat in soap and cause discoloration and bubbles, as seen in the photo above. Learn how to work with honey here.
Buy Local
Once you decide what ingredient you want to experiment with, the next step is finding it. We recommend buying local! There are so many quality ingredients at your local stores and farmers markets. For instance, at the Farmers Market here in Bellingham, there is everything from fresh coffee to juicy blueberries. They would be an amazing addition to your recipe. Not only do you support your local economy, but you can tell your customers all about the fresh ingredients and where they come from. It's an amazing selling point and it shows off your hometown pride.
Consider a Water Discount
To help the fresh ingredients mix in smoothly, they are often pureed with distilled water. Many fresh ingredients also contain a lot of liquid. The added water can make your soap too wet. That's where a water discount comes in – it accounts for the extra liquid you're adding to your batch. It also helps prevent glycerin rivers. How much water you discount from your recipe depends on the fresh ingredient. For instance, cucumber is a very watery vegetable, so a higher water discount would be helpful. A good water discount to start with is 10% and adjust from there based on the additive you choose. Learn more in the Water Discounting Cold Process Soap post.
A water discount keeps your recipe balanced.
Watch Those Temperatures
Fresh ingredients with natural sugar have the potential to overheat in cold process soap. That can cause discoloration, an unpleasant smell, heat tunnels and, worst case scenario, a soap volcano. That's why it's important to keep your temperatures low. Soaping around 100-110 ° F will keep things cool and give you time to work with your design. If you're using an alternative liquid, you may want to cool or freeze it beforehand to prevent scorching. Learn more about working with coffee and tea, alcohol and milk in your soap. Once the soap is in the mold, put it in the fridge or freezer for 5-24 hours. If you're using a fresh ingredient without natural sugar, they can still cause glycerin rivers or partial gel phase. You can leave the soap at room temperature or cool it in the freezer to be extra careful.
Milk needs to be frozen before the lye is added to keep temperatures low. Learn more in this post.
Test it Out
Whatever fresh ingredient you choose to jazz up your cold process soap, make a small test batch of your recipe. If you've never worked with that ingredient before it can do unpredictable things. The test batch allows you to make adjustments before making a larger batch. It's also a great time to test the bars on your skin to see what you think. If you add avocado and the bars are too soft, you can add more hard oils like coconut and palm. If the honey overheated your batch even with cooler temperatures, you can add less next time. Before you know it, you'll be a pro at adding random cabinet ingredients to your handmade goodies!
The Avocado + Spearmint Cold Process Soap took several tries to get just right.
What's your favorite fresh additive for cold process soap?


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Perle Beverage Glass
We love these bright and delicate Perle Beverage Glasses.
Designed by Federico de Majo, Perle has an extraordinarily charming effect thanks to the blend of transparency and vitreous ceramic body, obtained by combining unique materials with a particular method of working the glass, completely by hand, skillfully interpreted by the master glassblowers.
Complete the look with other items from Zafferano.
Height - 5.3"
Diameter - 3.1"
Capacity - 15oz
Dishwasher Safe
Due to the handmade nature of this item please expect variations in appearance, as well as possible bubbles.
Shipping methods are the estimated delivery days your order will take to deliver to your delivery address AFTER it has been picked up from our retail store. Delivery dates are not guaranteed, notwithstanding additional charges, but are approximations and will vary with specific requests, such as gift box, gift wrap, and personalizations. Shipments and deliveries occur only on weekdays. Shipping charges and times may vary.
We process orders for shipping Monday - Friday, excluding weekends and holidays. All orders received on weekdays after 12pm EST, on weekends or holidays will begin processing the following business day. All orders placed Monday - Friday before 12pm EST will begin processing the same business day. Please allow 24-48 hours for processing pending merchandise availability and credit card authorization. We offer free shipping on orders over $200 using the code FREETOSHIP. This offer cannot be combined with other offers.
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At Julia Moss Designs, we respect and value every customer. Because your trust is important to us, we want you to be completely happy with every purchase. If you are not satisfied, we are glad to accept returns of full-priced merchandise for a full refund or exchange within 7 days of original in-store purchase, or within 7 days after order delivery. After 7 days, we can offer store credit only. The sale is final after 30 days. Other restrictions apply. For full details on returns, click here.
Along with any merchandise purchased more than 30 days from date on original receipt, the following items are FINAL SALE (not eligible for return or exchange):
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13. Merchandise used for photoshoots or styling


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Review of our recent Future Human Symposium at GV art gallery, by Liane Fredericks, in SciArt in America
'To step into a program like Broad Vision, students have to accept that it might not be the most direct route to PhD funding. They need to be prepared to work within an emergent and "chaordic" learning process. In return, Broad Vision provides an unpatronizing mentored space for students to go beyond their disciplinary silos."


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Jaguar Land Rover
Jaguar Land Rover is a West Midlands-based automotive company, a wholly owned subsiduary of India's Tata Motors. The firm develops and manufactures the iconic Jaguar and Land Rover vehicles from bases at Castle Bromwich in Birmingham, Solihull, Gaydon, Whitley in Coventry, Halewood in Merseyside and Pune in India. Only a quarter of JLR's vehicles are sold in the UK, with the rest going to major markets in Europe, the US, Russia and China.
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Dopamine release in human brain tracked at microsecond timescale reveals decision-making
A research team led by investigators at the Virginia Tech Carilion Research Institute has demonstrated the first rapid measurements of dopamine release in a human brain and provided preliminary evidence that the neurotransmitter can be tracked in its movement between brain cells while a subject expresses decision-making behavior.
"In an experiment where we measured dopamine release while a subject made investment decisions in a stock market trading game, we showed that dopamine tracks changes in the value of the market," said Read Montague, director of the Human Neuroimaging Laboratory at the Virginia Tech Carilion Research Institute and professor of physics in the College of Science at Virginia Tech.
"A startling discovery was that the dopamine signal appeared to be a very good indicator of the market value and in many instances a good predictor of future market changes," said Kenneth Kishida, a postdoctoral associate with the Human Neuroimaging Laboratory and the lead author on the report. Interestingly, the choice expressed by the subject did not always correspond with the prescient brain chemistry, he said.
The research was published on Aug. 4, 2011, in the Public Library of Science journal, PLoS ONE, in the article "Sub-Second Dopamine Detection in Human Striatum," by Kishida; Stefan G. Sandberg, senior fellow with the Departments of Psychiatry and Behavioral Sciences and Pharmacology, University of Washington, Seattle; Terry Lohrenz, assistant professor in the Department of Neuroscience, Baylor College of Medicine; Dr. Youssef G. Comair, professor and chief, Division of Neurosurgery, American University of Beirut, Lebanon; Ignacio Saez, assistant professor at Virginia Tech Carilion Research Institute; Paul E. M. Phillips, associate professor, Departments of Psychiatry and Behavioral Sciences and Pharmacology, University of Washington, Seattle; and Montague, senior author.
The researchers adapted their sensors to existing technology used for functional mapping of the brain during surgical implantation of deep-brain stimulation devices. "Deep-brain stimulation is typically used in the treatment of Parkinson's disease," said Montague. "Uses for treating other neurological disorders are also being investigated, though, and may open new avenues for the technology we developed."
The researchers applied criteria that employed experimental methodology that is "safe to the patient, compatible with existing neurosurgical apparatus and the operating-room environment, and capable of sub-second detection of physiological dopamine," they state in the article. They modified existing sensor technology to improve signal conductivity, creating a microsensor that shares the electrochemical properties of existing electrodes yet can detect sub-second dopamine release. "Even more important, the new microsensors are biocompatible and can be sterilized without affecting performance, " Kishida added.
The new instrument was demonstrated in a single human subject, a consenting patient with late-stage Parkinson's disease who was undergoing elective surgery for deep-brain stimulation electrode implantation. The new microsensor was placed in the patient's brain and dopamine release was monitored as the patient engaged in a decision-making game. The current value and recent history of a stock market was graphically represented on a laptop monitor. The subject chose the proportion of a portfolio initially valued at $100 to be invested in the stock market. Decisions were submitted by pushing buttons on handheld response devices. Following the submission of each decision, the market was updated. The final portfolio determined the actual payout at the end of the experiment.
The researchers report that they were surprised to observe that "the slope of the dopamine signal over a period five seconds prior to a market price update correlated with subsequent market returns…, demonstrating that it is a significant predictor of future market activity."
To test this hypothesis, the researchers constructed a trader model that made decisions based on the fluctuations in the dopamine signal leading up to the market price changes. This decision model invested 100 percent, or all in, when the dopamine slope was positive and 0 percent, or all out, when the slope was negative. The researchers report that, "Over the five markets played, this trader model earned 202 points (a gain of 175 percent), more than two times the amount earned by the subject's expressed behavior. These data demonstrate that the information encoded in the dopamine signal of this patient is potentially useful for economic decision making."
"This exciting preliminary result requires replication, but it immediately sets the imagination in motion," said Kishida. "I often wonder whether there is a feeling associated with these dopamine fluctuations and whether there is any connection with that 'gut feeling' people sometimes ignore."
Writing in the PLoS ONE article, the researchers conclude, "This methodological demonstration opens the door to future investigations utilizing sub-second chemical measurements in the human brain, which should yield important insights into the role of dopamine signaling in human decision-making."