Statement of Allegations: In the Matter of Zungui Haixi Corporation et al.
1. Zungui Haixi Corporation ("Zungui") completed an initial public offering ("IPO") in Canada on December 21, 2009 and raised total gross proceeds of $39.8 million.1 Zungui's common shares are listed on the TSX Venture Exchange (the "TSX-V") and it is a reporting issuer in all Canadian provinces.
2. Zungui's IPO was carried out in connection with the acquisition of Mengshida Shoes Co., Ltd. ("Mengshida"), a Wholly Foreign Owned Enterprise located in the People's Republic of China (the "PRC"). Zungui conducts substantially all its business and operations through Mengshida and Mengshida's management and all its operations are located in the PRC.
3. Yanda Cai is the Chief Executive Officer of Zungui and along with the Chairman of Zungui, Fengyi Cai, controls the day-to-day operations of Mengshida.
4. On August 22, 2011, Zungui issued a press release announcing that Ernst & Young LLP ("EY"), Zungui's auditor, had suspended procedures with respect to the audit of Zungui's financial statements for the year ended June 30, 2011 pending further action from Zungui. Following the press release, the market price of Zungui shares dropped by over 75% representing approximately a $70 million loss in market capitalization in a single day of trading.
5. As noted below, EY suspended its audit procedures due to concerns related to (i) inconsistencies in bank documents; (ii) issues with the bank confirmation process; and (iii) incorrect VAT invoices supporting purchases from certain of Zungui's suppliers. In light of these issues, EY also raised concerns with respect to Zungui's previously issued financial statements and recommended an independent investigation.
6. These issues were raised with Yanda Cai and Fengyi Cai by Zungui's independent directors and a unanimous resolution was passed by Zungui's Board of Directors to create a Special Committee to investigate the issues raised by EY (the "Special Committee").
7. Despite voting in favour of the resolution to appoint the Special Committee, Yanda Cai and Fengyi Cai failed to cooperate with the Special Committee in addressing EY's concerns and obstructed an independent investigation into those concerns by the Special Committee and KPMG Forensic. This resulted in the resignations of Zungui's independent directors, Chief Financial Officer and auditor (EY) leaving Zungui with no directors or senior officers located in Canada, no independent directors, no audit committee, no Chief Financial Officer and no auditor. Further, on October 28, 2011, Zungui failed to file audited annual financial statements as required by Ontario securities law.
8. Yanda Cai's and Fengyi Cai's conduct was abusive to investors and the capital markets and/or contrary to the public interest.
9. Zungui's common shares are currently suspended from trading on the TSX-V and all Zungui securities are currently subject to a temporary cease trade order made by the Ontario Securities Commission (the "Commission").
Zungui engaged in conduct contrary to the public interest by failing to produce documents requested by Staff.
11. Zungui was incorporated under the Business Corporations Act, R.S.O. 1990 c. B-16 on August 11, 2009. Its registered office is in Toronto, Ontario. Zungui became a "reporting issuer" within the meaning of the Act when a receipt was issued for its IPO prospectus of that date. Its shares traded on the TSX Venture Exchange under the symbol ZUN.
12. Yanda Cai is a resident of the PRC. Yanda Cai is a director and the Chief Executive Officer of Zungui.
13. Fengyi Cai is a resident of the PRC and also maintains a residence in Hong Kong. Fengyi Cai is a director and the Chairman of Zungui. According to Zungui's management information circular dated November 2, 2010, Fengyi Cai beneficially owns or exercises control or direction over 56.8% of the outstanding common shares of Zungui.
14. Yanda Cai and Fengyi Cai are directors and officers of Mengshida and control its day-to-day operations.
15. Zungui is a holding company and conducts substantially all of its business through Mengshida which generates substantially all of Zungui's revenue.
16. Mengshida was founded on January 14, 1992 and its business involves the manufacture and sale of athletic footwear, apparel and accessories as well as casual footwear, to the local Chinese market.
17. According to the historical financial statements filed with the Commission, cash has consistently represented a substantial portion of Zungui's assets.
18. The audited consolidated financial statements for the year ended June 30, 2010 show that Zungui had $85.9 million of cash representing approximately 65% of Zungui's assets. The balance in one Mengshida bank account represented over 90% of the Zungui cash balance as at June 30, 2010.
19. Based on the most recently filed consolidated financial statements for the interim period ended March 31, 2011, Zungui had $65.3 million of cash representing approximately 52% of Zungui's assets.
20. According to the same March 31, 2011 consolidated financial statements, Zungui had $33.3 million in accounts receivable and, together, cash and accounts receivable accounted for 79% of Zungui's assets. The Zungui accounts receivable consist primarily of Mengshida accounts receivable.
21. Mengshida's bank accounts are held at bank branches located in the PRC. Access to Mengshida's bank accounts is controlled by the person in possession of Mengshida's chop (a form of corporate seal). During the material time, Mengshida's chop has been under the control and direction of Yanda Cai.
22. On August 22, 2011, Zungui issued a press release (the "August 22 Press Release") announcing that EY had suspended procedures with respect to the audit of Zungui's financial statements for the year ended June 30, 2011 pending further action from Zungui. The press release noted that EY's suspension of audit procedures will remain in place until Zungui clarifies and substantiates its position with respect to issues pertaining to the current and prior year which EY identified in the course of its audit work, and that EY recommended that the issues identified be addressed by an independent investigation.
23. On August 22, 2011, pending the issuance of the August 22 Press Release, trading of Zungui shares was halted on the TSX-V. Following the issuance of the August 22 Press Release, trading resumed and the market price of the Zungui shares dropped by over 75% to $0.34 per share from the last closing price of $1.47 per share. The halt was reinstated prior to the market open on August 23, 2011.
the Special Committee had sought the cooperation and assistance of Yanda Cai but added that "[i]t is not clear whether such cooperation or the funding necessary for the Special Committee to undertake its investigation will be forthcoming".
25. The Special Committee was comprised of the same independent directors that comprised Zungui's Audit Committee (the "Audit Committee").
In spite of numerous requests for cooperation, there has been no positive response from [Yanda] Cai. The requests for cooperation (which the Board unanimously supported when it resolved to appoint the Special Committee) have included two directors travelling to Hong Kong to meet with Mr. Cai, as well as other email and oral communications. These efforts have not led to the access required to conduct a meaningful investigation or the transfer of funds to Canada to allow the Special Committee to pursue its mandate, nor has there been any indication that such cooperation or funding will be forthcoming. As a consequence, the Special Committee has concluded that it is prevented from fulfilling its mandate and each of the four independent directors (Michael Manley, Patrick Ryan, Margaret Cornish and Elliott Wahle), as well as the Chief Financial Officer, Shelly Gobin, have resigned.
27. EY resigned as Zungui's auditor effective September 23, 2011.
28. Prior to commencing procedures with respect to Zungui's 2011 year end audit, EY informed Zungui's management that it would be implementing changes to its bank confirmation process.
29. However, in July and August 2011, when members of EY's audit team attempted to conduct the bank confirmations on the Mengshida accounts they were not permitted to confirm the accounts as requested. At that time, members of the EY audit team contacted Yanda Cai and he failed to provide the authorization necessary for EY to conduct their bank confirmation process.
30. As noted above, in addition to concerns with the bank confirmation process and the reliability of the bank confirmations received, during the course of their audit, EY identified issues with respect to invalid VAT invoices supporting purchases from suppliers and the authenticity of bank advices and bank statements reviewed during the audit. EY raised these concerns with the Audit Committee.
31. Zungui's independent directors, including members of the Audit Committee and Special Committee (once constituted), repeatedly sought the cooperation of Yanda Cai and Fengyi Cai in addressing EY's concerns. This included requesting that EY be permitted to perform the bank confirmations in the manner requested and that KPMG Forensic be permitted to conduct an independent investigation into the issues raised by EY.
32. As noted above, despite repeated attempts, Yanda Cai and Fengyi Cai failed to cooperate and provide the requested permissions and failed to respond to the valid concerns raised by EY, the Audit Committee and the Special Committee.
33. Further, through his actions Yanda Cai prevented independent verification of Zungui's largest asset: the cash balance in the Mengshida accounts.
34. Zungui's independent directors, including the members of the Audit Committee and Special Committee and Zungui's Chief Financial Officer resigned on September 22, 2011 and EY resigned as Zungui's auditor on September 23, 2011.
35. The conduct of Yanda Cai and Fengyi Cai described above was abusive to investors in Zungui securities and the capital markets and/or contrary to the public interest.
36. Prior to the resignation of Zungui's Chief Financial Officer and independent directors, Staff requested the production of documents and records from Zungui.
37. Many of the documents and records relevant to Zungui's business are located in China and the cooperation of Yanda Cai and/or Fengyi Cai is necessary to facilitate production of the documents.
38. Members of the Special Committee sought the cooperation of Yanda Cai to facilitate production of the documents. This cooperation was not provided.
39. Staff also requested, through counsel for the Special Committee, to speak with Yanda Cai and/or Fengyi Cai and this request was communicated to Yanda Cai.
40. Staff reiterated the request that Yanda Cai and Fengyi Cai contact Staff and assist in the production of documents by letters to Yanda Cai and Fengyi Cai dated October 18, 2011.
41. To date, Staff has not received any communication, directly or indirectly, from Yanda Cai or Fengyi Cai and none of Zungui, Yanda Cai or Fengyi Cai has facilitated production of the documents or records requested by Staff.
42. As a venture issuer, Zungui is required to file audited annual financial statements on or before the 120th day after the end of its most recently completed financial year pursuant to paragraph 4.2(b)(i) of National Instrument 51-102 Continuous Disclosure Obligations.
43. Zungui's audited financial statements for the year ended June 30, 2011 were due on October 28, 2011.
44. Zungui failed to file the audited financial statements on that date and is in default of its filing obligations contrary to Ontario securities law.
45. Zungui is a "reporting issuer" as that term is defined in Ontario securities law and is required to have an audit committee that complies with National Instrument 52-110 Audit Committees ("NI 52-110").
46. As noted above, the independent directors that comprised Zungui's audit committee resigned on September 22, 2011 and, since that time, Zungui has not appointed an audit committee in compliance with NI 52-110 contrary to Ontario securities law.
47. The conduct of Zungui, Yanda Cai and Fengyi Cai (the "Respondents") outlined above has been, and continues to be, abusive to investors and the capital markets and/or contrary to the public interest. At this time, Staff request that the Commission make certain orders in the public interest to, among other things, prohibit the Respondents' participation in Ontario's capital markets.
48. However, Staff's investigation in relation to Zungui (the "Investigation") is ongoing and Staff may bring further allegations and/or seek further orders in respect of the Respondents and/or other parties as a result of the Investigation.
49. Based on the foregoing, Zungui, Yanda Cai and Fengyi Cai breached Ontario securities law and/or acted in a manner that is contrary to the public interest.
50. Such further and other allegations as Staff may advise and the Commission may permit.
DATED at Toronto, November 7, 2011.
1. At the closing on December 21, 2009, 11,500,000 common shares were sold for gross proceeds of $37,375,000. On January 12, 2010, Zungui announced that the underwriters had exercised their over-allotment option and an additional 795,500 common shares had been issued for $2,468,375, bringing the total gross proceeds to $39,843,375.


Findings described as black are often more nearly a very dark gunmetal grey. All are nickel complient.
Black Findings There are 27 products.
5mm jump ring, black colour.
5mm Jump Ring, Black Colour, Pack of 120.
Stretchy Charm Bracelet Base, Black Coloured, Pack of 1.
Adjustable "Bling Ring", Black Colour, Pack of 1.
Large Bolt Ring for 3 Strand Necklace, Black Coloured, Overall Length Approx. 25 mm, Bolt Size Approx. 14 mm Diameter, Pack of 1.


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Tag Archives: Swades
Movie Review: Mohenjo Daro (2016)
Even without context, Mohenjo Daro isn't a very good movie, but it's especially disappointing when considered within the landscape of recent Indian films and with regard to director Ashutosh Gowariker's past achievements.
Gowariker's story takes place in the ancient Indus Valley city of Mohenjo Daro, around 2,000 years B.C. Hrithik Roshan plays Sarman, a nearby farmer with a mysterious connection to the city that he doesn't understand.
Sarman's uncle Durjan (Nitish Bharadwaj) caves to Sarman's relentless begging and allows his nephew to go to the Mohenjo Daro, albeit with warnings about the city's many dangers. At forty-two, Roshan is too old play a character so immature that he opens the "only in case of life or death" package that his uncle gives him as soon as Durjan is out of sight.
When Sarman arrives at the metropolis he finds a place governed by greedy politicians fearful of the merciless senate leader Maham (Kabir Bedi) and his bully of a son, Moonja (Arunoday Singh). Maham orders a tax increase, even as farmers struggle with diminishing yields due to Maham's damming of the river.
Sarman is fed up and ready to head home when, wouldn't you know it, he spots a beautiful woman who makes him change his mind. (Conveniently, everything of import in Mohenjo Daro happens at exactly the right moment.) The woman is Chaani (Pooja Hegde), daughter of the head priest (Manish Chaudhary) and The Chosen One of Mohenjo Daro.
Chaani presents all kinds of problems in the story (none of which are Hegde's fault). Right after Sarman admonishes his buddy and traveling companion Hojo to stop ogling women, Chaani shows up in an outfit that demands ogling. Her backless, floor-length dress has slits all the way up both thighs, a cutout to expose her navel, and a pushup bra. So, it's bad when other men leer at women, but not when Sarman does it?
Then there's the part about Chaani being The Chosen One. A prophesy at the time of her birth decreed that she would make a decision that would usher in a new era for Mohenjo Daro, but she never makes such a decision. She's just a bystander as the people forget about her divine destiny and declare Sarman the savior of Mohenjo Daro.
With very little written or archeological evidence to go by, Gowariker was free to style his version of Mohenjo Daro as he wished. The results are bizarre, not in a fanciful way but in an impractical one. In addition to feathers and several kilos of metal beads, Chaani's elaborate headdress has slices of geodes that hang next to her face. One can only imagine how annoying it must have been for Hegde to have slabs of rock clanking against her cheek in nearly every scene. And don't get me started on helpful city guard Lothar's (Diganta Hizarika) 1980s side-ponytail.
There are weird visual nods to classic Christian stories from Hollywood, too. In flashbacks, Maham is styled like an evil Jesus. Narendra Jha as the crazy prophet Jakhiro looks like Charlton Heston's Moses from The Ten Commandments.
The lack of historical data was an opportunity to create something visually stunning, but Mohenjo Daro just isn't. Worse, it looks really bad when compared to last year's historical epic, Baahubali: The Beginning. In every respect — costuming, CGI, fight scenes, musical numbers — Mohenjo Daro looks like a lackluster version of Baahubali, with a less compelling story.
The bland, obvious plot is perhaps the most shocking element of Mohenjo Daro. Gowariker has a great track record for writing and directing engrossing stories that subtly convey his political ideals. Lagaan had poor, rural Indians literally beating the British at their own game. Swades showed how innovation and dedication to community can circumvent the slow movement of government. Khelein Hum Jee Jaan Sey saw young Indians pushed to violence by oppressive British rule. In Jodhaa Akbar, Roshan played a progressive emperor who embraced multiculturalism.
Gowariker ditches the nuance and character motivations of his previous films for cliched populism. Sarman declares that The People are fed up paying the senate's taxes, and The People cheer in unison, somehow instinctively knowing that this outsider is the savior who can lead them out of poverty, and causing them to forget about the crew of murderous hill goons Maham employs as bodyguards, a la Tyrion Lannister.
It's too easy. The idea that all of India's (or anywhere's) problems could be solved if the masses would rise up as one behind a charismatic leader is lazy and unsatisfying, whether the action takes place in the modern day or thousands of years ago. It absolves the masses of having to do the hard work that was such an important part of Lagaan, Swades, and Khelein Hum Jee Jaan Sey. Just wait around for a messiah — but not the woman we thought was The Chosen One. This other guy instead.
Even the manner in which the story is presented is ham-handed. A. R. Rahman provides a score full of uncharacteristically garish musical cues. The single corniest moment sees one character tell another, "something something something YOUR FATHER," followed by a noisy instrumental blast and a zoom to closeup on the listener's face.
There are also none of the culture-clash elements from Gowariker's previous films present in Mohenjo Daro. Sarman is an outsider, but it's not really a problem. He adapts to life in the city almost immediately, making friends and falling in love without a hitch. Then again, there's not enough to Chaani's character to make her a complicating factor. She's there to look pretty, which Hegde does exceedingly well.
The actors aren't to blame for Mohenjo Daro's shortcomings. No one is particularly good or bad, although I did enjoy Singh's performance as the thwarted heir apparent more than I have some of his past work. This will be one of Roshan's most forgettable roles.
There's not enough substance here to tell if Mohenjo Daro could have been more than it is. It's just the unfortunate product of a talented filmmaker who appears to have lost his way, sublimating his ideals for pandering that pleases no one.
Mohenjo Daro at Wikipedia
Mohenjo Daro at IMDb
My review of Swades
My review of Jodhaa Akbar
My review of Khelein Hum Jee Jaan Sey
My review of Baahubali: The Beginning
This entry was posted in Reviews and tagged 1.5 Stars, 2016, A. R. Rahman, Arunoday Singh, Ashutosh Gowariker, Baahubali: The Beginning, Bollywood, Diganta Hizarika, Hindi, Hrithik Roshan, Indian, Jodhaa Akbar, Kabir Bedi, Khelein Hum Jee Jaan Sey, Lagaan, Manish Chaudhary, Mohenjo Daro, Movie Review, Narendra Jha, Nitish Bharadwaj, Pooja Hegde, Swades on August 15, 2016 by Kathy.
Movie Review: Swades (2004)
Buy or rent the movie at iTunes
Buy the soundtrack at Amazon
Swades is one of the first Bollywood films I watched. At the time, I absolutely loved it. Hundreds of movies later, I wanted to see if it still holds up. Happily, it does.
In short, Swades is the story of a NASA scientist who realizes that the meaning he's been searching for lies not in the stars but in a small village in India. It's about belonging to a community where one can have a dramatic impact on the lives of its members. It's shamelessly inspirational, and effectively so.
Shahrukh Khan gives what is probably my favorite of his performances as the scientist, Mohan. Wracked by guilt for having failed to visit his childhood nanny in India in the twelve years since his parents' deaths, Mohan takes a two-week leave from his weather satellite project to find his nanny, Kaveri Amma (Kishori Balal), and bring her back to the States.
Mohan finds Kaveri Amma now living in a rural village with Gita (Gayatri Joshi), Mohan's childhood friend and herself an adult orphan. Kaveri Amma is an integral member of the community, dispensing childcare tips and looking after Gita's eight-year-old brother, Chikku (Smith Seth), while Gita teaches at the local elementary school.
Kaveri Amma refuses to leave until Gita finds a husband, and Gita refuses to find a husband until she can secure the future of the school, whose building the village council would prefer to use as their own headquarters. Mohan's vacation stretches to five weeks as he helps Gita, falling in love with her in the process. The longer he stays, the more he realizes what a difference he can make in a community where power outages are the norm and the Internet seems like the stuff of science fiction.
Swades is directed by Ashutosh Gowariker, who specializes in long runtimes. Yet, even at 189 minutes, the movie is so well-paced that it never feels slow. Within twelve minutes, Mohan is on his way to India. He learns of Gita's problem with the village council at the hour mark. At two hours, he meets a destitute farmer who goes without a meal so that Mohan, his guest, may be treated according to custom, spurring Mohan to reconsider his plan to return home. New wrinkles appear in the plot at exactly the right times.
Mohan occupies an interesting position in the village. Despite his ethnic heritage and having spent his childhood in Kaveri Amma's care, his years in America have made him an outsider. His advocacy for reform — greater access to education, especially for girls, and integration of the castes — appeals to the more liberal members of the village, but not the conservative council members. With time, Mohan becomes more of a diplomat and less of a dictator.
That process gets at the heart of Swades. Mohan finds his place in a community, using his powers to influence but not to force change. Mohan admits that his parents' deaths closed him off to social opportunities in America. When he finally realizes around age thirty that he wants to belong, all of his peers have married and moved on with their lives, leaving him behind. Moving to India gives him a fresh start.
The theme of belonging is overshadowed by a nationalist tone that is sort of unnecessary, even if it was a popular movie convention of the time. Originally espousing American values like tolerance and ingenuity, Mohan falls blindly under his home country's spell. His decision to stay is scored by the lovely but over-the-top populist song "Yeh Jo Des Hai Tera." He tells his Indian-American co-worker at NASA, "You'll have to come there and see things. Otherwise, you'll never understand."
This turn at the end undercuts Mohan's rationale for returning to India. Rather than leaving NASA to use his skills to help his new friends and loved ones, the movie frames Mohan's return as that of an ethnic Indian succumbing to the irresistible pull of his homeland. It's a nice sentiment, but one that doesn't ring especially true with what we've seen to that point.
That said, such patriotic sentiment is not unique to Swades, and it doesn't diminish the universality of the desire for friendship, love, and a place to belong. Thanks to a terrific soundtrack by A. R. Rahman, touching performances, and a great screenplay — contributed to by a young Ayan Mukerji, who went on to direct Wake Up Sid and Yeh Jawaani Hai Deewani — Swades stands the test of time. It remains one of my favorite Hindi films.
Swades at Wikipedia
Swades at IMDb
A post on the movie's music at Background Score
My review of Wake Up Sid
My review of Yeh Jawaani Hai Deewani
This entry was posted in Reviews and tagged 2004, 4 Stars, A. R. Rahman, Ashutosh Gowariker, Ayan Mukerji, Bollywood, Gayatri Joshi, Hindi, Indian, Kishori Balal, Movie Review, Shahrukh Khan, Smith Seth, Swades on June 11, 2013 by Kathy.
Worst Bollywood Movies of 2009
There are a number of factors I considered when selecting candidates for Worst Bollywood Movie of 2009. Movies featuring racist jokes, such as Kambakkht Ishq and All the Best, obviously made the list. Incoherent plots brought Wanted and De Dana Dan to my attention, whereas Main Aurr Mrs. Khanna ignored traditional story structure and skipped the climax all together.
Ek — The Power of One deserves mention for its ridiculous title, which translates in English to "One — The Power of One".
What's Your Raashee? was easily the biggest disappointment of the year, coming from Ashutosh Gowariker, the filmmaker responsible for great flicks like Lagaan, Swades and Jodhaa Akbar.
2009 was a particularly bad year for Akshay Kumar. In addition to Kambakkht Ishq and De Dana Dan, he also starred in the bland supernatural thriller 8×10 Tasveer. His two other releases during the calendar year, Blue and Chandni Chowk to China, were fine but forgettable.
Because of their spectacular misunderstanding of human emotions and dubious moral messages, I thought about giving the award to either Kal Kissne Dekha or London Dreams. Kal Kissne Dekha suggested that one's value is dependent upon one's ability to save lives via superhuman powers, while London Dreams excused abhorrent behavior so long as it was committed in pursuit of a selfish goal.
But the ultimate winner had to be the most annoying, most cliché-riddled movie of the year, the worst of the worst. And the winner of Worst Bollywood Movie of 2009 is: Do Knot Disturb.
Do Knot Disturb, which deserves the honor based on its stupid title alone, contains all of the bad clichés that dominate Hindi comedies at the moment. The plot is based on a series of misunderstandings which could be clarified if the characters actually had conversations with one another. The jokes are written based on volume instead of quality, under the mistaken belief that what was funny the first time is even funnier the sixth, seventh and eighth time.
Case in point, the high-pitched screaming match between characters played by Govinda and Ritesh Deshmukh. The characters get spooked by something and start shrieking in girlish voices. The gag isn't original, but it's not inherently annoying. But in Do Knot Disturb, the characters scream dialogue at each other in those high-pitched voices for the next ten minutes of the movie.
After one minute, the gag had already stopped being funny. After ten minutes, it was unbearable. I actually walked out of the theater and only convinced myself to return out of a sense of journalistic duty. By virtue of having watched all but one minute of the movie, I can say that Do Knot Disturb is the worst Hindi film of 2009.
Previous Worst Movies List
This entry was posted in Articles and tagged 2009, 8x10 Tasveer, Akshay Kumar, All The Best, Ashutosh Gowariker, Blue, Bollywood, Chandni Chowk to China, De Dana Dan, Ek The Power of One, Govinda, Hindi, Indian, Jodhaa Akbar, Kal Kissne Dekha, Kambakkht Ishq, Lagaan, London Dreams, Main Aurr Mrs. Khanna, Movie Review, Ritesh Deshmukh, Swades, Wanted, What's Your Raashee?, Worst Bollywood Movie on January 3, 2010 by Kathy.
Movie Review: What's Your Raashee? (2009)
What if Stanley Kubrick had directed Mannequin? That's what Ashutosh Gowariker's venture into the realm of the romantic comedy feels like: an auteur squandering his talents in a genre he's ill-suited for.
What's Your Raashee? is a typical masala movie, in spite of being written, produced and directed by Gowariker, who was nominated for an Oscar for Lagaan.
Harman Baweja, stars as Yogesh, a recent MBA grad living in Chicago. He's summoned home to India under the pretense that his father has had a heart attack.
His dad is actually fine. The family just needs Yogesh to get married and collect his inheritance from his grandpa in order to pay off elder brother Jitu's debts. An astrologer predicts that Yogesh will get married in ten days time, so he'd better pick a girl quick.
Despite being smart enough to have earned an MBA from the University of Chicago, Yogesh doesn't try to find another solution to the family's money problems. He just goes along with their absurd scheme, following their command not to tell Grandpa about it, lest he view Jitu unfavorably.
Yogesh and his Uncle Devu cull the list of prospective marriage candidates to twelve women, one from each zodiac sign. All of the girls are played by Priyanka Chopra. Thankfully, Yogesh acknowledges their similarities in appearance, lest he come across as dimmer than he already seems for having agreed to his family's moronic plan in the first place.
Priyanka Chopra deserves a lot of credit for even attempting to play twelve characters in the same movie. That she's able to give them all distinct personalities and physical presences — showcased in a dance scene featuring all twelve characters on screen at the same time — is impressive.
But overall the movie disappoints, falling into the same traps as the worst Bollywood romantic comedies. There are two unnecessary, annoying side plots — one involving Uncle Devu, the other involving some gangsters — that drag the movie out and aren't resolved in a satisfying way despite the movie's nearly 210 minute runtime.
The movie is further dragged out by too many songs. Most of the women get their own song and dance numbers, even some of the girls Yogesh is obviously not going to marry. The routine associated with the Libra character, in which Yogesh plays a robot/puppet, is the worst number I've ever seen in a movie musical, Hindi or English.
There's no reason why a romantic comedy, Bollywood or otherwise, should be more than two-and-a-half hours long. What's Your Raashee? didn't need to be either. Without the annoying side plots and cumbersome dance numbers, the movie would've been an hour shorter, making it an enjoyable if unexceptional movie.
Instead, it's a movie best suited for watching on DVD, with your finger hovering over the fast forward button.
This entry was posted in Reviews and tagged 1.5 Stars, 2009, Ashutosh Gowariker, Bollywood, Harman Baweja, Hindi, Indian, Jodhaa Akbar, Lagaan, Mannequin, Masala, Movie Review, Priyanka Chopra, Runtime, Stanley Kubrick, Swades, What's Your Raashee? on September 29, 2009 by Kathy.


Church Restoration Series: THE LOVELESS CHURCH was systematically discussed by Bro. Gboyega Adedeji during our weekly Disciplers Convocation at the Centre for New Dimension Leadership, Abuja. You will really be blessed!


text:
\section{Introduction}
\label{sec:intro}

Recent years have witnessed a successful adoption of statistical methods for model inversion, emulation and bio-geophysical parameter retrieval~\cite{CampsValls11mc}. Machine learning algorithms are flexible non-parametric models that fit the observations using large heterogeneous data. Machine learning models for parameter retrieval avoid complicated assumptions, provide fast and accurate estimates, and learn the complex relations directly from data.

Current operational vegetation products, like leaf area index (LAI), are typically produced with neural networks, Gross Primary Production (GPP) --as the largest global CO$_2$ flux driving several ecosystem functions-- is estimated using ensembles of random forests, kernel methods and neural networks~\cite{Tramontana16bg}, biomass has been estimated with stepwise multiple regression~\cite{Sarker11}, partial least squares regression is used for mapping canopy nitrogen~\cite{Coops03,Townsend03}, support vector regression~\cite{Smola2004} showed high efficiency in modelling LAI, fCOVER and evapotranspiration~\cite{Durbha07,Yang06}, and kernel methods in general~\cite{ShaweTaylor04,CampsValls09wiley},
and Gaussian Processes (GPs) in particular~\cite{Rasmussen2006}, recently provided excellent results in chlorophyll content estimation among other vegetation parameters~\cite{Furfaro06,CampsValls16grsm}.

There is however an important issue that is often disregarded: statistical models learn input-output mappings from data but very often do not respect the most elemental rules of physics. They often come up with accurate, yet inconsistent predictions. For example, bio-geo-physical parameters are typically estimated with individual, indepdent models which are typically trained separately. This common approach ignores the (potentially nonlinear) cross-relations among variables.
Constraining the estimation problem is known in machine learning as {\em structured-output learning}, and is tightly related to {\em multitask learning}~\cite{Leiva2012,Tuia11structured}. Extension of such models to the regression setting is far from trivial, as the number of constraints increases cubically with the number of samples and outputs. Furthermore, including constraints in the regression models goes beyond consistency of model outputs; one could be interested in preserving some particular characteristics in the predictions, e.g. being independent of some ancillary information, faithful to certain variable ranges, or disregarding some information from the input (spectral) bands, just to name a few. Our notion of {\em consistency} is broad: we posit that a prediction is fully consistent with respect to some sensitive features if and only if the model's predictions are statistically independent of them.

In this work, we introduce a novel statistical regression framework that allows one to incorporate such broad consistency constraints. The framework builds upon \cite{kamishima2013independence,PerezSuay17ecmlpkdd} to minimize a functional that tries to jointly minimize the empirical error and maximize the dependence of the predictions with respect to an external subset of predictors, observations or ancillary information here called {\em sensitive} features. Two models are derived: a linear and a nonlinear, kernel-based, consistent regression model. The new {\em consistency term} trades-off accuracy for consistency, and translates into an extra regularization term that can be easily interpreted. Interestingly, the proposed models come in closed-form analytical solutions, and are very easy to implement, involving only matrix inversions.

The remainder of the paper is organized as follows. Section~\ref{sec:method} introduces notation and reviews the consistent regression models proposed. Section~\ref{sec:exp} gives experimental evidence of performance in
chlorophyll content estimation.
Finally, Section~\ref{sec:conc} concludes the paper with some summarizing remarks.

\section{Proposed methodology}
\label{sec:method}

This section starts by defining the notation and the concept of consistent regression.  The proposed framework for performing consistent regression learning based on cross-covariance operators for dependence estimation in Hilbert spaces is then introduced.

\subsection{Notation and the regularization framework}

We are given $n$ samples of a response (or target) data matrix ${\mathbf Y}\in{\mathbbm{R}}^{n\times c}$, and $d+q$ prediction variables: $d$ driver variables ${\mathbf X}\in{\mathbbm{R}}^{n\times d}$ and $q$ sensitive ${\mathbf S}\in{\mathbbm{R}}^{n\times q}$. The goal is to obtain a generic prediction function (or model) $f$ for the target variable ${\mathbf Y}$ from the input data, $({\mathbf X},{\mathbf S})$. The goal in our framework of {\em consistent learning} is to predict ${\mathbf Y}$ while being maximally independent of ${\mathbf S}$.

A prediction is said to be totally consistent with respect to the sensitive features ${\mathbf S}$ if and only if $\widehat{{\mathbf Y}} \perp {\mathbf S}$. Therefore, two main ingredients are needed to perform consistent predictions: we need to ensure independence of the predictions on the sensitive variables, and simultaneously to obtain a good approximation of the target variables.

The proposed function $f$ tries to learn the relation between observed input-output data pairs $({\mathbf x}_1,{\mathbf y}_1)$, $\ldots,$ $({\mathbf x}_n,{\mathbf y}_n)$ $\in{\mathcal X}\times {\mathcal Y}$ such that it generalizes well (good predictions $\hat{{\mathbf y}}_\ast=f({\mathbf x}_\ast)\in{\mathcal Y}$ for the unseen input data point ${\mathbf x}_\ast\in{\mathcal X}$), and the predictions should be as independent as possible of the sensitive features (variables, auxiliary information or even observations). As such, the following functional should be optimized:
\begin{equation}\label{eq:framework}
{\mathcal L} = \dfrac{1}{n}\sum_{i=1}^n V(f({\mathbf x}_i),{\mathbf y}_i)
                + \lambda~\Omega(\|f\|_{\mathcal H})
                + \mu~I(f({\mathbf x}),{\bf s}),
\end{equation}
where $V$ is the error cost function, $\Omega(\|f\|_{\mathcal H})$ acts as a regularizer of the predictive function and controls the smoothness and complexity of the model, and $I(f({\mathbf x}),{\bf s})$ measures the independence between the model's predictions and the protected variables. Note that one aims to minimize the amount of information that the model shares with the sensitive variables while controlling the trade-off between fitting and independence through hyperparameters $\lambda$ and $\mu$. By setting $\mu=0$ one obtains the ordinary (Tikhonov's regularized) functional, and by setting $\lambda=0$ one obtains the unregularized versions of this framework.

The framework admits many variants depending on the cost function $V$, regularizer $\Omega$ and the independence measure, $I$. For example, in~\cite{Kamishima:2012}, the function $f$ was the logistic regression classifier and $I$ was a simplification of the mutual information estimate. Despite the good results reported in~\cite{Kamishima:2012}, these choices do not allow one to solve the problem in closed-form, nor to cope with more than one sensitive variable at a time, since the proposed mutual information is an uni-dimensional dependence measure. In the following section, we elaborate on this framework by using the concept of cross-covariance operators in Hilbert spaces, which lead to closed-form solutions and permit one to deal with several sensitive variables simultaneously.

\subsection{Consistent Linear Regression}

Let us now provide a straightforward instantiation of the proposed framework for consistent linear regression (CLR).
We will adopt a linear predictive model for $f$, i.e. the matrix of predictions for a test data matrix ${\mathbf X}_\ast$ is given by $\hat {\mathbf Y}_\ast = {\mathbf X}_\ast{\mathbf W}$, the mean square error for the cost function $V = \|{\mathbf Y}-{\mathbf X}{\mathbf W}\|_2^2$ and the standard $\ell_2$ regularization for model weights $\Omega:= \|{\mathbf W}\|_2^2$. Other choices could be made, leading to alternative formulations. In order to measure dependence, we will rely on the cross-covariance operator between the predictions and the sensitive variables in Hilbert space. Let us consider two spaces ${\mathcal Y}\subseteq {\mathbbm{R}}^{c}$ and ${\mathcal S}\subseteq {\mathbbm{R}}^{q}$, where random variables $(\hat{\mathbf y},{\bf s})$ are sampled from the joint distribution ${\mathbb P}_{{\mathbf y}{\bf s}}$. Given a set of pairs ${\mathcal D}=\{(\hat{\mathbf y}_1,{\bf s}_1),\ldots,(\hat{\mathbf y}_n,{\bf s}_n)\}$ of size $n$ drawn from ${\mathbb P}_{{\mathbf y}{\bf s}}$, an empirical estimator of HSIC~\cite{Gretton05} allows us to define
$$I:=\text{HSIC}({\mathcal Y},{\mathcal S},{\mathbb P}_{{\mathbf y}{\bf s}}) = \|{\mathbf C}_{ys}\|_{\text{HS}}^2 = \frac{1}{n^2}\text{Tr}(\tilde{\mathbf Y}^\top \tilde{\mathbf S} \tilde{\mathbf S}^\top\tilde{\mathbf Y}),$$
where $\|\cdot\|_{\text{HS}}$ is the Hilbert-Schmidt norm, ${\mathbf C}_{ys}$ is the empirical cross-covariance matrix between predictions and sensitive variables\footnote{The covariance matrix is ${\mathcal C}_{{\mathbf y}{\bf s}} = {\mathbb E}_{{\mathbf y}{\bf s}}({\mathbf y}{\bf s}^\top) - {\mathbb E}_{{\mathbf y}}({\mathbf y}){\mathbb E}_{{\bf s}}({\bf s}^\top)$, where ${\mathbb E}_{{\mathbf y}{\bf s}}$ is the expectation with respect to ${\mathbb P}_{{\mathbf y}{\bf s}}$, and ${\mathbb E}_{{\mathbf y}}$ is the marginal expectation with respect to  ${\mathbb P}_{{\mathbf y}}$ (hereafter we assume that all these quantities exist).}, $\tilde{\mathbf Y}$ and $\tilde{\bf S}$ represent the feature-centered ${\mathbf Y}$ and ${\bf S}$ respectively, and $\text{Tr}(\cdot)$ denotes the trace operation. We want to stress that HSIC allows us to estimate dependencies between multidimensional variables, and that HSIC is zero if an only if there is no second-order dependence between $\hat{\mathbf y}$ and ${\bf s}$. In the next section we extend the formulation to higher-order dependencies with the use of kernels~\cite{Scholkopf02,ShaweTaylor04}.

Plugging these definitions of $f$, $V$, $\Omega$ and $I$ in Eq.~\eqref{eq:framework}, one can easily show that the solution has the following closed-form solution for weight estimates
\begin{equation}\label{eq:disp_lin}
\widehat{\mathbf W} = (\tilde{\mathbf X}^\top \tilde{\mathbf X} + \lambda~{\bf I} + \frac{\mu}{n^2}~\tilde{\mathbf X}^\top \tilde{\mathbf S} \tilde{\mathbf S}^\top \tilde{\mathbf X})^{-1} \tilde{\mathbf X}^\top {\mathbf Y}, \end{equation}
where consistency is trivially controlled with $\mu$, which acts as an additional regularization term. Also note that when $\mu=0$ the ordinary (regularized) least squares solution is obtained.

\begin{figure*}[t!]
\small
\centerline{
\begin{tabular}{ccc}
\includegraphics[width=5.6cm]{images/chl_L_L_L_corr_hsic_IGARS.png}&
\includegraphics[width=5.6cm]{images/chl_L_L_Lscatter_IGARS_point1.png}&
\includegraphics[width=5.6cm]{images/chl_L_L_Lscatter_IGARS_point2.png}\\
\includegraphics[width=5.6cm]{images/chl_NL_NL_L_corr_hsic_IGARS.png}&
\includegraphics[width=5.6cm]{images/chl_NL_NL_Lscatter_IGARS_point1.png}&
\includegraphics[width=5.6cm]{images/chl_NL_NL_Lscatter_IGARS_point2.png}\\
\end{tabular}
}
\caption{Evolution of the RMSE [$\mu$g/m$^2$] and HSIC for predicting Chl-a with either linear (top) or kernel (bottom) regression as a function of the consistency parameter $\mu$. Scatter plots illustrate the accuracy (blue points, predicted vs. observed chlorophyll content) and consistency (purple points, predicted chlorophyll content vs. sensitive band) for two choices of $\mu$ (low and high consistency correspond to red and green respectively).}
\label{consistency}
\end{figure*}

\subsection{Consistent Kernel Regression}

Let us now extend the previous model to the nonlinear case in terms of the prediction function, the regularizer and the dependence measure by means of reproducing kernels~\cite{Scholkopf02,ShaweTaylor04}. We call this method the consistent kernel regression (CKR) model. We proceed in the standard way in kernel machines by mapping data ${\mathbf X}$ and ${\mathbf S}$ to a Hilbert space ${\mathcal H}$ via the mapping functions $\boldsymbol{\phi}(\cdot)$ and $\boldsymbol{\psi}(\cdot)$ respectively. This yields $\boldsymbol{\Phi},\boldsymbol{\Psi}\in{\mathcal H}\subseteq{\mathbbm{R}}^{d_{\mathcal H}}$, where $d_{\mathcal H}$ is the (unknown and possibly infinite) dimensionality of mapped points in ${\mathcal H}$. The corresponding kernel matrices can be defined as: $\tilde{\bf K}=\tilde\boldsymbol{\Phi}\tilde\boldsymbol{\Phi}^\top$ and $\tilde{\bf K}_S=\tilde\boldsymbol{\Psi}\tilde\boldsymbol{\Psi}^\top$.
Now the prediction function is $\hat {\mathbf Y} = \boldsymbol{\Phi}{\mathbf W}_{\mathcal H}$, the regularizer is $\Omega:= \|{\mathbf W}_{\mathcal H}\|_2^2$, and the dependence measure $I$ is the HSIC estimate between predictions $\hat {\mathbf Y}$ and sensitive variables ${\mathbf S}$, which can now be estimated in Hilbert spaces:
$I:=\text{HSIC}({\mathcal Y},{\mathcal H},{\mathbb P}_{{\mathbf y}{\bf s}}) = \|{\mathbf C}_{ys}\|_{\text{HS}}^2$.
Now, by plugging all these terms in the cost function, using the representer's theorem ${\mathbf W}_{\mathcal H} = \tilde{\boldsymbol{\Phi}}^\top\boldsymbol{\Lambda}$ and after some simple linear algebra,
we obtain the dual weights in closed-form
\begin{equation}\label{eq:disp_kern}
	\boldsymbol{\Lambda} = (\tilde{\bf K} + \lambda {\bf I} + \frac{\mu}{n^2} \tilde{\bf K}_S \tilde{\bf K})^{-1} {\mathbf Y},
\end{equation}
which can be used for prediction with a new point ${\mathbf x}_\ast$ by using $\hat{\mathbf y}_\ast = {\bf k}_\ast\boldsymbol{\Lambda}$, where ${\bf k}_\ast = [K({\mathbf x}_\ast,{\mathbf x}_1),\ldots,K({\mathbf x}_\ast,{\mathbf x}_n)]^\top$. Note that in the case where $\mu = 0$ the method reduces to standard kernel ridge regression (KRR) method~\cite{ShaweTaylor04}. Note that centering points in feature spaces can be done implicitly with kernels~\cite{ShaweTaylor04}: a kernel matrix ${\bf K}$ is centered by doing $\tilde{\bf K} = {\bf H}{\bf K}{\bf H}$, where ${\bf H}= {\bf I} - \frac{1}{n}\mathbbm{1}\mathbbm{1}^\top$.

\section{Experiments}
\label{sec:exp}

This section presents the results of the application of our consistent regression framework to a remote sensing
problem.
In particular, we illustrate the performance of the proposed method to retrieve consistent estimates of chlorophyll content from hyperspectral images.

\subsection{Data collection}

The data were obtained in the SPARC-2003 (SPectra bARrax Campaign) and SPARC-2004 campaigns in Barrax, Spain. The region consists of approximately 65\% dry land and 35\% irrigated land. The methodology applied to obtain the {\em in situ} leaf-level Chl$_{ab}$ data consisted of measuring samples with a calibrated CCM-200 Chlorophyll Content Meter in the field.
Chl measurements were between 2 and 55 $\mu$g/cm$^2$.
Additionally, 30 random bare soil spectra with zero chlorophyll
value were added to broaden the dataset to non-vegetated samples.
Concurrently, we used CHRIS images Mode 1 (62 spectral bands, 34m spatial resolution at nadir). The images were preprocessed, geometrically and atmospherically corrected. A total of $n=136$ datapoints in a $62$-dimensional space and the measured chlorophyll concentration constitute the database.

\subsection{Results}

The experiment deals with the prediction of
chlorophyll content while forcing the model to be as independent as possible from the bands beyond the NIR. It is physically understood that the chlorophyll content drives reflectance mostly in the red edge.
Hence, our sensitive variables are the channels far beyond the NIR spectrum.
Figure \ref{consistency} shows the results that were obtained. We show the root mean square error (RMSE) and the HSIC for different levels of the consistency parameter $\mu$ (and, implicitly, the corresponding optimal $\lambda(\mu)$) for both the linear and non-linear models.

Several conclusions can be obtained. First, one can readily recognize, in both cases, a trade-off between obtaining accurate predictions and imposing consistency. As we increase the consistency parameter $\mu$ we obtain predictions with greater independence to the sensitive variables but the accuracy of the prediction deteriorates.
Second, although more accurate predictions can be obtained with the non-linear, kernel model (RMSE=2.99 mg/m$^3$ versus RMSE=5.69 mg/m$^3$), the rate of deterioration of the accuracy when consistency is imposed is greater for the non-linear, kernel model than for its linear counterpart.  Figure \ref{consistency} also illustrates the quality of predictions and consistency for two choices of the consistency parameter $\mu$. It can be noted that good models in terms of accuracy (red point) lead to more correlated predictions with the sensitive bands, while enforcing the constraints (blue point) lead to higher independence but poor fitting results for both linear (RMSE=13.71 mg/m$^3$) or nonlinear (RMSE=14.19 mg/m$^3$) models.

\iffalse
\subsection{Experiment 2: Detection and attribution}

We tackle here perhaps the most relevant problem in {\em climate science}: attributing causes to the observed climate change in our Planet. In this field, simulations with climate models are used to fit models. Two scenarios are simulated, using {\em either} natural or anthropogenic forcings (covariates) to generate scenarios for a variable of interest, e.g. anomalies of average global temperature. Then, one attributes climate change to human activities (e.g. carbon emissions) if the anthropogenic-driven model better fits observations.

We assess such scenarios with our consistent regression learning framework where only one model is needed, can be learned from real data, and assess sensitivity to several variables simultaneously. Interestingly the consistency parameter $\mu$ could be related to the attribution level. See preliminary results in figure~\ref{panel1}.

\begin{figure}[h!]
\centerline{\includegraphics[width=7cm]{images/da_fkl_quick.pdf}}
\caption{The consistency parameter $\mu$ sheds light on the problem at hand: when used to predict observed global temperature anomalies from natural (Nat: volcanoes, solar cycle) and anthropogenic (Ant: land use change, GHG emissions) forcings, the FKL algorithm~\cite{PerezSuay17ecmlpkdd} can only explain observations (and hence attribute climate change to human activities) by being independent to natural causes (high $\mu$ values, cyan). \label{panel1}}
\end{figure}

\fi

\section{Conclusions}\label{sec:conc}

This paper presented a novel statistical regression framework that allows one to incorporate consistency constraints. The methodology confers, to both linear and nonlinear statistical regression, methods whose solution can be expressed in closed-form. The models exploit all the information from a set of covariates while being maximally independent of a set of auxiliary, protected variables. We successfully illustrated the performance for the estimation of  chlorophyll content while being independent to particular spectral bands.

\small
\bibliographystyle{IEEEbib}

\section{Introduction}
\label{sec:intro}

Recent years have witnessed a successful adoption of statistical methods for model inversion, emulation and bio-geophysical parameter retrieval~\cite{CampsValls11mc}. Machine learning algorithms are flexible non-parametric models that fit the observations using large heterogeneous data. Machine learning models for parameter retrieval avoid complicated assumptions, provide fast and accurate estimates, and learn the complex relations directly from data.

Current operational vegetation products, like leaf area index (LAI), are typically produced with neural networks, Gross Primary Production (GPP) --as the largest global CO$_2$ flux driving several ecosystem functions-- is estimated using ensembles of random forests, kernel methods and neural networks~\cite{Tramontana16bg}, biomass has been estimated with stepwise multiple regression~\cite{Sarker11}, partial least squares regression is used for mapping canopy nitrogen~\cite{Coops03,Townsend03}, support vector regression~\cite{Smola2004} showed high efficiency in modelling LAI, fCOVER and evapotranspiration~\cite{Durbha07,Yang06}, and kernel methods in general~\cite{ShaweTaylor04,CampsValls09wiley},
and Gaussian Processes (GPs) in particular~\cite{Rasmussen2006}, recently provided excellent results in chlorophyll content estimation among other vegetation parameters~\cite{Furfaro06,CampsValls16grsm}.

There is however an important issue that is often disregarded: statistical models learn input-output mappings from data but very often do not respect the most elemental rules of physics. They often come up with accurate, yet inconsistent predictions. For example, bio-geo-physical parameters are typically estimated with individual, indepdent models which are typically trained separately. This common approach ignores the (potentially nonlinear) cross-relations among variables.
Constraining the estimation problem is known in machine learning as {\em structured-output learning}, and is tightly related to {\em multitask learning}~\cite{Leiva2012,Tuia11structured}. Extension of such models to the regression setting is far from trivial, as the number of constraints increases cubically with the number of samples and outputs. Furthermore, including constraints in the regression models goes beyond consistency of model outputs; one could be interested in preserving some particular characteristics in the predictions, e.g. being independent of some ancillary information, faithful to certain variable ranges, or disregarding some information from the input (spectral) bands, just to name a few. Our notion of {\em consistency} is broad: we posit that a prediction is fully consistent with respect to some sensitive features if and only if the model's predictions are statistically independent of them.

In this work, we introduce a novel statistical regression framework that allows one to incorporate such broad consistency constraints. The framework builds upon \cite{kamishima2013independence,PerezSuay17ecmlpkdd} to minimize a functional that tries to jointly minimize the empirical error and maximize the dependence of the predictions with respect to an external subset of predictors, observations or ancillary information here called {\em sensitive} features. Two models are derived: a linear and a nonlinear, kernel-based, consistent regression model. The new {\em consistency term} trades-off accuracy for consistency, and translates into an extra regularization term that can be easily interpreted. Interestingly, the proposed models come in closed-form analytical solutions, and are very easy to implement, involving only matrix inversions.

The remainder of the paper is organized as follows. Section~\ref{sec:method} introduces notation and reviews the consistent regression models proposed. Section~\ref{sec:exp} gives experimental evidence of performance in
chlorophyll content estimation.
Finally, Section~\ref{sec:conc} concludes the paper with some summarizing remarks.

\section{Proposed methodology}
\label{sec:method}

This section starts by defining the notation and the concept of consistent regression.  The proposed framework for performing consistent regression learning based on cross-covariance operators for dependence estimation in Hilbert spaces is then introduced.

\subsection{Notation and the regularization framework}

We are given $n$ samples of a response (or target) data matrix ${\mathbf Y}\in{\mathbbm{R}}^{n\times c}$, and $d+q$ prediction variables: $d$ driver variables ${\mathbf X}\in{\mathbbm{R}}^{n\times d}$ and $q$ sensitive ${\mathbf S}\in{\mathbbm{R}}^{n\times q}$. The goal is to obtain a generic prediction function (or model) $f$ for the target variable ${\mathbf Y}$ from the input data, $({\mathbf X},{\mathbf S})$. The goal in our framework of {\em consistent learning} is to predict ${\mathbf Y}$ while being maximally independent of ${\mathbf S}$.

A prediction is said to be totally consistent with respect to the sensitive features ${\mathbf S}$ if and only if $\widehat{{\mathbf Y}} \perp {\mathbf S}$. Therefore, two main ingredients are needed to perform consistent predictions: we need to ensure independence of the predictions on the sensitive variables, and simultaneously to obtain a good approximation of the target variables.

The proposed function $f$ tries to learn the relation between observed input-output data pairs $({\mathbf x}_1,{\mathbf y}_1)$, $\ldots,$ $({\mathbf x}_n,{\mathbf y}_n)$ $\in{\mathcal X}\times {\mathcal Y}$ such that it generalizes well (good predictions $\hat{{\mathbf y}}_\ast=f({\mathbf x}_\ast)\in{\mathcal Y}$ for the unseen input data point ${\mathbf x}_\ast\in{\mathcal X}$), and the predictions should be as independent as possible of the sensitive features (variables, auxiliary information or even observations). As such, the following functional should be optimized:
\begin{equation}\label{eq:framework}
{\mathcal L} = \dfrac{1}{n}\sum_{i=1}^n V(f({\mathbf x}_i),{\mathbf y}_i)
                + \lambda~\Omega(\|f\|_{\mathcal H})
                + \mu~I(f({\mathbf x}),{\bf s}),
\end{equation}
where $V$ is the error cost function, $\Omega(\|f\|_{\mathcal H})$ acts as a regularizer of the predictive function and controls the smoothness and complexity of the model, and $I(f({\mathbf x}),{\bf s})$ measures the independence between the model's predictions and the protected variables. Note that one aims to minimize the amount of information that the model shares with the sensitive variables while controlling the trade-off between fitting and independence through hyperparameters $\lambda$ and $\mu$. By setting $\mu=0$ one obtains the ordinary (Tikhonov's regularized) functional, and by setting $\lambda=0$ one obtains the unregularized versions of this framework.

The framework admits many variants depending on the cost function $V$, regularizer $\Omega$ and the independence measure, $I$. For example, in~\cite{Kamishima:2012}, the function $f$ was the logistic regression classifier and $I$ was a simplification of the mutual information estimate. Despite the good results reported in~\cite{Kamishima:2012}, these choices do not allow one to solve the problem in closed-form, nor to cope with more than one sensitive variable at a time, since the proposed mutual information is an uni-dimensional dependence measure. In the following section, we elaborate on this framework by using the concept of cross-covariance operators in Hilbert spaces, which lead to closed-form solutions and permit one to deal with several sensitive variables simultaneously.

\subsection{Consistent Linear Regression}

Let us now provide a straightforward instantiation of the proposed framework for consistent linear regression (CLR).
We will adopt a linear predictive model for $f$, i.e. the matrix of predictions for a test data matrix ${\mathbf X}_\ast$ is given by $\hat {\mathbf Y}_\ast = {\mathbf X}_\ast{\mathbf W}$, the mean square error for the cost function $V = \|{\mathbf Y}-{\mathbf X}{\mathbf W}\|_2^2$ and the standard $\ell_2$ regularization for model weights $\Omega:= \|{\mathbf W}\|_2^2$. Other choices could be made, leading to alternative formulations. In order to measure dependence, we will rely on the cross-covariance operator between the predictions and the sensitive variables in Hilbert space. Let us consider two spaces ${\mathcal Y}\subseteq {\mathbbm{R}}^{c}$ and ${\mathcal S}\subseteq {\mathbbm{R}}^{q}$, where random variables $(\hat{\mathbf y},{\bf s})$ are sampled from the joint distribution ${\mathbb P}_{{\mathbf y}{\bf s}}$. Given a set of pairs ${\mathcal D}=\{(\hat{\mathbf y}_1,{\bf s}_1),\ldots,(\hat{\mathbf y}_n,{\bf s}_n)\}$ of size $n$ drawn from ${\mathbb P}_{{\mathbf y}{\bf s}}$, an empirical estimator of HSIC~\cite{Gretton05} allows us to define
$$I:=\text{HSIC}({\mathcal Y},{\mathcal S},{\mathbb P}_{{\mathbf y}{\bf s}}) = \|{\mathbf C}_{ys}\|_{\text{HS}}^2 = \frac{1}{n^2}\text{Tr}(\tilde{\mathbf Y}^\top \tilde{\mathbf S} \tilde{\mathbf S}^\top\tilde{\mathbf Y}),$$
where $\|\cdot\|_{\text{HS}}$ is the Hilbert-Schmidt norm, ${\mathbf C}_{ys}$ is the empirical cross-covariance matrix between predictions and sensitive variables\footnote{The covariance matrix is ${\mathcal C}_{{\mathbf y}{\bf s}} = {\mathbb E}_{{\mathbf y}{\bf s}}({\mathbf y}{\bf s}^\top) - {\mathbb E}_{{\mathbf y}}({\mathbf y}){\mathbb E}_{{\bf s}}({\bf s}^\top)$, where ${\mathbb E}_{{\mathbf y}{\bf s}}$ is the expectation with respect to ${\mathbb P}_{{\mathbf y}{\bf s}}$, and ${\mathbb E}_{{\mathbf y}}$ is the marginal expectation with respect to  ${\mathbb P}_{{\mathbf y}}$ (hereafter we assume that all these quantities exist).}, $\tilde{\mathbf Y}$ and $\tilde{\bf S}$ represent the feature-centered ${\mathbf Y}$ and ${\bf S}$ respectively, and $\text{Tr}(\cdot)$ denotes the trace operation. We want to stress that HSIC allows us to estimate dependencies between multidimensional variables, and that HSIC is zero if an only if there is no second-order dependence between $\hat{\mathbf y}$ and ${\bf s}$. In the next section we extend the formulation to higher-order dependencies with the use of kernels~\cite{Scholkopf02,ShaweTaylor04}.

Plugging these definitions of $f$, $V$, $\Omega$ and $I$ in Eq.~\eqref{eq:framework}, one can easily show that the solution has the following closed-form solution for weight estimates
\begin{equation}\label{eq:disp_lin}
\widehat{\mathbf W} = (\tilde{\mathbf X}^\top \tilde{\mathbf X} + \lambda~{\bf I} + \frac{\mu}{n^2}~\tilde{\mathbf X}^\top \tilde{\mathbf S} \tilde{\mathbf S}^\top \tilde{\mathbf X})^{-1} \tilde{\mathbf X}^\top {\mathbf Y}, \end{equation}
where consistency is trivially controlled with $\mu$, which acts as an additional regularization term. Also note that when $\mu=0$ the ordinary (regularized) least squares solution is obtained.

\begin{figure*}[t!]
\small
\centerline{
\begin{tabular}{ccc}
\includegraphics[width=5.6cm]{images/chl_L_L_L_corr_hsic_IGARS.png}&
\includegraphics[width=5.6cm]{images/chl_L_L_Lscatter_IGARS_point1.png}&
\includegraphics[width=5.6cm]{images/chl_L_L_Lscatter_IGARS_point2.png}\\
\includegraphics[width=5.6cm]{images/chl_NL_NL_L_corr_hsic_IGARS.png}&
\includegraphics[width=5.6cm]{images/chl_NL_NL_Lscatter_IGARS_point1.png}&
\includegraphics[width=5.6cm]{images/chl_NL_NL_Lscatter_IGARS_point2.png}\\
\end{tabular}
}
\caption{Evolution of the RMSE [$\mu$g/m$^2$] and HSIC for predicting Chl-a with either linear (top) or kernel (bottom) regression as a function of the consistency parameter $\mu$. Scatter plots illustrate the accuracy (blue points, predicted vs. observed chlorophyll content) and consistency (purple points, predicted chlorophyll content vs. sensitive band) for two choices of $\mu$ (low and high consistency correspond to red and green respectively).}
\label{consistency}
\end{figure*}

\subsection{Consistent Kernel Regression}

Let us now extend the previous model to the nonlinear case in terms of the prediction function, the regularizer and the dependence measure by means of reproducing kernels~\cite{Scholkopf02,ShaweTaylor04}. We call this method the consistent kernel regression (CKR) model. We proceed in the standard way in kernel machines by mapping data ${\mathbf X}$ and ${\mathbf S}$ to a Hilbert space ${\mathcal H}$ via the mapping functions $\boldsymbol{\phi}(\cdot)$ and $\boldsymbol{\psi}(\cdot)$ respectively. This yields $\boldsymbol{\Phi},\boldsymbol{\Psi}\in{\mathcal H}\subseteq{\mathbbm{R}}^{d_{\mathcal H}}$, where $d_{\mathcal H}$ is the (unknown and possibly infinite) dimensionality of mapped points in ${\mathcal H}$. The corresponding kernel matrices can be defined as: $\tilde{\bf K}=\tilde\boldsymbol{\Phi}\tilde\boldsymbol{\Phi}^\top$ and $\tilde{\bf K}_S=\tilde\boldsymbol{\Psi}\tilde\boldsymbol{\Psi}^\top$.
Now the prediction function is $\hat {\mathbf Y} = \boldsymbol{\Phi}{\mathbf W}_{\mathcal H}$, the regularizer is $\Omega:= \|{\mathbf W}_{\mathcal H}\|_2^2$, and the dependence measure $I$ is the HSIC estimate between predictions $\hat {\mathbf Y}$ and sensitive variables ${\mathbf S}$, which can now be estimated in Hilbert spaces:
$I:=\text{HSIC}({\mathcal Y},{\mathcal H},{\mathbb P}_{{\mathbf y}{\bf s}}) = \|{\mathbf C}_{ys}\|_{\text{HS}}^2$.
Now, by plugging all these terms in the cost function, using the representer's theorem ${\mathbf W}_{\mathcal H} = \tilde{\boldsymbol{\Phi}}^\top\boldsymbol{\Lambda}$ and after some simple linear algebra,
we obtain the dual weights in closed-form
\begin{equation}\label{eq:disp_kern}
	\boldsymbol{\Lambda} = (\tilde{\bf K} + \lambda {\bf I} + \frac{\mu}{n^2} \tilde{\bf K}_S \tilde{\bf K})^{-1} {\mathbf Y},
\end{equation}
which can be used for prediction with a new point ${\mathbf x}_\ast$ by using $\hat{\mathbf y}_\ast = {\bf k}_\ast\boldsymbol{\Lambda}$, where ${\bf k}_\ast = [K({\mathbf x}_\ast,{\mathbf x}_1),\ldots,K({\mathbf x}_\ast,{\mathbf x}_n)]^\top$. Note that in the case where $\mu = 0$ the method reduces to standard kernel ridge regression (KRR) method~\cite{ShaweTaylor04}. Note that centering points in feature spaces can be done implicitly with kernels~\cite{ShaweTaylor04}: a kernel matrix ${\bf K}$ is centered by doing $\tilde{\bf K} = {\bf H}{\bf K}{\bf H}$, where ${\bf H}= {\bf I} - \frac{1}{n}\mathbbm{1}\mathbbm{1}^\top$.

\section{Experiments}
\label{sec:exp}

This section presents the results of the application of our consistent regression framework to a remote sensing
problem.
In particular, we illustrate the performance of the proposed method to retrieve consistent estimates of chlorophyll content from hyperspectral images.

\subsection{Data collection}

The data were obtained in the SPARC-2003 (SPectra bARrax Campaign) and SPARC-2004 campaigns in Barrax, Spain. The region consists of approximately 65\% dry land and 35\% irrigated land. The methodology applied to obtain the {\em in situ} leaf-level Chl$_{ab}$ data consisted of measuring samples with a calibrated CCM-200 Chlorophyll Content Meter in the field.
Chl measurements were between 2 and 55 $\mu$g/cm$^2$.
Additionally, 30 random bare soil spectra with zero chlorophyll
value were added to broaden the dataset to non-vegetated samples.
Concurrently, we used CHRIS images Mode 1 (62 spectral bands, 34m spatial resolution at nadir). The images were preprocessed, geometrically and atmospherically corrected. A total of $n=136$ datapoints in a $62$-dimensional space and the measured chlorophyll concentration constitute the database.

\subsection{Results}

The experiment deals with the prediction of
chlorophyll content while forcing the model to be as independent as possible from the bands beyond the NIR. It is physically understood that the chlorophyll content drives reflectance mostly in the red edge.
Hence, our sensitive variables are the channels far beyond the NIR spectrum.
Figure \ref{consistency} shows the results that were obtained. We show the root mean square error (RMSE) and the HSIC for different levels of the consistency parameter $\mu$ (and, implicitly, the corresponding optimal $\lambda(\mu)$) for both the linear and non-linear models.

Several conclusions can be obtained. First, one can readily recognize, in both cases, a trade-off between obtaining accurate predictions and imposing consistency. As we increase the consistency parameter $\mu$ we obtain predictions with greater independence to the sensitive variables but the accuracy of the prediction deteriorates.
Second, although more accurate predictions can be obtained with the non-linear, kernel model (RMSE=2.99 mg/m$^3$ versus RMSE=5.69 mg/m$^3$), the rate of deterioration of the accuracy when consistency is imposed is greater for the non-linear, kernel model than for its linear counterpart.  Figure \ref{consistency} also illustrates the quality of predictions and consistency for two choices of the consistency parameter $\mu$. It can be noted that good models in terms of accuracy (red point) lead to more correlated predictions with the sensitive bands, while enforcing the constraints (blue point) lead to higher independence but poor fitting results for both linear (RMSE=13.71 mg/m$^3$) or nonlinear (RMSE=14.19 mg/m$^3$) models.

\iffalse
\subsection{Experiment 2: Detection and attribution}

We tackle here perhaps the most relevant problem in {\em climate science}: attributing causes to the observed climate change in our Planet. In this field, simulations with climate models are used to fit models. Two scenarios are simulated, using {\em either} natural or anthropogenic forcings (covariates) to generate scenarios for a variable of interest, e.g. anomalies of average global temperature. Then, one attributes climate change to human activities (e.g. carbon emissions) if the anthropogenic-driven model better fits observations.

We assess such scenarios with our consistent regression learning framework where only one model is needed, can be learned from real data, and assess sensitivity to several variables simultaneously. Interestingly the consistency parameter $\mu$ could be related to the attribution level. See preliminary results in figure~\ref{panel1}.

\begin{figure}[h!]
\centerline{\includegraphics[width=7cm]{images/da_fkl_quick.pdf}}
\caption{The consistency parameter $\mu$ sheds light on the problem at hand: when used to predict observed global temperature anomalies from natural (Nat: volcanoes, solar cycle) and anthropogenic (Ant: land use change, GHG emissions) forcings, the FKL algorithm~\cite{PerezSuay17ecmlpkdd} can only explain observations (and hence attribute climate change to human activities) by being independent to natural causes (high $\mu$ values, cyan). \label{panel1}}
\end{figure}

\fi

\section{Conclusions}\label{sec:conc}

This paper presented a novel statistical regression framework that allows one to incorporate consistency constraints. The methodology confers, to both linear and nonlinear statistical regression, methods whose solution can be expressed in closed-form. The models exploit all the information from a set of covariates while being maximally independent of a set of auxiliary, protected variables. We successfully illustrated the performance for the estimation of  chlorophyll content while being independent to particular spectral bands.

\small
\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


IFSA LC UGM is looking for new members! For anyone who wants to be the part of us, please fill the registration form below or through the following link at bit.ly/OpenCallMembers2019 and submit it.
Kindly share this info to your friends and see you soon at the interview session!
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The brand new 2019 Chevy Trax will be obtainable in subcompact SUV for making enhancements. The far more successful region with the part and specs while in the presence of those motor vehicles will probably be practical due to the fact the present model. Nevertheless, the new model is trusted for a few excellent new part that may enhance shortly spearhead supplemental sales make much more improvement with regards to gives. Makers of vehicles which may be utilized in 6 trim are relied on to provide the SUV somewhat on this in the method that will talk to a range of forms of individuals.
As described previously, the 2019 Chevrolet Trax provides the incorrect frame to the Chevrolet Sonic and because of this, it cost much less Buick Encore. Nevertheless, the Trax is going to be drastically larger than expected. Chevy has picked to incorporate some functions in the direction of the intelligence with this vehicle, certainly, it is a wise way. This auto has a hatchback model in addition to a small crew look taller in the similar time. Review with Encore by utilizing the safety options of the automobile. It can make prosperous exterior assists make it seem so relaxed and successful tame as envisioned.
Possibly a smaller draft back for the basics rendition SL rides on wheels made from steel and it has a silver plastic wheel cover at the same time as the base model reveals monetary platform with mirror entry methods is darker and shed Strip Chrome beltline. The roof will be the exact same for every trim degree while in the vehicle and display help from 2019 Chevy Trax has lasted a 12 months now. Decreasing of fuel consumption demonstrated indications of advancement. The wheels may be from the 16-inch and 18-inch for LT and LTZ degree.
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text:
Smart dolphin fish
(13) | 26/04/2017 | Translate: ΕλληνικάEnglishEspañolFrançaisItalianoDeutschРусскийNederlandsTürkçePortuguês日本語中文(简体)한국어DanskSuomiPolskiSvenskaNorskالعربيةБългарски中文(漢字)Češtinaעבריתहिन्दी; हिंदीMagyarRomânăCрпски језикSlovenčinaภาษาไทยУкраїнська
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Dolphins use a very clever method to catch small fish. creating a "Wall" sand with their tail panicking fish, which jump directly out of the water and the mouth of dolphins.
Dolphin against surfers On April 27, 2018, a group of twelve dolphins chasing fish along the coast in Gracetown, Australia. One of them jumped out of the water, and collided with surfer Andrew Hill who was on the board of.
The fish who drinks beer On the banks of the river Manso Mato Grosso state of Brazil, fishermen in a boat giving beer to a fish. The fish jumped and stood at the edge of the boat, drinking a few gulps before returning to the water.
Pitbull saves a chihuahua from drowning A Chihuahua falls into a pool in the Brazilian city of Jardinópolis and can't get out. A pit bull that heard his cries, he jumps into the pool to grab the little dog with his mouth and pull him out of the water, saving it from drowning.
Greetings to the fishermen A fish jumps out of the water and onto a boat where two fishermen are.
Dolphin mocks a seagull A dolphin named Helen has fun mocking a seagull in Vancouver Aquarium, Canada. The dolphin pulls its head out of the water and throws the fish over its snout several times in front of the gull, who watches hungry from the edge of the tank.
He tried to catch a fish with a shotgun A man in Brazil tries to catch a fish by shooting it with a carbine in the water. His idea was not so clever.
Women on a kayak were found in the mouth of a whale At Avila Beach in California, Julie and Liz, two kayakers, found in the mouth of a large whale. The scene was filmed by another kayaker and lasted a few seconds. A whale was thrown out of the water with its mouth open to catch some fish, but instead caught the two women in the two-seater kayak. Larger whales cannot swallow large prey, as their mouth is shaped for small foods. Fortunately, No one was injured.
Surfing from the sand to the sea In Hobart, Australia, Theo starts with a surfboard from a dune, and ends at the sea. Hitting a wave, makes a big jump before landing in the water.
A MotoGP driver jumps off his motorcycle During the Austrian MotoGP held on 23 August, Spanish rider Maverick Viñales jumped off his motorcycle as he was traveling at 220 km / h. Viñales realized in one turn that his brakes were not working, and decided to abandon his motorcycle heading for the firewall at high speed.
Curious cats watching a fish Countertop, two strange cats observe a fish swimming in the sink. The fish will suddenly jump out of the water and scare the two cats, which run away.
Dog saves a puppy on a boat A Rottweiler jumping into the water to "Save" a puppy trapped in a knit boat, on the banks of the Bei River Qingyuan China. Then he managed to catch the rope with his mouth and pull it to shore, allowing the puppy to get out of the boat.
Two children throw their mother in the street Two children jumping at the same time to avoid a puddle, and lure their mother throwing her on the street. WTF?!
A dolphin hunts using its tail A dolphin beats fish with its tail and firing several meters before devour.
Three humpback whales jumping together in front of tourists Sts August 17 of 2018 in Nova Scotia, Canada, tourists in a boat were lucky enough to watch three humpback whales also jumped out of the water.
Dog saves his friend in the pool The Remus Smokey and dogs are friends for years, and that day his boss Remus visited that of Smokey in Mesa, Arizona. The Smokey rarely swims in the pool, and when it does it is always close to the stairs and under the supervision of bosses. This time, the Smokey fell in the pool while the owners were at home. He could not get up the stairs or climb on the edge of the pool, and threatened to drown. Seeing his friend in danger, Remus jumped into the water to help him get out of the pool. A truly heroic movement!
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text:
Dr. Prakash on the Methanol Economy
Links and References:
The Methanol Alternative: 2012 Methanol Forum
The Methanol Economy
Methanol Fuel Cell Energy for Homes and Businesses
Methanol fuel cell plug-in-hybrids?
Posted by Marcel F. Williams at 6:02 PM No comments:
Labels: industrial chemical, methanol, synfuel
Establishing a Permanent Human Presence on Mars with a Lunar Architecture
ETLV-2 coupled with an ADEPT deceleration shield on its way the martian surface
by Marcel F. Williams
"As we reported in August 2013, even after the SLS and Orion are fully developed and ready to transport crew NASA will continue to face significant challenges concerning the long-term sustainability of its human exploration program. For example, unless NASA begins a program to develop landers and surface systems its astronauts will be limited to orbital missions of Mars. Given the time and money necessary to develop these systems, it is unlikely that NASA would be able to conduct any manned surface exploration missions until the late 2030s at the earliest."
NASA Office of Inspector General
It is generally agreed by both the Congress and the Executive Branch that sending humans to the surface of Mars, sometime in the 2030s, should be the long term goal of the National Aeronautics and Space Administration (NASA). However, the intermediate destination during the 2020s needed to develop and to mature such space faring capability has been subject to controversy. While some have advocated a return to the lunar surface as a bridge towards Mars, others have argued that the development of a lunar architecture could actually siphon off necessary funds for sending humans to the martian surface.
The extraterrestrial deployment of huge amounts of water will be essential for any interplanetary journey. Even if some future Mars vessels are xenon fueled solar electric interplanetary vehicles, crewed missions between cis-lunar space and Mars orbit will still require a substantial tonnage water for drinking, washing, food preparation, the production of air and for mass shielding habitat modules from the dangers of cosmic radiation and major solar events. The most expensive source of water for an interplanetary vehicle within cis-lunar space is from the Earth's deep gravity well. However, water derived from ice in the Moon's polar regions would be a substantially cheaper source since the Moon has a significantly lower gravity well. Water, of course, could also be used for the production of LOX/LH2 propellant necessary for voyages between cis-lunar space and Mars orbit.
ADEPT payload deployment scenarios for Mars (Credit: NASA)
Because of its thin atmosphere and higher gravity, Mars is a very different world than the Moon. Still, habitat modules, propellant producing water depots, and mobile ground vehicles that could be utilized on the surface of the Moon could also be used on the surface of Mars. This could save NASA enormous amounts of money since basically the same surface architecture for the Moon could also be deployed on the surface of Mars. So no new surface infrastructure unique to Mars would not have to be developed.
Vehicles designed to deploy cargoes and crews to the lunar surface could also be used to deploy cargoes and crews to the surface of Mars. But in order to safely deploy cargoes and crews to the martian surface, such vehicles will need to be shielded and decelerated through the thin martian atmosphere through the ADEPT or HIAD technologies currently being developed by NASA.
HIAD decelerator entering martian atmosphere with crew vehicle (credit: Boeing)
HIAD and ADEPT technologies simply use a large expandable heat shield to protect a spacecraft from the frictional heating of the thin martian atmosphere (100 thinner than the Earth's atmosphere) while also decelerating the vehicle enough to eventually allow the vehicle to utilize retrorockets to hover and land on the martian surface. The weight of these decelerating heat shields approach 50% of the payload being deployed to the surface. But NASA believes that these technologies should enable them to deploy as much as 40 tonnes of payload to the martian surface.
C-ETLV-5 cargo lander and ETLV-2 crew lander for deploying cargoes and crew to the surface of the Moon, Mars, and on the surfaces of the moons of Mars
In July of 1962, NASA invited private companies to submit proposals for the development of a Lunar Module (LM). Seven years later, this lunar landing craft took Neil Armstrong and Buzz Aldrin to the surface of the Moon. Assuming a similar length of time for the development of a new Extraterrestrial Landing Vehicle (ETLV), proposals submitted for an ETLV in 2016 could result in the return of humans to the lunar surface by 2023. Thus, by the 2030's, the ETLV will be a mature landing vehicle ready to deploy humans and cargo to the surface of Mars.
If we assume that NASA's annual human spaceflight related budget remains at approximately $8 billion a year over the next 25 years, then NASA will spend approximately $200 billion over the next quarter of a century on human spaceflight related technology, operations, and activities. $8 billion a year should be enough for NASA to establish a permanent human presence on the surface of the Moon in the 2020s and on the surface of Mars in the 2030s-- if such efforts are prioritized-- especially if NASA is no longer burdened with the $3 billion a year ISS program during the next two decades.
Ares vehicle (ETLV-2 and ADEPT deceleration shield) for landing on Mars: A. ETLV-2 begins to dock with DS (deceleration shield); B. ETLV-2 trajectory burn propels the Ares towards Mars; C. ETLV-2 undocks with the DS, turning around 180 degrees; D. ETLV-2 docks with DS in a Mars entry configuration; E. Ares vehicle enters the Martian atmosphere; F. ETLV-2 separates from the DS after subsonic deceleration velocity is achieved; G. ETLV-2 decelerates further towards the martian surface before hovering and landing its crew.
A reusable single staged LOX/LH2 ETLV capable of a round trips between the Earth-Moon Lagrange points and the lunar surface should also be capable of easily transporting crews from the surface of Mars to Low Mars Orbit-- or even all the way to the surface of Mars's inner moon, Phobos. HIAD or ADEPT deceleration shield would, again, be used to deploy the crewed ETLV safely to the martian surface. The cost of developing a crewed lunar landing vehicle has been estimated to be as much as $8 to $12 billion. So over the course of seven years, the cost for developing an ETLV could range from $1.1 billion to $1.7 billion per year. And that's a cost that is certainly affordable with an $8 billion a year human spaceflight related budget.
ETLV-2 at a sintered landing area being refueled with LOX and LH2 for its departure to Mars orbit.
Regolith shielded lunar habitats cheaply derived from SLS propellant tank technology could also be deployed to the martian surface using a lunar cargo lander and a, of course, a HIAD or ADEPT deceleration shield. Regolith shielding a martian habitat to a similar degree as a lunar habitat will be necessary since the level of cosmic ray exposure on the martian surface is not substantially lower than on the lunar surface.
Three habitat modules previously deployed by a C-ETLV-5. The pressurized habitats are shielded with 2 meters of martian regolith contained within the automatically deployed regolith wall. Solar charged batteries are used to provide power for the habitat at night. But nearby nuclear power units buried beneath the regolith will also provide supplementary power for the outpost.
Ionizing Radiation on the Surface of the Moon:
38 Rem - annual amount of cosmic radiation on the Lunar surface during the solar minimum
11 Rem - annual amount of cosmic radiation on the Lunar surface during the solar maximum
Ionizing Radiation on the Surface of Mars:
33 Rem - annual rate of cosmic radiation on the surface of Mars beneath 16 gm/cm3 of Martian atmosphere during the solar minimum
8 Rem - annual rate of cosmic radiation on the surface of Mars beneath 16 gm/cm3 of Martian atmosphere during the solar maximum
Percentage of water contained in different regions on the martian surface.
Lunar water collecting mobile vehicles using microwaves used to extract water from the regolith at the lunar poles could also be used on much of the martian surface. The water content of the lunar regolith at the lunar poles has been estimated to be approximately 5%. The water content of the martian regolith at the lower latitudes ranges from approximately 1 to 7% depending on the region. But there are large regions near the martian equator that may have regolith with a water content as high as 7%. At higher latitudes, the water content may be as high as 30%. And at the martian poles, the water content could be as much as 70%.
Mobile water tanker and microwave water extraction robot on Mars.
Solar powered WPD-LV-5 water storage and propellant producing unit on the surface of Mars. Additional power can be provided by nearby nuclear power units buried beneath the martian regolith.
Small nuclear power units will probably be necessary to supplement the solar electric power supply at a martian outpost. While batteries and electric flywheels charged during the daytime could provide power for an outpost a night, dust storms could substantially reduce solar electricity for up to a month with dust particles blanketing the solar panels. But small nuclear power units could run 24 hours a day for several years before having to be replaced. So during dust storms, it might be wise for the outpost to contract the solar panels while mostly relying on nuclear power until the storm is over. Such nuclear power units for Mars should also probably be initially tested on the lunar surface in the 2020s for a few years before they are eventually deployed at a martian outpost in the 2030s.
Nuclear power unit on the Moon with its reactor buried beneath the regolith and its cooling panels above the surface. Such units could also be utilized on the martian surface (Credit: NASA).
Adative Deployable Entry and Placement Technology (ADEPT):
ADAPTABLE, DEPLOYABLE ENTRY AND PLACEMENT TECHNOLOGY (ADEPT) FOR FUTURE MARS MISSIONS
ADEPT Hypersonic Deployable Decelerators Adaptable, Deployable Entry Technology and Placement (ADEPT) Project
MECHANICALLY DEPLOYED HYPERSONIC DECELERATOR AND CONFORMAL ABLATOR TECHNOLOGIES FOR MARS MISSION
Hypersonic Inflatable Aerodynamic Decelerator (HIAD)
Utilizing Lunar Water Resources for Human Voyages to Mars
Landing Large Cargoes and Crews on the Surface of Mars
Cosmic Radiation and the New Frontier
Utilizing the SLS to Build a Cis-Lunar Highway
The Lunar Module
Future Mars Explorers Face Dusty Challenges
Posted by Marcel F. Williams at 11:42 AM 2 comments:
Labels: ADEPT, aerobrake, astronauts, ETLV-2, heat shield, HIAD, Mars, Moon, NASA, Space travel
Establishing a Permanent Human Presence on Mars wi...


text:
Q: Calculate $\log_{\frac{1}{2}}{(2\sqrt[3]{4})}$ I have this exercise:$$\log_{\frac{1}{2}}{(2\sqrt[3]{4})}$$
There is not equation or something else, how should I solve this type of exercise?

A: $2 = \frac{1}{2}^{-1}$
$\sqrt[3]{4} = 2 ^{2/3}= (1/2)^{-2/3}$
$\log_{\frac{1}{2}}{(2\sqrt[3]{4})} = -1 - 2/3 = -5/3$

A: \begin{align*}
\log_{1/2}(2\sqrt[3]4) & =\log_{1/2}(2)+\frac{1}3\log_{1/2}(4) \\
& =-1-\frac{2}3
\end{align*}

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