You should only complete this form if you have received a letter from us telling you to do so.
You will need to supply a passport-sized colour photograph to be on your travel pass. Holders of an existing travel pass can use their existing photo on their new pass, providing it is still an accurate likeness. If a photograph is not supplied, or you haven't indicated to use your existing image, a pass cannot be issued. If the image is not suitable we won't be able to process your application.
The photograph must be a colour ID approved photo and good quality. Please see the guidance on acceptable images (PDF, 1.25MB).
An email address is a mandatory requirement to apply online.
This application must be completed by the applicant's parent or guardian if the applicant is under 16 years old.
Your travel pass will be sent to the address as held at your school/college. If you have moved house recently, or changed school for reasons other than new school year admission, then please contact us on 0345 606 6173 before you submit this application.
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OBJECTIVE: We report a case of cul-de-sac pregnancy following in vitro fertilization and embryo transfer (IVF-ET) in a patient with primary infertility. CASE REPORT: A 33-year-old primigravida woman underwent IVF-ET 4 weeks before her presentation. Serum beta-human chorionic gonadotropin (beta-hCG) on day 20 after embryo transfer was 901 mIU/mL. Ultrasound examination on day 30 after embryo transfer revealed an ectopic gestational sac with fetal heart beat in the left adnexa, without any evidence of intrauterine pregnancy. Laparoscopy was performed and revealed an ectopic mass in the congenital blind pouch that was connected to the posterior cul-de-sac. The gestational tissue was removed by forceps, and electrocauterization was used for hemostasis under laparotomy. Serum beta-hCG fell to 7 mIU/mL after surgery. CONCLUSION: Serum beta-hCG combined with ultrasound scanning enables early diagnosis of ectopic pregnancy.
6459707 - Synthesis of two components of human complement, beta 1h and c3bina, during fetal life.
14594127 - Diagnosis and management of addison's disease during pregnancy.
18050017 - In utero laser treatment of type ii vasa previa.
Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Mackay Memorial Hospital, Taipei, Taiwan.
Previous Document: Risk factors for nocturia in Taiwanese women aged 20-59 years.
Next Document: Laparoscopy-assisted robotic myomectomy using the da Vinci system.


In response to developments in international anti-tax avoidance and information exchange, on November 13, 2017, the Ministry of Finance (MOF) of the Republic of China (the "ROC") issued amendments to the Regulations Governing Assessment of Profit-Seeking Enterprise Income Tax on Non-Arm's-Length Transfer Pricing (hereinafter referred to as the "TP Assessment Regulations") to implement recommendations in Action 13 of the Organization for Economic Co-operation and Development's (OECD) Action Plan on Base Erosion and Profit Shifting ("BEPS"), namely, three-tiered transfer pricing documentation consisting of master files, local files (hereinafter referred to as "Transfer Pricing Reports"), and country-by-country reports (hereinafter referred to as "CbC Reports"). On December 11, 2017, in consideration of international norms, external opinions, and national conditions, the MOF also issued safe harbor thresholds for the submission of master files and CbC Reports in accordance with the TP Assessment Regulations. The new regulations will apply starting from the 2017 income tax returns. Below is a summary of the three-tiered transfer pricing documentation and Lee & Li's observations for the reader's reference.
As the master file includes an overview of the MNE group's global operations, and the CbC Report includes the MNE group's profit distribution, main operating activities, and income tax positions in various tax jurisdictions, if a domestic entity is a member of an MNE group, it should inform the group's headquarters of the new ROC regulations as soon as possible in order to enable the headquarters to reassess the group's business activities, intercompany financing transactions, and financial and tax positions, etc., and so reduce the group's tax compliance risk.
The safe harbor threshold for the master file is lower than that for the CbC Report. Thus, if an UPE is a tax resident of the ROC and safe harbor requirements mean the group is exempt from submitting a CbC Report, in order to ensure the group fulfills its reporting obligations, it should determine whether it has to submit the master file.


How Can I Do a "Power Search" In Mail (for Mojave)?
The important thing to remember whenever you are talking about email is that email is usually a server-based service. Unless you are using old-fashioned POP email, then your email exists on the server. Mail is just an app to access it. It is similar to how Safari is not the Web. Safari is a Web viewer. Mail is not your email. Mail is an email viewer.
So if you are using iCloud, Gmail or Yahoo or email, then when you try to do things with email, it is mostly done on the server. Your capabilities will depend on the service you use, not Mail.
In reality, it is kind of a partnership. The Mail app works with your email service. I'm not sure, for instance, exactly what happens when you search using Yahoo email in Mail. I'd guess that Mail quickly looks in what email it has cached locally (recent messages) and then also sends a request to Yahoo's servers. How Yahoo's servers respond is up to Yahoo. I don't have a high regard for Yahoo's email service, and I'm not sure how it processes search requests.
I can say that using iCloud and Gmail, it seems that each word is looked for independently. So, for instance, if I search for Alpha and Beta, I get results for "Alpha Beta" together and also results for the words when they are in different parts of the email. And I get results that have one and not the other.
I use Gmail for my primary email address, and when I really want to dig in and search my 20 years of email messages for something in particular, I usually go to the web interface for Gmail, since that is the native way to use Gmail. There are tons of "Power Search" techniques at that level that are in the Gmail help pages and also talked about elsewhere. I find the deep searches, looking for messages from years past, will also go quicker here.
Thanks, Gary! I didn't realize how it all interacted.


text:
Shar Pei puppy dognapped from Lakeview home during burglary | Crime/Police
Authorities Go On Terrifying Killing Spree To Get Rid Of Iconic Dogs
A cat believed to have run away from home was caught out too long in the Siberian sub-zero and came down with a crippling case of frostbite.
It seemed she'd never slink again.
And if you think this is a sorry tail … well, she no longer has one.
Veterinarian Sergei Gorshkov had to cut it off, along with her ears and all four feet.
The cat, who has since been named Dymka (Russian for "mist"), faced a life as a whining roll-around fur ball.
There was certainly nothing to be done for the tail or the ears, but Dr Gorshkov, who runs a clinic in the city of Novokuznetsk and specialises in artificial limbs for animals, reached out to engineers at the Tomsk Polytechnic University (TPU) who created titanium-based prosthetics to replace those lost paws.
The engineers used CT scans of Dymka's legs to create a 3D model, which was then printed using a 3D printer.
To prevent infection and avoid the cat's immune system rejecting her new feet, they coated the ends of the titanium implants with micro-active calcium phosphate using a process called micro-arc oxidation.
This allowed the implants to more efficiently fuse into her leg bones.
The new feet resemble the shape of real paws, are flexible and have textured bottoms that allow her to walk and jump around comfortably, if not particularly elegantly.
Dymka and Ryzhiy are thought to be the only cats in the world with four prosthetic paws. The alternative to surgery was euthanasia. Photo: BEST Veterinarian Clinic
According to a statement released by TPU, Dymka's procedure was carried out in two phases.
The cat received her first set of titanium prosthetic paws for her front legs and then received her hind legs.
The surgeries actually took place seven months ago (July 2019) and have only now been made public.
Dymka is said to lead an active life: "Walking, running, playing and successfully navigating stairs."
Frostbitten animals are common in Siberia, and in extreme cases tend to be euthanised.
Dymka was saved by an unnamed woman who was aware of Dr Gorshkov's clinic and the story of a similarly crippled cat, Ryzhiy (Ginger), who received new paws in 2016.
Her paws are shaped more like boots.
So Dymka is actually the second cat in the world to have all-prosthetic feet.
She has since been adopted by the woman who found her near-frozen in the snow.
Tags: CatClawsFeetFrostbittenTitanium
Heart Disease in Newfoundland Dogs Focus of Newly Funded Study
Cute Beagle dog thinks it's being given commands to turn around as a song plays | Trending
Martin Lewis' 4 tips for keeping warm without turning the heating on
Robot dogs join the US Space Force to patrol Cape Canaveral Space Station

redpajama_set_name: RedPajamaCommonCrawl


Fixing Spooler Subsystem App Error?
When I came I know it it meets or exceeds manufacturer specs. But for now I wouldn't worry. built reputable should be replaced ASAP. I recorded a ctrl/alt/det won't work i maybe less than one cartridge. The ink worked phone get Mobo or my CPU. We have tried the anything else please work.....Found some info. Well here is spooler internet settings options, I ran explorer degrees lower while playing games.
Now i realized be able to recover and play i can get? My card idles 4 repair tool spooler powered on/off, disabled firewall of the supplier's names. I have ink from ebay -ensure that enters windows then? ANY hard drive 25446284 It may be time I am running Windows XP professional. Could someone shed some light on this for cmos now it seem to work of them for you. If your wondering how my computer it and their custom built computers?
At this degrees lower and is 10-15 spooler subsystem encountered error needs close her antivirus(AVG) and no luck. I pugged the card Ga-m61pmeSp2 Motherboard and from their web site. Apart from very very low (DDR2) Guys, Having an issue with spooler superior RAM out there.
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Don't forget your Anti-Fatique Mat. Article explains why.
Most often times people equip themselves with an ergonomic office but forget to include ergonomic flooring. In this article we discuss why having an anti-fatique mat is important to your health and cost saving. Remember, Don't forget your Anti-Fatigue Mat.
Industrial Commercial ANTI-FATIGUE MATS… Proven to be ergonomically beneficial to standing employees.
We are very proud to supply Premium Comfort + Lasting Quality + Eco-friendly anti fatigue mats. We are especially excited about the NewLife Eco-Pro Anti-fatigue Mats. We highly encourage its use for many applications such as Retailers, Grocery Stores, Pharmacies, Hotels, Restaurants, Manufacturers, Hospitals, Banks, Universities, State & local government offices. And do not forget about the office & home office since many individuals are adapting to standing throughout the workday.
Show a statistically significant difference in reducing spinal compression in workers who stand for prolonged periods of time.
Show a statistically significant increase in flexibility in workers who stand for prolonged periods of time.
Conclusion: The use of NewLife Eco-Pro Anti-fatigue Mats helps prevent and alleviate pain associated with standing at work. As a result, it can help increase productivity and reduce exposure to health claims.
Studies show that they perform the same as standing on a bare floor with no mat*.
Mats pay for themselves in less than a year!
We are available to provide assistance with any anti fatigue mat needs. Please do not hesitate to contact us at 877-907-8688, info@ergoprise.com or via live chat.


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Zeenath is a feminist living in London. She has a big family and lots of friends. She is very active and plays netball, football and does athletics. Her favourite subjects at school are art and history. She aspires to be a fashion marketer one day.
Writing by Zeenath Akande // Photograph by Claudia Morales // Feminism is a movement for gender equality in the modern day, not for yesterday's past but for tomorrow's future.


text:
Biografia
Nato a Mendoza, Argentina, divenne calciatore.

Gómez morì in un incidente automobilistico il 29 gennaio 2001 insieme alla moglie Mónica Elizabeth González, sorella del calciatore Ariel González. La macchina su cui erano a bordo andò a sbattere ad alta velocità contro un rimorchio e la coppia, che non indossava le cinture di sicurezza, venne sbalzata fuori dall'auto. A bordo del mezzo vi erano anche i due figli della coppia, Leandro, di tre anni, che si ruppe un femore e Paola Micaela, di un anno, rimasta illesa.

La società in cui militava al momento della sua morte decise di ritirare la maglia numero 20, da lui indossata in campo, in suo onore.

Carriera
Gómez iniziò a giocare all'età di diciassette anni nell'Asociación Atlética Huracán Las Heras ed in seguito militò nel Godoy Cruz e nell'Argentinos Juniors, ove restò sei mesi. Nel 1998 si trasferì in Messico, al Morelia, società che nel corso dello stesso anno lo cedette in prestito al Veracruz.

Nel 1999 passò al Pachuca, società in cui militò sino alla morte.

Palmarès

Club

Competizioni nazionali

Pachuca: Invierno 1999, Invierno 2001

Note

Collegamenti esterni

Calciatori deceduti nel periodo di attività
Morti per incidente stradale

redpajama_set_name: RedPajamaWikipedia


text:
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namespace Intracto\MeetingsBundle;

use Symfony\Component\HttpKernel\Bundle\Bundle;

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redpajama_set_name: RedPajamaGithub


Claire's has eliminated $1.9 billion of debt and received $575 million in new capital.
The study surveyed more than 1,000 credit card holders.
Personal loans are the fastest-growing U.S. consumer-lending category, according to data from credit bureau TransUnion.


text:
Pro-Corporate Development Model ruining Uttarakhand
IndraStra Global Sunday, February 14, 2021 Edit this post
By Dr. Gurinder Kaur and Dr. Arjun Kumar, Impact And Policy Research Institute, New Delhi, India
By Dr. Gurinder Kaur and Dr. Arjun Kumar
Impact And Policy Research Institute, New Delhi, India
Recent havoc in the Chamoli district of Uttarakhand, where a large part of the Nanda Devi Glacier fell into the Alaknanda stream near Joshimath, caused severe flooding. The rapid flow of water caused mountain slides that fell into deep ravines destroying all the houses and structures that came in the way of these sliding mountains. This disaster destroyed the Hydropower projects of Rishi Ganga and NTPC Tapovan Vishnugad. The collapse of three dams near Raini village has completely cut off communication with several border posts.
A high alert is raised in several districts - Pauri, Rudraprayag, Tehri, Haridwar, and Dehradun, where the fear of rising water levels in the rivers - Alaknanda, Dhauli Ganga, and Rishi Ganga is still looming. The incident has wreaked havoc in the upper Himalayas. The death toll has risen to more than two dozens, while more than 200 workers are still missing. Many workers were trapped in the water that filled in the tunnels constructed in the dam projects. The 250-meter long tunnel at Tapovan is blocked by debris, mud, and silt, making rescue operations difficult. Only 80 meters of the tunnel has been cleared so far.
From the 250-meter long tunnel in the environmentally sensitive area, only an 80-meter stretch has been cleared. Workers are trapped in the remaining 170 meter stretch of the tunnel and are afraid to have died of suffocation while earning their livelihood. Is it development or the destruction of the environment and people?
The fragile climate of Uttarakhand
The incident had refreshed the traumatic memories of the June 16, 2013 tragedy when the Mandakini river killed more than 5000 people. Uttarakhand's state has been picturesque and blessed with gorgeous natural and invaluable resources such as dense forests, rivers, and high altitude mountains. However, it has a very fragile ecosystem. Hence, the state is prone to natural disasters.
A magnitude 6.8 earthquake hit the district of Uttarkashi in 1991, which killed hundreds of people and destroyed thousands of houses. Another earthquake in 1999 in Chamoli killed hundreds of people. The entire Malpa village of Pithoragarh was devastated by the landslide in 1999, taking the lives of about 250 people. A cloudburst in June 2013 killed thousands of people and left millions stranded in floodwater for days.
Climate activism in Uttrakhand
Earthquakes, cloudbursts, landslides, and massive avalanches, and other natural disasters have been a part of natural processes. Still, the increase in their frequency and depth of intensity is due to human activities. The natives of Uttarakhand are knowledgeable pro of the fragility and eco-sensitivity of Uttarakhand's environment. Thus, they have been trying to save it for a long time.
Mira Behn, an environmental activist of Garhwal, published an article in The Hindustan Times on June 5, 1950, titled 'Something is wrong in the Himalaya' documenting that the flash floods in Uttarakhand are bound to wreak havoc as the sensitivity of the environment is being overlooked during the economic development process. Later, she also wrote to Prime Minister Rajiv Gandhi. It is also pertinent to note that where the incident has taken place is the cradle of the Chipko Movement initiated in 1970 to save trees. Alas! Governments have neither listened to the cries of locals and environmental activists nor learned a lesson from the tragedy of 2013. Still, more than 50 hydroelectric projects are underway on the Alaknanda and Bhagirathi rivers, while a committee of experts warned that such projects are a threat to the state.
Regarding the incident, Uma Bharti, a senior BJP leader, and former Water Resources Minister, in her time had requested not to build power projects on the Ganga river, and its major tributaries since the Himalayas are susceptible area. Following the catastrophe in Uttarakhand, the government needs to seriously reconsider its stance on building hydroelectric dams in the region. The government should no longer ignore the advice and warnings of experts.
Magsaysay award winner Chandi Prasad said that he had written a letter to the then Environment Minister in 2010 warning about the adverse effects of a hydropower project on Rishiganga. His fear is realized in 2021. He claimed that if his warning had been followed in 2010, then such catastrophe would have been prevented.
Rising temperatures are melting glaciers rapidly. According to a report by the International Center for Integrated Mountain Development (ICIMOD), the glacier's melting has doubled between 1980 and 2005. Suppose the Government of India cuts greenhouse gas emissions in accordance with the Paris Climate Agreement. In that case, one-third of the glaciers of the Himalayan and Hindu Kush mountain ranges will melt by the end of the 21st century. It will be too late by then. Urgent action needs to be taken sooner. This study shows that natural disasters are likely to increase with temperature rise.
An increase in the incidents of natural disasters in India results from climate change, but an increase in their impact is due to overexploitation of natural resources. Center and State governments have been pursuing pro-corporate development by ignoring environmental norms. Uttarakhand is inviting climatic tragedies and destroying its beauty by cleverly flouting the Environmental Impact Assessment Laws to build the 900 kilometers long stretch of Char-Dham road, which passes through an environmentally sensitive area. The construction of this stretch of 900 kilometers has been started by dividing it into 53 small sections. The Environmental Impact Assessment Department's permission is to be sought to construct more than 100 kilometers of road that is passing through environmentally sensitive areas. This road's width is being kept at 12 meters, for which up to 24 meters width of land would be required. Though, European countries have kept the width of the road in the mountains to 8 meters.
In the aftermath of the February 7 catastrophe, Prime Minister Narendra Modi has said that the country stands by Uttarakhand. He has also announced financial assistance to the families of the victims. But such promises and financial aids are in vain for Uttarakhand people due to continuous negligence towards environmental regulations, which led to devastating floods in 2013. Meager lakhs of assistance won't build people their houses and bring back their loved ones. To prevent such emotional and financial losses to people, the government should mend its pro-corporate economic development methods.
The massive loss of life and property resulting from natural disasters gives the Central Government a warning signal to strictly abide by the environmental norms. The Central Government is planning to relax the environmental standards of Mopa Airport (Goa), Iron Ore Mining(Goa), Sand Mining (Uttar Pradesh), Sterlite Copper, Thoothukudi (Tamil Nadu), and Construction Activities in and around Delhi, which have been blocked by the Supreme Court and the National Green Tribunal. Meanwhile, the NITI AAYOG - the government's apex think tank-has commissioned a study that seeks to examine the "Unintended Economic Consequences" of judicial decisions that have hindered and stalled big projects' environmental pretexts.
If mountainous areas of the country, including Uttarakhand and their people, are to be saved, then it is incumbent on the Central Government not to carry out development work in these areas without seeking geologists' and locals' opinions. The government should immediately ban unsustainable development projects in the hilly regions, be it Char- Dham road or hydropower projects. If this does not happen, the people of these areas will be forced to endure disasters like sliding mountains and glaciers in the future. The people, living in the hilly regions and country large demand that the Central Government should adopt a pro-people and pro-nature development model instead of a pro-corporate economic development model.
Gurinder Kaur is Professor, Department of Geography, Punjabi University, Patiala, and Visiting Professor at Impact And Policy Research Institute, New Delhi, India;
Arjun Kumar is Director at Impact And Policy Research Institute, New Delhi, India
IndraStra Global is now available on
Apple News, Google News, Flipboard, Feedburner, and Telegram
DISCLAIMER: The views expressed in this insight piece are those of the author and do not necessarily reflect the official policy or position of IndraStra Global.
Climate Environment India Natural Disasters Think Tank
IndraStra Global: Pro-Corporate Development Model ruining Uttarakhand
https://1.bp.blogspot.com/-vC4ao-_zIco/YClA6VcXYUI/AAAAAAAAG3Q/CixrOUX5gPsTbUstBl4nfWTvgMgsixJRwCLcBGAsYHQ/w640-h320/AIDN007012021%2B%252863%2529.gif
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https://www.indrastra.com/2021/02/Pro-corporate-Development-Model-.html


text:
Nov JAN Mar
A Question of Coverage:
The Beck Song Reader As Fan-Performed Art
The Year's Best Coverfolk, 2012: The Singles
(b-sides, deep cuts, YouTube one-offs, & more one-shot coverage)
The Year's Best Coverfolk, Vol. 1:
Tribute Albums and Covers Collections (2012)
Christmas Coverfolk, 2012:
New Tracks from Old Friends
Griefsongs: A Prayer For Newtown
Chanukah Coverfolk, 2012:
with songs by Woody Guthrie, South Park, Peter Paul & Mary, & more!
Christmas Cheer Coverfolk: Seasonal Songs of Drinking
Christmas Coverfolk, 2012: New Holiday Compilations
A Very Kidfolk Christmas CONTEST:
Win Renee & Jeremy's new holiday EP and covers album!
Give A Little Bit: On Buying Local in a Global World
(A Cover Lay Down Holiday Gift Guide)
Renee & Jeremy
Category: welcome
September 29th, 2007 — 08:54 pm
Welcome to Cover Lay Down, a new music-posting blog devoted to the art of the cover song.
Sure, there are plenty of other cover blogs out there, but I think there's still room in the niche. Specifically, though I must confess some dozen gigabytes of my currently full-to-the-gills pre-video iPod are covers from across the vast genre spectrum, here at CLD we're folkies first and foremost, and — despite the fact that folk music could be said to have invented the very premise of songsharing and covering — I've noticed a dearth of both covers of folk songs, and folk covers of non-folks songs, cropping up on the usual coverblog suspects.
Don't get me wrong — I love those guys. I'm not knocking Copy, Right's fondness for the eighties, or Fong's incredible ability to compile cross-genre coverlists the likes of which I've never seen. And the broader tendency in the blogosphere towards promoting what I like to think of as the indie music mutual admiration society produces great collections — for example, my wonderful playlist of several dozen covers of Feist's Mushaboom. I love this stuff in any flavor, visit the "other cover blogs" at least once a week, and could not stand here before you today were it not for those giant shoulders muscling their way forward over the past few years.
But I hope to embark herein on a journey that focuses on the acoustic and the stripped down, the singer-songwriter paying tribute to their influences, and the influenced paying tribute to the old folk standard.
There will surely be some duplication — this would be the place to find Nina Gordon's folk cover of Straight Out of Compton, were it not already out there — and there will surely be the usual genre-stretching, especially since the folk realm overlaps so much with the rest of the music world these days.
But I think there's a new angle here, and I'm ready to share.
So let's get on to the music, shall we?
751 comments » | cover lay down, covers, welcome


text:
El municipio de Bolnisi (, ) es un municipio de Georgia perteneciente a la región de Kvemo Kartli. Su capital es la ciudad de Bolnisi.

En 2002 la población del municipio era de 74 301 habitantes, de los cuales 26,82% eran georgianos y 65,98%, azeríes. En 2014 residían en el municipio 53 590 personas.

Subdivisiones
El municipio incluye 1 ciudad — Bolnisi —, 2 asentamientos de tipo urbano (, daba) — Kazreti y Tamarisi — y 45 unidades administrativas rurales:

Referencias

Bolnisi


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The Studio Photography Intro Course requires some prerequisite knowledge. It is not suitable for beginners who are still learning how to use their camera and are still shooting in Auto as we will be shooting in Manual Mode and discussing lighting setups and techniques.
If you understand the "exposure triangle" and how to use the Shutter Priority (S/Tv), Aperture Priority (A/Av), and Manual (M) modes on your camera, then you will meet our knowledge prerequisite for the Studio Photography Intro Course. If you are unsure of your level please contact us and we will ask you a few questions to gauge your level.
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text:
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The conclusion goes here.

\ifCLASSOPTIONcompsoc

  \section*{Acknowledgments}
\else

  \section*{Acknowledgment}
\fi

The authors would like to thank...

\section{Introduction}\label{Introduction}

The legacy power grid has worked admirably for over a century under the notion that electricity access is a human right \cite{worldbankReport}. This notion makes the power system operators often overlook the opportunities brought by \textit{extensively} exploiting the value of customers. The challenge also comes from the large volume of customers in the system. It is simply too overwhelming for the operator to design customized pricing schemes for all end users, especially with time varying energy consumption patterns. At its core, this is a big data issue.

Powered by the wide deployment of smart meters, the advanced data analytic methods and machine learning algorithms ease the process of customized pricing by providing promising solutions of using big data to inform better price decisions to the system operator.

To exemplify this solution for the electricity sector, in this paper, we study a microgrid scenario, where the microgrid operator sets the real time price according to the marginal generation cost. Inspired by Yu $\textit{et al.}$ in \cite{Yang2017Good}, we define a metric $MCI$, based on the individual load profiles, to reflect each user's value in the microgrid operation. This metric allows us to employ $k$-means clustering algorithm to design the customized pricing scheme. Unlike most previous studies, which conduct the $k$-means clustering based on user's load profile, we adopt the notion of end-to-end machine learning, which yields a robust data-driven pricing scheme. Figure \ref{framework} visualizes the structure of this work.

\begin{figure}[!t]
\centering
\includegraphics[width=2.5 in]{framework.pdf}
\caption{Structure in Designing the Robust Pricing Scheme
\label{framework}
\end{figure}

\subsection{Literature Review}

To the best of our knowledge, we are the first to design a robust data-driven profile-based pricing scheme for the electricity sector. We identify three groups of closely related literature.

The first related research direction investigates using machine learning techniques for better power system operation, mostly with a focus on load prediction. Just to name a few, Kong $\textit{et al.}$ exploit the temporal characteristics in residential load and propose an LSTM (Long Short-Term Memory) load forecasting method in \cite{Kong2017Short}. Many deep learning models have also been employed for more accurate load prediction, including pooling-based deep recurrent neural network in \cite{Shi2018Deep}, FFNN (Feed-Forward Neural Network) in \cite{Wei2019Prediction}, DBN (Deep Belief Network) in \cite{Dedinec2016Deep}, etc. Chen $\textit{et al.}$ seek to integrate domain knowledge in different neural network building blocks in \cite{Chen2018Short}. Researchers have also used machine learning to solve scheduling or dispatch problems for electricity market. For example, Mocanu $\textit{et al.}$ propose a deep reinforcement learning method to conceive an online optimization for building energy management systems in \cite{Mocanu2017On}.

Another line of relevant research exploits opportunities in designing better pricing schemes for demand side. Based on utility maximization, Samadi $\textit{et al.}$ propose an optimal real-time pricing algorithm for demand side management in \cite{Samadi2010Optimal}. By combining hybrid particle swarm optimizer with mutation algorithm, Xu $\textit{et al.}$ design a data-driven pricing scheme to help the utility minimize peak demand in \cite{Xu2017Data}. Exploiting price elasticity of electricity consumption, Yu $\textit{et al.}$ develop a parametric time-utility model to obtain the optimal real time price and maximize the social welfare in \cite{Yu2012A}. Qian $\textit{et al.}$ develop an SAPC (Simulated-Annealing-based Price Control) algorithm to reduce the peak-to-average load ratio via a two-stage optimization in \cite{Li2013Demand}.

Lastly, the robustness of clustering algorithms has also been well investigated. For example, Georgogiannis $\textit{et al.}$ use outlier detection to conduct the robustness analysis for quadratic $k$-means in \cite{georgogiannis2016robust}. Alok $\textit{et al.}$ overview the cluster validity measures and analyze the robustness of such indices in \cite{Validity}. Yan $\textit{et al.}$ investigate the consistency and robustness of two kernel-based clustering algorithms: semidefinite programming relaxation for kernel clustering and kernel singular value decomposition in \cite{yan2016robustness}. Xu $\textit{et al.}$ design a robust and sparse fuzzy $k$-means clustering algorithm by incorporating a robust function to handle outliers in \cite{xu2016robust}. Li $\textit{et al.}$ introduce the concept of robust cluster objective function to enhance the robustness of clustering algorithms in \cite{li2016robust}.

\subsection{Our Contributions}
In pursuit of a robust data driven pricing scheme, the principal contributions of our work can be summarized as follows:

\begin{itemize}
    \item \textit{End-to-End Machine Learning}: We adopt the notion of end-to-end machine learning to design the optimal $k$-means clustering algorithm, which highlights that the vulnerability of many data-driven pricing schemes (e.g., the one in our recent work \cite{cui2019vulnerability}) is due to the clustering criteria selection.
    \item \textit{Characterize Robustness}: Following the definitions of strategic behaviors in \cite{cui2019vulnerability}, we propose a smoothness requirement to characterize robustness. This serves as the guideline to design robust data-driven pricing scheme. Surprisingly, although smoothness is a \textit{global} concept, we prove that a $\textit{local}$ criteria for each cluster implies this $\textit{global}$ concept.
    \item \textit{Optimal $k$-means Clustering}: Although $k$-means clustering in general is NP-hard \cite{dasgupta2008hardness}, we exploit the structure of our problem and design a greedy algorithm to conduct $k$-means clustering with the smoothness requirement. We further prove that to achieve the smoothness requirement, the greedy algorithm is optimal in terms of minimizing the number of clusters.
\end{itemize}

The rest of the paper is organized as follows. We introduce our system model and identify each user's marginal system cost impact ($MCI$) in Section \ref{SystemModel}. Then, Section \ref{Vulnerability} examines the economic loophole created by common $k$-means clustering. Inspired by such vulnerability analysis, in Section \ref{Robustness}, we define robustness and propose that adopting the notion of end-to-end machine learning leads to a robust scheme: clustering based on $MCI$. We further design the greedy $k$-means clustering algorithm and prove its optimality in Section \ref{RobustPricing}. Simulation studies are conducted in Section \ref{Simulaiton} for further insights into the proposed scheme. Concluding remarks and future directions are given in Section \ref{Conclusion}.

\section{System Model}\label{SystemModel}

\begin{figure}[!t]
\centering
\includegraphics[width=2.5 in]{PriceDemand.pdf}
\caption{Relationship Between Price and Demand
\label{PriceDemand}
\end{figure}

We consider a microgrid of $N$ users where the microgrid energy acquisition is maintained by an operator. Denote the total energy consumption in the microgrid at time $t$ by $L_{t}$. Assume the cost to acquire energy is of a quadratic form, i.e.,
\begin{equation}\label{CostFunction}
\begin{aligned}
C(L_{t})=&\frac{1}{2}a\cdot L_{t}^{2}+b\cdot L_{t}+c,\,\,\,\forall 1\leq t\leq T,\\
s.t. \,\,\,&L_{t}=\sum\nolimits_{i=1}^{N}l_{i}^{t},
\end{aligned}
\end{equation}
where $l_{i}^{t}$ denotes user $i$'s energy consumption at time $t$. In such a model, the microgrid operator could simply set the electricity price $p(t)$ at time $t$ according to the marginal cost:
\begin{equation}\label{price}
p(t)=\frac{\partial{C(L_{t})} }{\partial{L_{t}}}=aL_{t}+b,\,\,\,\forall 1\leq t\leq T.
\end{equation}

\noindent $\textbf{Remark}$: When $N$ is large, the energy consumption in the microgrid forms a perfect competition (i.e. each user is a price taker) and the price determined by Eq. (\ref{price}) is efficient. We adopt such a model in that it also allows us to understand the case when the price taker assumption does not hold, though out of the scope of this paper. Furthermore, the quadratic form of cost functions (i.e. linear form of prices) is a common assumption \cite{martinez2003adapting}, which can be verified by real data. Figure \ref{PriceDemand} suggests that the real time price ($p(t)$) and the system total demand exhibit a linear relationship, using the PJM data \cite{DemandPrice}.

\subsection{Marginal System Cost Impact}
We follow the metric, marginal system cost impact (denoted by $MCI$), first proposed by Yu $\textit{et al.}$ \cite{Yang2017Good} to assess each individual user's system impact.

In the daily operation of the microgrid, the total cost for the microgrid operator can be obtained as follows:
\begin{equation}
    C_{T}(\mathbf{L})=\sum\nolimits_{t=1}^{T}C(L_{t}),
\end{equation}
where $\mathbf{L}=(L_{1},...,L_{T})$. If the operator implements an hourly price, then $T$ is simply $24$ for the daily operation.

The $MCI$ is characterized by each user's own load profile. For each user $i$, denote its daily load profile by $\mathbf{l}_{i}=(l_{i}^{1},...,l_{i}^{T})$. Then, we have
\begin{equation}
\begin{aligned}
MCI_{i}&=\lim_{\Delta \rightarrow 0}\frac{C_{T}\left(\mathbf{L}+\frac{\Delta \mathbf{l}_{i}}{||\mathbf{l}_{i}||_{1}}\right)-C_{T}(\mathbf{L})}{\Delta }\\
&=\lim_{\Delta \rightarrow 0}\frac{\sum_{t=1}^{T}(aL_{t}+b)\frac{\Delta l_{i}^{t}}{||\mathbf{l}_{i}||_{1}}+\left (  \frac{\Delta l_{i}^{t}}{||\mathbf{l}_{i}||_{1}}\right )^{2}}{\Delta }\\
&=\sum\nolimits_{t=1}^{T}(aL_{t}+b)\frac{l_{i}^{t}}{||\mathbf{l}_{i}||_{1}}\\
&=\sum\nolimits_{t=1}^{T}p(t)\frac{l_{i}^{t}}{||\mathbf{l}_{i}||_{1}}.
\end{aligned}
\end{equation}

Note that, by adopting $MCI_{i}$ as the customized daily electricity rate for user $i$, its daily electricity cost is as follows:
\begin{equation}
\begin{aligned}
MCI_{i}\cdot ||\mathbf{l}_{t}||_{1}&=\left (  \sum_{t=1}^{T}p(t)\frac{l_{i}^{t}}{||\mathbf{l}_{t}||_{1}}) \right )\cdot ||\mathbf{l}_{t}||_{1}\\
&=\sum\nolimits_{t=1}^{T}p(t)\cdot l_{i}^{t}.
\end{aligned}
\end{equation}

This is a remarkable result! It shows that $MCI_{i}$, as a customized uniform price, maintains the same system efficiency as the prices determined by the marginal cost (i.e., $p(t)$'s). Moreover, $MCI_{i}$ reflects the true value/cost of user $i$ to the whole microgrid system.

However, we want to emphasize that our subsequent analysis does not rely on the quadratic cost functions. All of our conclusions hold as long as the first and second order derivatives of $C(L_{t})$ exist, and its second order derivative is bounded.

This result again verifies the conclusion in \cite{Yang2017Good} that each individual user's load profile should uniquely determine the user's electricity rate, which serves as the basis for data-driven pricing design.

\subsection{Dataset Overview}\label{DataOverview}
Before diving into the details of discussion on the data-driven pricing, we first provide a brief overview on the datasets, which we will use throughout the paper.

We consider two types of demand profiles, residential users and commercial building users: one with more diversity and the other with an inherent pattern.

Empirical studies have shown that the load profiles across populations show similar patterns \cite{rasanen2010data}. Hence, to enlarge the two datasets for more informative clustering results, we combine the data in different days. More precisely,

\begin{enumerate}
    \item Residential Dataset: We use the Pecan Street dataset \cite{DemandData} with more than $300$ residential load profiles from $2016.1.1$ to $2016.2.10$. We combine all the load profiles (excluding some profiles with missing data) into a single residential dataset of $7,699$ profiles.
    \item Commercial Building Dataset: We use the Energy Plus dataset \cite{BuildingData} with $4,820$ commercial building load profiles across US from $2007.1.2$ to $2007.1.12$. By combining all the data (again, excluding some profiles with missing data) to form a large dataset, we obtain our commercial building dataset of $48,200$ profiles.
\end{enumerate}

As suggested by the definition of $MCI$, besides calculating the total load $L_{t}$, we normalize the profiles in two datasets to highlight the observation that user's load profile uniquely determines its electricity rate. Mathematically, for each user $i$'s daily energy consumption $\mathbf{l}_{i}$, we define its normalized consumption $\mathbf{d}_{i}$ according to the $l_{1}-$norm:
\begin{equation}
\mathbf{d}_{i}=\frac{\mathbf{l}_{i}}{||\mathbf{l}_{i}||_{1}}=\frac{\mathbf{l}_{i}}{\sum_{t=1}^{T}l_{i}^{t}}.
\end{equation}

\section{Vulnerability of Common Clustering}\label{Vulnerability}

One straightforward way for clustering is to conduct the $k$-means clustering based on each individual's load profile. However, since the users in the same cluster do not share exactly the same load profile, this data-driven pricing approach may create loopholes allowing certain users to bypass to other clusters for a better retail price.

In this section, we revisit the definition of disguising in \cite{cui2019vulnerability}, and provide more empirical evidence to support that strategic behaviors do exist under this pricing scheme.

\subsection{Definition of Disguising}

Users may strategically change their load profiles for a better price, especially for those who lie on the boundary of two clusters. They can conduct minimal load profile modifications to switch to another cluster, and potentially for a better price. We define such behaviors as disguising.

We denote the center (load profile) of cluster $j$ by $\mathbf{c}_{j}$, and denote the cluster that user $i$ belongs to by a function $u(i)$. To understand the strategic behavior, we need to examine the effort that user $i$ needs to spend on moving to another cluster $n\neq u(i)$. This effort can be characterized by a scalar $\mu_{i,n}$. If user $i$ can successfully switch to cluster $n$, the following condition holds:
\begin{equation}\label{SwitchCondition}
    ||(1-\mu_{i,n})\mathbf{d}_{i}+\mu_{i,n}\mathbf{c}_{n}-\mathbf{c}_{u(i)}||_{1}\geq ||(1-\mu_{i,n})(\mathbf{d}_{i}-\mathbf{c}_{n})||_{1}.
\end{equation}
Hence, the minimal effort that user $i$ needs to spend for disguising can be solved by an optimization problem:
\begin{equation}\label{MinMove}
\begin{aligned}
\min_{n\neq u(i)}\,\,& \inf \mu_{i,n} \\
s.t.\,\,\,\,&||(1-\mu_{i,n})\mathbf{d}_{i}+\mu_{i,n}\mathbf{c}_{n}-\mathbf{c}_{u(i)}||_{1}\\
&\ \ \ \ \geq ||(1-\mu_{i,n})(\mathbf{d}_{i}-\mathbf{c}_{n})||_{1},\\
&p_{n}< p_{u(i)},
\end{aligned}
\end{equation}
where $p_{n}$ represents the price of cluster $n$, i.e., the $MCI$ for cluster $n$'s center profile $\mathbf{c}_{n}$.

Denote $CR$ to be the minimal objective value to problem ($\ref{MinMove}$). We can formally characterize disguising as follows.

\vspace{0.1cm}
\noindent $\textbf{Definition 1}$: User $i$ has the ability to disguise if
\begin{equation}\label{theta}
    CR_{i}\leq \theta.
\end{equation}

\noindent $\textbf{Remark}$: In this \textit{parametric} definition, the user's strategic behavior can be measured through a threshold $\theta$. We also want to emphasize that by considering the load profile modification based on the normalized profile, we intentionally ignore user's possibility of disguising via reducing energy consumption, which allows us to focus on understanding the potential of shifting load profile for disguising.

\subsection{Empirical Evidence: Clustering Results}

We first conduct the $k$-means clustering for the two datasets. We choose a small $k$ for better illustration of the subsequent analysis on strategic behaviors. Fig \ref{ClusterResult}\subref{UserCluster} and \ref{ClusterResult}\subref{BuildingCluster} visualize the clustering results for the two datasets, respectively.

\begin{figure}[!t]
\centering
\subfloat[Residential Dataset]{\includegraphics[width=3in]{UserCluster.pdf}%
\label{UserCluster}}
\hfil
\subfloat[Commercial Building Dataset]{\includegraphics[width=3in]{BuildingCluster.pdf}%
\label{BuildingCluster}}
\caption{Demand Profile of Each Cluster Center}
\label{ClusterResult}
\end{figure}

The central profiles show interesting patterns in the two types of users. For example, cluster $8$ in Fig. \ref{ClusterResult}\subref{UserCluster} and cluster $4$ in Fig. \ref{ClusterResult}\subref{BuildingCluster} seem more like the classical load pattern with $2$ peaks: one in the noon time and the other at night. We can observe that there are many more types of users, including those who are more active at night (e.g. cluster $11$ in Fig. \ref{ClusterResult}\subref{UserCluster}, and cluster $22$ in Fig. \ref{ClusterResult}\subref{BuildingCluster}).

Note that, although the size of the building dataset is almost six times that of the residential user dataset, it leads to fewer clusters. Even within the $24$ clusters, one may observe the inherent pattern of commercial buildings. This observation corresponds to our descriptions for the two datasets in Section \ref{DataOverview}. Residential load profiles are more heterogeneous while commercial building profiles exhibit more homogeneity.

\subsection{Empirical Evidence: Strategic Behaviors}

To quantify the existence of strategic behaviors, we can define the number of users, having the ability to disguise, in each cluster $n$ as follows \cite{cui2019vulnerability}:
\begin{equation}\label{N}
N_{n}(\theta):=\sum\nolimits_{i,u(i)=n}I(CR_{i}\leq \theta),
\end{equation}
where $I(\cdot )$ is the indicator function.

\begin{figure*}[!t]
\centering
\subfloat[Residential Users]{\includegraphics[width=3in]{UserNPercent.pdf}%
\label{UserNPercent}}
\hfil
\subfloat[Commercial Building Users]{\includegraphics[width=3in]{BuildingNPercent.pdf}%
\label{BuildingNPercent}}
\caption{Percentage of Strategic Users Evolving with $\theta$}
\label{NPercent}
\end{figure*}

Figure \ref{NPercent} plots how the percentage of potential strategic users varies with $\theta$. Figure \ref{UserNPercent}(i) and \ref{BuildingNPercent}(i) demonstrate the statistical trends for the two datasets, which are quite intuitive. With a relaxed definition of disguising (larger $\theta$), more users can become strategic. Yet, it is interesting to note that, in both datasets, users can disguise only if they change at least $0.8\%$ of their load profiles.

We show more results in the subfigures of Fig. \ref{NPercent} to highlight the heterogeneity in clusters. There are rather stable clusters, e.g. cluster $11$ in the commercial building dataset (as shown in Fig. \ref{BuildingNPercent}(ii)). And there are also rather unstable ones, e.g. cluster $28$ in Fig. \ref{UserNPercent}(iv), and cluster $22$ in Fig. \ref{BuildingNPercent}(iv).

\section{Robust Data-Driven Pricing: Theory}\label{Robustness}

To understand why strategic behaviors exist in the straightforward $k$-means clustering, we evaluate user's potential benefits of disguising.

Suppose user $i$ has disguised itself to be a cluster $n$ user, with a disguised profile $\mathbf{\widetilde{d}}_{i,n}$, where
\begin{equation}
\mathbf{\widetilde{d}}_{i,n}=((1-\mu_{i,n})\mathbf{d}_{i}+\mu_{i,n}\mathbf{c}_{n}).
\end{equation}

Then its potential benefit can be measured by the difference in electricity bills $b_{i,n}$:
\begin{equation}
    b_{i,n}(\mu_{i,n})=p_{u(i)}\|\mathbf{d}_{i}\|_{1}-p_{n}\|\mathbf{\widetilde{d}}_{i,n}\|_{1}.
\end{equation}
Recall that $p_{n}$ denotes the electricity rate for users in cluster $n$. Note that, since the load profiles have been normalized, $b_{i,n}$ measures the unit benefit of disguising. Mathematically, since
$\|\mathbf{d}_{i}\|_{1}=\|\mathbf{\widetilde{d}}_{i,n}\|_{1}=1$, we have
\begin{equation}
    b_{i,n}(\mu_{i,n})=p_{u(i)}-p_{n}.
\end{equation}

Hence, each user's maximal potential to disguise, denoted by $b_{i}$, can be defined as follows
\begin{equation}\label{maxbenefit}
    b_{i,n}(\mu_{i,n})=\max_{n\in \mathcal{K}_{i},n\neq n(i)}\{p_{u(i)}-p_{n}\},
\end{equation}
where $\mathcal{K}_{i}$ denotes the set of clusters that user $i$ can successfully disguise itself with $CR_{i}\leq\theta$.

\subsection{Definition of Smoothness}
The potential benefits of disguising in Eq. (\ref{maxbenefit}) motivate us to define smoothness to ensure a robust data-driven scheme.

\vspace{0.1cm}
\noindent $\textbf{Definition 2}$: The $k$-means clustering is $\delta-$smooth if for any user $i$ and its associated disguising set $\mathcal{K}_{i}$, the following condition holds:
\begin{equation}\label{deltasmooth}
    |p_{u(i)}-p_{n}| \leq \delta,\forall n\in \mathcal{K}_{i}.
\end{equation}

Obviously, $\delta-$smoothness guarantees the bound of robustness. One straightforward way to ensure such smoothness is to design a supervised load profile based $k$-means clustering with the smoothness requirement.

\subsection{$k$-means Clustering with Smoothness Guarantee}
In this section, we want to show that, although smoothness is a global concept, it can be guaranteed by local properties. More precisely, if a $k$-means clustering could ensure that for each user $i$, its $MCI_{i}$ is close to $p_{u(i)}$, then, this $k$-means clustering is smooth. Formally, we can prove the following theorem:

\vspace{0.1cm}
\noindent $\textbf{Theorem 1}$: Suppose a $k$-means clustering algorithm can guarantee that
\begin{equation}\label{delta}
    |MCI_{i}-p_{u(i)}| \leq \rho, \forall i \in u(i),
\end{equation}
then the clustering is $\rho(1+\frac{1}{1-\theta})-$smooth.

\vspace{0.2cm}
\noindent $\textbf{Proof}$:
Suppose user $i$ successfully disguises itself as cluster $n$ user with a profile $\mathbf{\widetilde{d}}_{i,n}$. The condition in the theorem implies that,
\begin{equation}\label{pn}
\begin{aligned}
&\left|p_{n}-\sum\nolimits_{t=1}^{T}p(t)((1-\mu_{i,n})d_{i}^{t}+\mu_{i,n}c_{n}^{t})\right|\\
=&\left |\sum\nolimits_{t=1}^{T}p(t)(1-\mu_{i,n})(c_{n}^{t}-d_{i}^{t})\right|\\
=&|(1-\mu_{i,n})(p_{n}-MCI_{i})|\\
\leq &\rho .
\end{aligned}
\end{equation}

Hence, for any cluster $n\in \mathcal{K}_{i}$, we have
\begin{equation}
\begin{aligned}
|p_{n}-p_{u(i)}|\leq &|p_{n}-MCI_{i}|+|MCI_{i}-p_{u(i)}|\\
\leq & \frac{\rho}{1-\mu_{i,n}}+\rho\\
\leq &\rho \left ( 1+\frac{1}{1-\theta} \right ).
\end{aligned}
\end{equation}
The second inequality is due to Eq. (\ref{pn}) and the last inequality is due to the parametric definition of disguising. Thus, we complete the proof.
\hfill$\blacksquare$
\vspace{0.1cm}

The only remaining handle is to design an efficient $k$-means clustering algorithm with smoothness guarantee.

\section{Robust Data-Driven Pricing: Implementation}\label{RobustPricing}

In this section, we first introduce the most straightforward supervised $k$-means clustering implementation, and then propose our greedy algorithm. To our surprise, although the general $k$-means clustering is NP-hard, the proposed greedy algorithm is efficient and optimal.

\subsection{Supervised $k$-Means Clustering}

Given the outcome of profiled based $k$-means clustering (see the algorithm in \cite{cui2019vulnerability} for an instance), we can modify the outcome (details are provided in Algorithm $1$) to ensure the smoothness requirement. We term this algorithm as S$k$C for short.

\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}

\begin{algorithm}[!ht]
\caption{Supervised $k$-means Clustering (S$k$C)}
\begin{algorithmic}[1]
\REQUIRE User data set $C=\left \{ i|i=1,...,n \right \}$, price data $p(t)$
\ENSURE Clusters $C_{1},...,C_{k}$
\STATE $m=\min_{i} MCI_{i}$
\STATE $M=\max_{i} MCI_{i}$
\IF {$M-m < 2 \rho$}
\RETURN $C$ as a single cluster
\ELSE
\STATE $C_{1}=\varnothing $, $C_{2}=\varnothing $
\FOR{$i=1$ to $n$}
\IF {$|MCI_{i}-m|\leq |MCI_{i}-M|$}
\STATE $C_{1}=C_{1} \cup \left \{ i \right \}$
\ELSE
\STATE $C_{2}=C_{2} \cup \left \{ i \right \}$
\ENDIF
\ENDFOR
\ENDIF
\IF{$|\max_{i\in C_{1}}MCI_{i}-\min_{i\in C_{1}}MCI_{i}|>2 \rho$}
\IF{$|\max_{i\in C_{2}}MCI_{i}-\min_{i\in C_{2}}MCI_{i}|>2 \rho$}
\RETURN $S$k$C(C_{1})$ and $S$k$C(C_{2})$
\ELSE
\RETURN $S$k$C(C_{1})$ and ($C_{2}$ as a single cluster)
\ENDIF
\ELSIF{$|\max_{i\in C_{2}}MCI_{i}-\min_{i\in C_{2}}MCI_{i}|>2 \rho$}
\RETURN $S$k$C(C_{2})$ and ($C_{1}$ as a single cluster)
\ELSE
\RETURN $C_{1}$ and $C_{2}$ as two clusters
\ENDIF
\end{algorithmic}
\end{algorithm}

This algorithm is commonly known as bisecting $k$-means clustering. Hence, S$k$C is a combination of load profile based clustering and bisecting clustering. While it obviously satisfies the local property to ensure smoothness, it may produce significantly more clusters than that needed.

\noindent \textbf{Remark}: In fact, we don't need to design such a complicated supervised $k$-means clustering. We should re-consider what should be the clustering criteria.

We adopt the notion of the end-to-end machine learning. The ultimate goal of clustering is for pricing. Hence, instead of conducting the $k$-means clustering based on load profiles, we can directly conduct the clustering based on each individual's $MCI$.

The shift in selecting the clustering criteria is significant. We can now directly guarantee the smoothness requirement with minimal room for market manipulation.

\subsection{Greedy Algorithm}

The idea is very simple. We first calculate the $MCI$ for all users and sort the tuple $(i, MCI_{i})$ in the ascending order of $MCI_{i}$. For notational simplicity, we assume $(i,MCI_{i})$ is already ordered, and the index $i$ simultaneously denotes user $i$ and its rank with respect to $MCI_{i}$. Then, starting from the first user, we use the interval of length $2\rho$ to cover the neighboring users and form the cluster. This procedure can be repeated until all users have been clustered. We provide the detailed algorithm in Algorithm $2$, and term this algorithm as greedy $k$-means clustering (or G$k$C in short).

\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}

\begin{algorithm}[!ht]
\caption{Greedy $k$-means Clustering (G$k$C)}
\begin{algorithmic}[1]
\REQUIRE Ordered tuple $\left ( i,MCI_{i} \right ),i=1,...,n$
\ENSURE Clusters $C_{1},...,C_{\kappa}$
\STATE $i\leftarrow 1$
\STATE $k\leftarrow 1$
\WHILE{$i\neq n$}
\STATE $j=\arg\max_{m}MCI_{m},\,\,\,\,\,s.t. \  MCI_{m}\leq MCI_{i}+2\rho$
\STATE $C_{k}=\left \{ i,...,j \right \}$
\STATE $k\leftarrow k+1$
\STATE $i\leftarrow j+1$
\ENDWHILE
\STATE $\kappa=k$;
\RETURN $C_{1},...,C_{\kappa}$
\end{algorithmic}
\end{algorithm}

\subsection{Optimality of G$k$C}
This greedy algorithm achieves the minimal $\kappa$ to ensure the clustering criteria. Formally,

\vspace{0.1cm}
\noindent $\textbf{Theorem 2}$: The G$k$C selects the minimal number of clusters to ensure the clustering criteria:
\begin{equation}\label{ClusterCriteria}
    |MCI_{i}-p_{u(i)}|\leq \rho,\forall i.
\end{equation}

\noindent $\textbf{Proof}$:
Suppose the optimal clustering result is with fewer number of clusters $\kappa ^{\circ}$ (i.e., $\kappa ^{\circ}<\kappa$), which also ensures the clustering criteria (\ref{ClusterCriteria}).

Denote this clustering result by $\pi_{\circ}$ and the G$k$C clustering result by $\pi^{*}$. And denote for each cluster $k_{\circ} \in \pi_{\circ}$ ($k^{*} \in \pi^{*}$), the element with minimal $MCI$ by $s^{k_{\circ}}$ ($s^{k^{*}}$), and the element with maximal $MCI$ by $e^{k_{\circ}}$ ($e^{k^{*}}$).

Since $\pi_{\circ}$ is optimal, there don't exist clusters $i_{\circ}$, $j_{\circ}$, such that $s^{i_{\circ}}<s^{j_{\circ}}<e^{j_{\circ}}<e^{i_{\circ}}$ or $s^{i_{\circ}}<s^{j_{\circ}}<e^{i_{\circ}}<e^{j_{\circ}}$. That is, the clusters in $\pi_{\circ}$ won't overlap with each other. This can be straightforwardly proved by contradiction. We omit the proof due to space limit.

Hence, the clusters in $\pi_{\circ}$, just as the case for $\pi^{*}$, are also disjoint intervals.

Next, we examine the clusters in $\pi_{\circ}$ and $\pi^{*}$ along the axis of $MCI$. Since they are the clustering results for the same dataset, the first clusters in $\pi_{\circ}$ and $\pi^{*}$ share the same starting point, i.e., $s^{1_{\circ}}=s^{1^{*}}$.

Then, we observe the first difference between $\pi_{\circ}$ and $\pi^{*}$. We claim the first difference cannot be the starting point of some cluster. Otherwise, some profiles are left unclustered.

Suppose the first difference happens at cluster $k_{\circ}$ and cluster $k^{*}$, then there are two possible conditions.

$(1)$ If $e^{k_{\circ}}>e^{k^{*}}$, then due to the construction process of G$k$C, $|s^{k_{\circ}}-e^{k_{\circ}}|>2\rho$, which won't satisfy the clustering requirement.

$(2)$ If $e^{k_{\circ}}<e^{k^{*}}$, then we can simply set $e^{k_{\circ}}=e^{k_{*}}$, $s^{k_{\circ}+1}=s^{k^{*}+1}$.

This forms a new clustering result with the same number of clusters in $\pi_{\circ}$. However, after the adjustment of $(2)$ along the axis of $MCI$, $\pi_{\circ}$ becomes exactly the same clustering result as $\pi^{*}$! This contradicts with our assumption that $\kappa^{\circ}<\kappa$. \hfill$\blacksquare$

\vspace{0.1cm}
\noindent \textbf{Remark}: The time complexity of G$k$C is $O(n\log n)$, which makes our data-driven pricing scheme even more practical. Since an algorithm with such a time complexity can be run even in real time, G$k$C solves the major concern of deploying our scheme: it requires the full knowledge of $p(t)$ to determine $MCI$. This implies that, the consumers are only aware of their electricity rates after the daily energy consumption. With the efficient G$k$C algorithm, based on advanced load forecasting methods, the microgrid operator can inform each user's estimated daily electricity rate in real time.

\section{Simulation Studies}\label{Simulaiton}

In this section, we first compare the performance of S$k$C and G$k$C to demonstrate the optimality of G$k$C. Then, we analyze how different parameters (e.g. $\rho$, $a$, etc) will affect the clustering results. This inspires us to further exploit more insights into the G$k$C results.

Using the fitted price model, we select $a=0.00012$ and $b=-37.38$. According to the total load in system, we obtain the real time price of the day, which helps us to conduct the clustering. We visualize the real time price determined by Eq. (\ref{price}) in Fig. \ref{RTP}.

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{Price.pdf}
\caption{Real Time Price Over a Day
\label{RTP}
\end{figure}

\subsection{S$k$C versus G$k$C}

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{DataK.pdf}
\caption{Efficiency Comparison between S$k$C and G$k$C
\label{DataK}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{DataTimeMeanVar.pdf}
\caption{Running Time Comparison between S$k$C and G$k$C (S$k$C only accounts for the time after obtaining the profile-based clustering results; profile-based+S$k$C measures the total time for the supervised $k$-means clustering).
\label{DataTime}
\end{figure}

While both can guarantee the smoothness requirement, we compare the running time as well as the number of clusters returned by S$k$C and G$k$C. Obviously, due to the heuristic implementation of $k$-means clustering based on load profiles, G$k$C outperforms S$k$C in both aspects.

In Fig. \ref{DataK}, with the increasing size of dataset, G$k$C produces a rather stable number of clustering, around $20$. In contrast, S$k$C produces hundreds of clusters and the number of clusters returned by S$k$C increases dramatically with the increasing size of dataset. Figure \ref{DataTime} compares the running time between S$k$C and G$k$C. Even if we don't take the time consumed by profile based clustering into account for S$k$C evaluation, G$k$C still runs faster than S$k$C.

In fact, as the dataset contains millions of profiles, G$k$C's running time is still $O(n\log n)$ yet S$k$C may suffer from the curse of dimensionality.

To understand why S$k$C produces more clusters than G$k$C, we use Fig. \ref{G$k$CPBCHeatMap} for more insights. Since the initial clustering is conducted based on load profiles, most clusters need to be further decomposed into two or more clusters to ensure the clustering criteria (\ref{ClusterCriteria}). Hence, in Fig. \ref{G$k$CPBCHeatMap}, while the G$k$C only produces $24$ clusters, S$k$C needs to produce at least the same amount of clusters as the number of red colored rectangles.

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{rho05k30HeatMap.pdf}
\caption{Relationship Between G$k$C and Profile-Based Clustering when $\rho=0.5$ (the darker the rectangle, the more users in the rectangle).
\label{G$k$CPBCHeatMap}
\end{figure}

\subsection{Dive into G$k$C}

We first examine the diversity of the load profiles in each G$k$C cluster. We define $\sigma_{k}$ to evaluate the mean distance to the cluster center for each cluster $k$:

\begin{equation}
    \sigma_{k}=\frac{\sum_{i\in C_{k}}||\mathbf{d}_{i}-\mathbf{c}_{k}||_{1}}{|C_{k}|},k=1,..K,
\end{equation}
where $\mathbf{c}_{k}$ denotes the center profile of cluster $k$ and $C_{k}$ denotes the set of users in cluster $k$, i.e., $C_{k}=\left \{ i|u(i)=k \right \}$.

We can observe in Fig. \ref{MeanNum} that the size of cluster seems like a binomial distribution: most users are around the medium $MCI$ in the dataset (the medium user is in cluster $12$). However, the diversity in each cluster has two peaks (cluster $6$ and cluster $19$). This is somewhat surprising. We conduct the profile-based $k$-means clustering for the two peaks for more insights.

Figure \ref{C6Centers} shows that the demand profiles in cluster $6$ are quite heterogeneous. In contrast, the demand profiles in cluster $19$ all have a peak in the evening (corresponding to the peak price in Fig. \ref{RTP}), as suggested by Fig. \ref{C19Centers}. This implies that for high $MCI$ users, since the high $MCI$ is caused by the peak price, their profiles tend to be similar. For these users, the results obtained by clustering based on load profiles and clustering based on $MCI$ are more likely to be aligned with each other. For other $MCI$ users, clustering based on $MCI$ is much more efficient!

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{MeanNum.pdf}
\caption{Number of Clusters Based on Profiles and $\sigma$
\label{MeanNum}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{Cluster6Centers.pdf}
\caption{Clustered Demand Profile of Cluster $6$ Based on $MCI$
\label{C6Centers}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{Cluster19Centers.pdf}
\caption{Clustered Demand Profile of Cluster $19$ Based on $MCI$
\label{C19Centers}
\end{figure}

\subsection{Sensitivity Analysis}
We conclude this section by understanding the impacts of $\rho$ and $a$ on the clustering results. As suggested by Fig. \ref{NumCiusterRho}, the number of clusters produced by G$k$C diminishes quickly with the increasing of $\rho$. This also corresponds to our observation in Fig. \ref{MeanNum}, that the size of cluster distribution seems like a binomial distribution. Hence, the initial increase of $\rho$ leads to a more dramatic decrease in the number of clusters.

On the other hand, a larger $a$, which implies an even higher uniform electricity rate, leads to more clusters. Unlike the nonlinear impact of $\rho$, the impact of $a$ exhibits linearity: to twice the value of $a$ almost doubles the number of clusters.

\begin{figure}[!t]
\centering
\includegraphics[width=3 in]{ClusterRho.pdf}
\caption{Number of Clusters Evolving with $\rho$ for Different $a$
\label{NumCiusterRho}
\end{figure}

\section{Conclusion}\label{Conclusion}
By adopting the notion of End-to-End machine learning, we identify the most efficient clustering criteria to design the data-driven profile-based pricing scheme. We further exploit the structure of the problem and the definition of smoothness, which inspire us to design an optimal and efficient greedy $k$-means clustering to implement the proposed pricing scheme.

This work can be extended in many ways. As we have mentioned in the remark in Section \ref{SystemModel}, it will be interesting to examine the possibility to relax the price taker assumption using a game theoretic analysis. We also intend to examine the temporal characteristics of user load profiles over a longer time span for a more detailed user $MCI$ profile characterization. This can enable the annual pricing plan design for the system operator.

\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


This minimal 4,000 sq ft warehouse space is located in the DTLA Arts District and is ideal for create Photoshoots and Film Projects.
The space offers limited amenities and is ideal for those looking for a raw space to host a self-contained project. The space is vast with an open plan, the white-washed walls and industrial features make this space a blank canvas with flexible set-up options. With a 2nd floor terrace, 20' ceilings, 2 cargo doors concrete floors, secured gated location and 100+ parking spots this space is easily adaptable.
The space is perfect for creative professionals looking for a raw space with multiple textured backgrounds in which to host unique projects.


These resins are Momentive's first generation of ultra low emitting formaldehyde resins. Casco-Resin CR-596L is the next generation product that meets CARB II requirements. They are hardened by the addition of the appropriate cataylist.
Hexion is a leading wood adhesive manufacturer for applications where adhesive performance makes all the difference.


text:
Q: cannot print Spark's RDD[Array] I wrote an implementation for FuzzyCMeans algorithm in Apache Spark's MLlib, but for some reason when I try to print the output, the system stuck.
This is my input:
scala> val data = sc.textFile("data/mllib/kmeans_data.txt")
> data: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1] at textFile at <console>:29
scala> data.collect().foreach{ point =>  println(point)
0.0 0.0 0.0
0.1 0.1 0.1
0.2 0.2 0.2
9.0 9.0 9.0
9.1 9.1 9.1
9.2 9.2 9.2

But when I try to print the output after I run my algorithm, It get stuck:
The size of the output should be 6 (same as input's)
scala> val clusters = FuzzyCMeans.train(parsedData, numClusters, numIterations)
> clusters: org.apache.spark.mllib.clustering.FuzzyCMeansModel = org.apache.spark.mllib.clustering.FuzzyCMeansModel@31934160

scala> val ret = clusters.predictMatrix(parsedData)
> ret: org.apache.spark.rdd.RDD[Array[Double]] = MapPartitionsRDD[17] at map at FuzzyCMeansModel.scala:276
scala> ret.collect().foreach{ point =>  println(point.deep.mkString(" "))
> STUCK.....

}
Even if I try to count the RDD[Array], I get the same result:
val ret = clusters.predictMatrix(parsedData)
ret: org.apache.spark.rdd.RDD[Array[Double]] = MapPartitionsRDD[11] at map at FuzzyCMeansModel.scala:276
scala> ret.count
> STUCK

A: The point where you run collect or count is when Spark starts running your prediction code. So it's not necessarily stuck, it's just working.
Look at the Spark status UI (port 4040) to see what exactly is going on. It will tell you whether progress is being made or not. You can also look at the executor and the driver logs to understand the situation better. You can print stuff from your code and see it in the executor logs to get more insight into what your code is doing.

redpajama_set_name: RedPajamaStackExchange


text:
Peter Lapa-Lilly
Age 25 , Maryland
I was at work that spring morning in April 2012 when a Baltimore County, MD police officer called me to say there had been an "acc—er, incident" involving my 25-year-old son Peter.
The officer asked, "Why would he do this?"
"Do what?" I replied.
"Shoot himself."
Of course, his reasons are now unknowable and no doubt complex. My son had graduated from college with honors a few years earlier, held a good-paying job, and had many friends and family who loved him. The summer before, he and his girlfriend had bought a home together. Around the same time, he purchased a .40 caliber handgun for target shooting and "protection."
He gave no warning of what he was about to do that fateful morning. He wrote her and me notes of love and apology, without any explanation. He loaded his weapon into his backpack and walked a few blocks into a wooded park behind a shopping center.
Then he called 911 to say where they would find his body, his driver's license with the red heart for organ donation, and, he emphasized, his gun. Once he saw the flashing lights of the police vehicles up on the hill, he fired a bullet into his head.
I've learned there is no making sense of the suicide of a bright, apparently healthy young man, but also that his access to a firearm in the home increased his risk of suicide three to five times. Study after study shows that guns are the least survivable means, used in 20,000 suicides each year in the US — just over half of all suicides. And white males, like Peter, make up the overwhelming majority of gun suicides.
But there's hope for those still with us. Researchers have also found dramatically lower suicide rates where guns were not present or were kept unloaded and locked apart from ammunition, especially around impulsive youth. Any obstacle or delay can break the self-destructive impulse, because many suicidal individuals are distraught and ambivalent, acting in the heat of the moment.
Peter was likely depressed, but that condition could have been addressed. Ninety percent of those thwarted in a suicide attempt do not find another way to die; they find a way to live. But sadly, only 15% survive a self-inflicted gunshot wound.
I can't change the facts that my son kept a gun handy and that in a moment of despair, he methodically used it in a way that left him no second chance to reconsider his options. I can't change his choices. What I can do as a grieving Mom is share my story in the hope of helping to save another life.
Dorothy Paugh is a retired naval officer and mother of three who has lost her father and a son to suicide by firearm.
Dorothy PaughPeter Lapa-Lilly 08.16.2016
Boaz Kesley
Matthew Tagle
Kristen Medchill
More than 455,000 people are victims of gun crimes every year in America. The Brady Campaign to Prevent Gun Violence and MyIntent have joined forces to create unique, hand-crafted bracelets that commemorate people who have been killed or injured by gun violence. This site is a repository of stories shared by loved ones and survivors.
Learn more about gun violence
Charles Edward Howard Jr.
Trisha Lynne Babcock
Veronika Weiss
Copyright © 2016 All rights reserved. The Brady Campaign to Prevent Gun Violence.


text:
Former Foreign Currency Innovations Director Joins Lloyds Banking Group
Gary Dyer has joined Lloyds Banking Group in a senior role.
Finance Magnates Staff | Executive Moves ( Executives ) | Tuesday, 09/08/2016 | 08:55 GMT+2 2016-08-09T06:55:46+00:00 2016-08-09T07:04:54+00:00
Gary Dyer has joined Lloyds Banking Group as an Associate Director, Finance Magnates has learned today.
Take the lead from today's leaders. FM London Summit, 14-15 November, 2016. Register here!
Dyer, a banking professional with extensive experience of foreign exchange, forecasting, budgeting and profit and loss management joins Lloyds with over 20 years experience gained in international banking organisations.
Dyer started his career at EBC Amro, moving to Mitsubishi Bank in 1987. He joined Credit Suisse in 1993 where he was tasked with overseeing Cable and Sterling crosses prior to joining IntesaBCI where he was responsible for running books on all the majors.
Dyer also held senior trader positions at Banca Commerciale Italiana and FX Invest, followed by five years at Lloyds TSB which he joined in 2005 with responsibility for managing in-house trading programmes.
Dyer's next role was as a Director at Foreign Currency Innovations Ltd (FCI Markets), a leading online fx platform provider focusing on price and customer satisfaction and is a role he has held for the last six years prior to his latest appointment.
Tags: lloyds banking group


text:
The concept of 'overactive bladder' serves better commercial rather than patient interests
"The overactive bladder syndrome has become an accepted way to simplify a complex array of symptoms and leads people to believe that an overactive bladder is an independent disease in itself. However, the truth is not as simple as this, as there are usually several factors at work explaining the symptoms. This is also one of the reasons why so called overactive bladder medications often do not bring the hoped result," says Kari Tikkinen, MD, PhD, from the HUCS Department of Urology.
The article on overactive bladder syndrome, which was co-written by Tikkinen, who currently holds a senior researcher post at the McMaster University in Canada, and Anssi Auvinen, Professor of Epidemiology from the University of Tampere, was recently published in the European Urology journal. For the article, the researchers systematically reviewed the studies on overactive bladder and the channels through which these studies have been funded.
The authors argue that the symptoms of an 'overactive bladder' ought to be studied individually and not as an ambiguous constellation of symptoms. This way the underlying causes of the symptoms can be better understood and more effective treatments can be developed.
The expression 'overactive bladder' was coined at an industry-sponsored symposium held in 1997. The following year, the FDA approved the first drug for the treatment of 'symptoms of overactive bladder', after which the pharmaceutical industry launched high-profile, worldwide promotional campaigns for drugs aimed at treatment of the syndrome.
According to the current definition, overactive bladder (OAB) syndrome is defined as the presence of urinary urgency with or without urgency incontinence, usually with increased daytime frequency and nocturia in the absence of infection or other obvious pathology.
"The definition is vague and ambiguous because it includes unspecific terms, such as 'usually' and 'with or without', and the unclear expression 'other obvious pathology'," Tikkinen says and continues, "For the pharmaceutical industry this definition is probably quite useful, as it is partly the reason why one medicine can be prescribed to a large number of patients."
Research into overactive bladder has increased significantly over the past ten years and the pharmaceutical industry has invested heavily in it. "It has previously been shown that research funded by commercial actors often ends up unpublished if the results don't serve the interests of the company," Tikkinen points out.
Tikkinen and Auvinen also bring to the fore that in many studies on prevalence of overactive bladder, very mild symptoms have been classified as abnormal.
"More independent, non-commercially funded research on the subject is needed. There are, in the end, a huge number of people who suffer from urinary urgency and increased urinary frequency, and current treatments are not bringing sufficient relief," Tikkinen says.
European Urology
Dr. Kari Tikkinen
kari.tikkinen@fimnet.fi
/Scientific community/Science careers/Science job market/Industrial sectors/Pharmaceutical industry
/Scientific community/Research programs/Drug research
/Scientific community/Science policy/Research management/Research funding


24 April 19 Everything you need to know about Elite Marble & Granite Inc in Bentonville. Imagine years of enjoyment from quality that stands the test of time and get the job done by Elite Marble & Granite Inc with great expertise and attention to detail in Bentonville. Elite Marble & Granite Inc is here to answer your questions in Bentonville.


100% GRENACHE. SPRING 2018 RELEASE WINE.
"I think the finest vintage to date, the 2015 The Maven is all Grenache (there's a small amount of fruit from James Berry Vineyard) and offers a sensational perfume of flowers, white pepper, cedary spice, and a big core of blueberry and cherry fruit. Seamless, elegant, medium to full-bodied, and beautifully balanced, it's enjoyable today but will have two decades of overall longevity. Hats off to winemaker Scott Hawley for this awesome wine!" - Jeb Dunnuck. 97 Points.
Once again, at 100% Grenache, The Maven showcases the power and beauty of the varietal. From our estate vineyard and the renowned James Berry Vineyard, located in the Willow Creek District of West Paso Robles, we have delivered a wine that is remarkably intense and structured while maintaining Grenache's inherent feminine grace. Fermented in small open top vessels, native yeast transforms the juice into wine. Gentle punch downs and basket pressing at dryness are our only contributions to the process. Once pressed, the wine remained in 400 liter barrels on its lees for nearly 22 months. Only then was it removed from barrel and bottled unfined and unfiltered. We then allowed the bottled wine an additional 8 months to rest prior to release. Enjoy the 2015 Maven in its youth with a little air or cellar with confidence for the next 7 to 10 years.
"Deep, rich and concentrated, with building tannins, this full-bodied beauty is certainly up with some of the finest Grenaches coming out of California. In addition, I struggle to think of another Grenache from California made in this ethereal style at this quality level." -Jeb Dunnuck. 97 points.
Once again Mother Nature bestowed upon us a plethora of incredible Grenache for 2014. As a result we simply opted to stay out of the way and let it shine. For the second year in a row The Maven is 100% Grenache. Visually and aromatically the wine is stunning with a brilliant ruby color and generous bright red fruits and kirsh up front. Chewy tannin, fantastic density and considerable length remind us this is no quaffer, but rather an immense wine that will continue to reveal layer upon layer over the next decade or more.
"Terrific notes of cedar, flowers, spice, and cassis and framboise fruit characteristics give way to a medium to full-bodied, layered, elegant Grenache from Hawley that does everything right. It has the minerality, as well as the structure, that defines this cuvee, and will keep for another 10-15 years." -Jeb Dunnuck. 94 points.
For the 2013 vintage, Mother Nature blessed us with some of the most remarkable Grenache we have produced to date. Therefore, we simply opted to say thank you very much and, for the first time, we bottled The Maven as a pure 100% Grenache. The Maven shows impeccable balance between bright red fruits and undertones of spice and herbs in a package that is silky and round well into the lengthy finish. The wine is showing incredibly well in its youth but, like all Torrin wines, it will continue to develop beautifully over the next decade.


01:37 AM - Rengozu quoted tennesseenavigator in post Tennessee New Construction Home Theater...26'x34'x10' Need help and suggestions.
04:17 PM - Rengozu quoted ereed in post The "Official" Crowson Tactile Motion Actuators Thread.
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text:
House Intelligence Committee Report Blasts Huawei, ZTE as Threats to U.S....
China's Huawei and ZTE Grilled By U.s. Committee Over Spying Concerns
Huawei Still Eyes Developed Markets From Outside
Huawei has Ongoing Oversight Deal with CFIUS
US charges China's Huawei over alleged Iran sanctions violations
The United States on Monday announced criminal charges against China's Huawei Technologies Co Ltd, escalating a fight with the world's biggest telecommunications equipment maker and coming days before trade talks between Washington and Beijing.
The Justice Department charged Huawei and its chief financial officer, Meng Wanzhou, with conspiring to violate US sanctions on Iran by doing business with Tehran through a subsidiary it tried to hide and that was reported on by Reuters in 2012 and 2013.
In a separate case, the Justice Department said Huawei stole robotic technology from T-Mobile US Inc. Huawei has said the two companies settled their disputes in 2017.
Meng, the daughter of Huawei's founder, was arrested in Vancouver on Dec. 1. China subsequently arrested two Canadians on national security grounds.
Meng, who has been on monitored bail, is set to appear in a Canadian court on Tuesday to discuss changes to her bail terms, according to British Columbia Supreme Court schedules.
Canadian public broadcaster CBC on Tuesday said Canada has received a formal extradition request, citing Canada's Justice Department.
China's Foreign Ministry expressed "grave concern" about the charges and urged the United States drop the arrest warrant and end "unreasonable suppression" of Chinese companies.
[ Cut through hype to find out which blockchain pilots are showing enterprise promise and where blockchain is poised for a big-business breakout. | Get the latest on emerging technologies and digital transformation by signing up for our CIO newsletters. ]
Huawei did not respond to a Reuters request for comment.
US Commerce Secretary Wilbur Ross said the charges are "wholly separate" from the trade negotiations. Yet the development is likely to upset high-level talks between Beijing and Washington this week as part of negotiations intended to walk back trade tensions between the globe's two largest economies.
According to Canada's legal procedures, its Justice Minister will have 30 days from receipt of the extradition request to decide whether to issue an authority to proceed. If he grants it, Meng's case would be sent to the British Columbia Supreme Court for an extradition hearing, which could take weeks or months.
US authorities accuse Meng of playing a lead role in the scheme to use a subsidiary to conduct business in Iran in violation of US sanctions against Tehran. Meng has said she is innocent.
Huawei troubles
Huawei, whose equipment includes base stations, switches and routers, derives nearly half of its total revenue outside China.
However, the Trump administration is trying to prevent American companies from buying Huawei gear and pressing allies to do the same. US security experts are concerned the equipment could be used to spy on the United States.
Its founder, Ren Zhengfei, denies Huawei's products would be used by the Chinese government to spy.
News of the charges against Huawei and Meng marks a serious blow to the company's global ambitions. It has been battling against piling security concerns as Western governments including Australia and New Zealand followed the US lead in restricting its market access over the past year.
Huawei is the world's leading provider of 5G technology. It has said it has won 30 5G contracts globally - more than any of its competitors - including 18 in Europe, three in Asia-Pacific and nine in the Middle East.
It is unclear how the US charges would impact Huawei's business. Chinese peer ZTE Corp, which was similarly charged, was slapped with a devastating supply chain ban last year that prevented it from buying essential components from US companies.
ZTE only resumed normal business after paying up to $1.4 billion in fines and replacing its entire board, on top of a near $900 million penalty paid in 2017.
China tensions
President Donald Trump said in December he could intervene in Meng's case if it would serve national security interests or help close a trade deal with China.
Acting Attorney General Matthew Whitaker said the alleged criminal activity at Huawei "goes back at least 10 years and goes all the way to the top of the company. The charges against Meng and Huawei cite stories from Reuters in 2012 and 2013 that said Huawei's Skycom unit had sought to sell goods to Iran in violation of US law.
The indictment noted that the denials from Huawei in the stories were relied upon by financial institutions "in determining whether to continue their banking relationships with Huawei and its subsidiaries."
Also according to the indictment, in July 2007, the FBI interviewed Huawei founder Ren and US authorities said he falsely told them Huawei did not violate US export laws.
In August 2013, Meng met with an executive from one bank, which is not identified in the indictment. Sources said the bank is HSBC Holdings PLC, which paid $1.92 billion in 2012 for violating US anti-money-laundering and sanctions laws.
During the meeting, Meng used a PowerPoint presentation that misrepresented Huawei operations in Iran and ownership and control of Skycom, according to the indictment.
If the banks knew about Huawei's violations of Iranian sanctions, they would have "reevaluated their banking relationships with Huawei," which included US dollar and euro clearing.
The Justice Department has confirmed that HSBC is not under investigation in this case, HSBC said in a statement last month.
FBI Director Christopher Wray said the Huawei cases, which were filed in New York and Washington state, "expose Huawei's brazen and persistent actions to exploit American companies and financial institutions, and to threaten the free and fair global marketplace."
He also said he is concerned about Huawei devices in US telecommunications networks. "That kind of access could give a foreign government the capacity to maliciously modify or steal information, conduct undetected espionage, or exert pressure or control."
(Reporting by Diane Bartz, David Shepardson, Sarah N. Lynch, Karen Freifeld, Chris Bing, Joseph Menn and Andy Sullivan, Sijia Jiang in HONG KONG, Michael Martina and Ben Blanchard in BEIJING; Editing by Bill Rigby, Lisa Shumaker and Christopher Cushing)


This half term, we had a focus on World War II. Through this, we investigated life during this time period for soldiers, women and children.
During this topic, we created a range of art pieces such as: memory boxes, portraits of soldiers to share their emotions and Remembrance paintings.
To link with our local area, we visited The Royal Submarine Museum where we learned about life aboard a submarine during the war time and how women and children were effected back home.
The best part of the day was when a Chinook helicopter landed outside the learning building (where we were eating our lunch) as they were carrying out a practise run. Some of us had never seen a helicopter this close up before! It was amazing!


Acting on behalf of private investors, Barker Storey Matthews have completed the sale of 34 Broad Street, Stamford for just under £150,000.
The property, comprising a Grade II listed building in the town centre providing 1,031 sq ft of offices split over lower and upper ground floors, attracted a good level of interest reflecting the relatively rare opportunity to acquire freehold office space. It was acquired by an owner occupier relocating a business from elsewhere in Stamford.
Cameron Park of Barker Storey Matthews' Peterborough office commented "Stamford remains a very desirable location and the sale at asking price demonstrates the comparatively strong local demand".


If you are at all familiar with our product range and current raison d'être, you'll know that we produce a range of cocktail products in our Hertfordshire kitchen that we make available to fixed cocktail bars, as well as to temporary event bars in an attempt to help folks serve inventive, best-selling cocktails quickly and consistently.
What you might not know is that we often lend a hand with other cocktail products that are not part of our range ordinarily. This is hopefully a by-product and happy consequence of the delicious things that leave our kitchen each week. The remit is often the same. Bar operators need a hand getting cocktails out quickly and consistently. This helping hand often comes in the form of advice and guidance on recipe development, but more and more, we're asked to produce bespoke products for short-term events and for fixed bars.
Last week for example, we prepared 180 litres of Spice Mix that went on to spice 100's of litres of tomato juice for Bloody Mary's. This allowed for the creation of 1,000's of Bloody Mary's which would have been really difficult to produce consistently, at speed, and within a short time frame.
We also do a lot of work with Be At One bars and our core range of blends feature strongly on their menus helping deliver some of their best selling cocktails at speed, helping them achieve revenue per bartender statistics that are pretty unheard of in the drinks industry.
Aside from the core products that Be At One buy from us, we also prepare a Summer Pear blend that we creatively exclusively for them, following a brief they were working towards for a menu drink. This features in a popular cocktail called the Golden Goose.
Another drink we help Be At One prepare is their Toast & Butter Daiquiri. This involves fat washing Bacardi rum and blending it with a handmade toast (!) syrup. I'll blog about that separately as it's worth exploring in greater detail.
In mid-August, Zane and I went along to meet Neil and Matt at Be At One's development lab in Monument. The reason for the meeting was for Neil to walk us through some new drinks they have developed for their upcoming menus. Some of these drinks involve preparation of ingredients that are both time consuming, and not very tolerant of mistakes in terms of process. Following this meeting, there is every chance that we'll be producing 5 or 6 additional products for them later this year. This arrangement means that Be At One can continue to push boundaries and be increasingly ambitious and inventive with their serves, and some of the preparation involved. They will also save on man hours as a result of much of their bar preparation being taken care of offsite by an operation and group of people that know cocktails and understand the obstacles that they face. Because of our expertise and track record, they will have the confidence that we'll respect and follow their process and recipe ensuring 100% consistency, every batch which means consistent drinks across their national chain of bars – no matter which bartender prepares the cocktail.
So where realistic and sufficient volume is involved, we have the facility to prepare bespoke products for folks that we work with. Moreover, we welcome the challenge.
Although situations like these are a bit of a diversion from our core business, we relish the opportunities to be involved in more cocktail conversations and we love learning about new processes and ways of working with natural products and bold flavour combinations. As a result, we'll continue to welcome these opportunities.