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Khanna Industries have a problem which the company almost bankrupt. Mr Khanna son which is Dev Annand wants to investigate why the company loss every year. So he applies a job at the company with letter of support from Mr Khanna but not as Dev Annand. The managers do not know Dev is the son of Mr Khanna. In the interview session the manager of the company not ask proper question during the…...

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...rear end of the train. Passing by the ‘public clocks’ on the train Alice compares the reading on her watch sA (proper time!) with the time h (coordinate time!) shown on these clocks. Assume that the point n = 0 of the train where Bob is sitting reaches Whitewood at the moment t = 0 and at this moment the velocity of the whole train suddenly changes sign. Such a sudden synchronous velocity reversal can be accomplished only if all the cars are motor-cars and the motormen on them are aware of the time t due to e.g. the public clocks along the railroad. Our object of study is the dependence of sA on h i.e. the function f1(h) in the relation sA ¼ f1 ðhÞ (the index 1 refers to the ﬁrst method of synchronization). If the train’s motion is to a good approximation uniform with the same velocity V both to Whitewood and backward from it we have f1 ðhÞ ¼ TA h where TB ¼ TB pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 2 TA 1 À V =c : Indeed, looking at the series of encounters of Alice with the clocks on the train from the point of view of her rest frame, we observe that the later a train clock passes by Alice the longer path it had already travelled and, therefore, the more it is losing compared to her watch. This result is the strict analogue of Bob’s expeTB rience which has been summed up in the formula sB ¼ TA t Consider now the second method of synchronization. Assume that the coordinate time h on the train has been chosen according to Einstein’s synchronization procedure when the train was moving with......

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...ĐẠI HỌC QUỐC GIA HÀ NỘI TRƯỜNG ĐẠI HỌC KHOA HỌC TỰ NHIÊN | | | |---------- ( ---------- | | ĐỀ CƯƠNG MÔN HỌC PHƯƠNG PHÁP MONTER – CARLO 1. Thông tin về giảng viên: - Họ và tên: Nguyễn Quý Hỷ - Chức danh, học hàm, học vị: Giảng viên cao cấp, Giáo sư, Tiến sỹ - Thời gian, địa điểm làm việc: 1961-2005, tại Trường ĐHTH, ĐHKHTN Hà Nội - Địa chỉ liên hệ: 262/BT 16- Nguyễn Huy Tưởng, Q. Thanh Xuân, Hà Nội - Điện thoại: 557 5260, email: nquyhy@vnn.vn, quyhy@hn.vnn.vn - Các hướng nghiên cứu chính: Phương pháp Monte Carlo, Lý thuyết Đổi mới - Thông tin về giảng viên có thể giảng dạy môn học: + Trần Đình Quốc, Khoa Toán Cơ Tin học-Trường ĐHKH Tự nhiên-ĐHQG Hà Nội + ĐT:04.8581135,04.2109728,Mobile:091205499 + E-mail: quoctd@vnu.edu.vn , quoctd@gmail.com 2. Thông tin về môn học: - Tên môn học: Phương pháp mô phỏng số Monte Carlo - Mã môn học: - Số tín chỉ: 2 - Giờ tín chỉ đối với các hoạt động học tập: + Nghe giảng lý thuyết trên lớp: 25 + Làm bài tập trên lớp: 4 + Tự học: 1 - Đơn vị phụ trách môn học: + Bộ môn: Toán học tính toán + Khoa: Toán - Cơ - Tin học -...

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...following mappings T : V ! V are linear, but the third is not: (a) T ðAÞ ¼ MA, (b) T ðAÞ ¼ AM þ MA, (c) T ðAÞ ¼ M þ A. 5.55. Give an example of a nonlinear map F : R2 ! R2 such that F À1 ð0Þ ¼ f0g but F is not one-to-one. 5.56. Let F : R2 ! R2 be deﬁned by Fðx; yÞ ¼ ð3x þ 5y; 2x þ 3yÞ, and let S be the unit circle in R2 . (S consists of all points satisfying x2 þ y2 ¼ 1.) Find (a) the image FðSÞ, (b) the preimage F À1 ðSÞ. 5.57. Consider the linear map G : R3 ! R3 deﬁned by Gðx; y; zÞ ¼ ðx þ y þ z; y À 2z; y À 3zÞ and the unit sphere S2 in R3 , which consists of the points satisfying x2 þ y2 þ z2 ¼ 1. Find (a) GðS2 Þ, (b) GÀ1 ðS2 Þ. 5.58. Let H be the plane x þ 2y À 3z ¼ 4 in R3 and let G be the linear map in Problem 5.57. Find (a) GðHÞ, (b) GÀ1 ðHÞ. 5.59. Let W be a subspace of V. The inclusion map, denoted by i : W ,! V, is deﬁned by iðwÞ ¼ w for every w 2 W . Show that the inclusion map is linear. 5.60. Suppose F : V ! U is linear. Show that FðÀvÞ ¼ ÀFðvÞ. Kernel and Image of Linear Mappings 5.61. For each linear map F ﬁnd a basis and the dimension of the kernel and the image of F: (a) F : R3 ! R3 deﬁned by Fðx; y; zÞ ¼ ðx þ 2y À 3z; 2x þ 5y À 4z; x þ 4y þ zÞ, (b) F : R4 ! R3 deﬁned by Fðx; y; z; tÞ ¼ ðx þ 2y þ 3z þ 2t; 2x þ 4y þ 7z þ 5t; x þ 2y þ 6z þ 5tÞ. CHAPTER 5 Linear Mappings 5.62. For each linear map G, ﬁnd a basis and the dimension of the kernel and the image of G: (a) G : R3 ! R2 deﬁned by Gðx; y; zÞ ¼ ðx þ y þ z; 2x þ 2y þ 2zÞ, (b) G : R3 ! R2 deﬁned by......

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...assuming there are only two uses to which an individual may devote his or her time—either engaging in market work at a real wage rate of w per hour or not working. We shall refer to nonwork time as “leisure,” but this word is not meant to carry any connotation of idleness. Time not spent in market work can be devoted to work in the home, to self-improvement, or to consumption (it takes time to use a television set or a bowling ball).1 All of those activities contribute to an individual’s well-being, and time will be allocated to them in what might be assumed to be a utility-maximizing way. More speciﬁcally, assume that an individual’s utility during a typical day depends on consumption during that period ðcÞ and on hours of leisure enjoyed ðhÞ: utility ¼ U ðc, hÞ: (16.1) Notice that in writing this utility function we have used two “composite” goods, consumption and leisure. Of course, utility is actually derived by devoting real income and time to the consumption of a wide variety of goods and services.2 In seeking to maximize utility, 1 Perhaps the first formal theoretical treatment of the allocation of time was given by G. S. Becker in “A Theory of the Allocation of Time,” Economic Journal 75 (September 1965): 493–517. This observation leads to the consideration of how such activities are produced in the home. For an influential survey, see R. Gronau, “Home Production: A Survey,” in O. C. Ashenfelter and R. Layard, Eds., Handbook of Labor Economics (Amsterdam:......

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...kfjks dajf ksa Df kjas dkfsda kfDfjsda kfks a fja s d kf aks D fkj da kfsd akf dkfja kdf dfk asjfs dka dfj asdk fjs akf dkfj djf fjdfj a djfj d f djd d djd d fd fd kd kjls aflkj d df ks dlkj sd kjd d lkd slks kfkj sj sd d dkf lkfjslk afkjsdf f dksd klfl ksd fkjsd ds ds ds ds jd j k dj sj dj s s a s s d d d d d d djf dfjds djd d djd dj djd d jd s sk s ks sk sk sk sk sdki aka tha aks ahte sha did stjk tha eht ehei the ehd ehe eh ehsj e he ejd eh eit wo sht eod dko dht ekco thhe do the do cko the to he the oso the lo the sch to thek dit the dki co tho she the skou dht eh the dke the do th sl eh ti the dke the sla tkso the skoy sht eo the sl dl dos do dh the do dht eod the do the do sht eot the dko the do the dok the dso the dko the dod dhth the the t the thet th e hee te ht tt hethet het hethe thte tehte htet he he the hteht et het het et heht htht het he the ht het hth wt het hweaht ah that hat hethew ht heh dhthd t hdt hd hd hhdthdt hd hd hthdthdt hdh dh dht hd ht hdt hdthsdt h...

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...example, note (a) pertains to an EPV variable, ‘‘Sustainable Pre-Tax Earnings before NOLs’’ (note (3E)), which when divided by NAV (note (d)) on an after tax basis gives Marvel’s estimated Return on Net Asset Value (or RNAV, note (e)). Dividing RNAV by Marvel’s discount rate (note (f)) gives a growth multiple (note (g)), which when multiplied by our EPV derives a growth value of $1,607.1 (note (i)). Exhibit 6 Marvel’s growth value $ in millions Sustainable pre-tax earnings before NOLs Tax rate (%) Sustainable earnings before NOLs NAV RNAV (%) Discount rate (%) Growth multiple EPV Growth value 126.5 30.0 88.6 468.8 18.9 9.9 1.9 844.8 1,607.1 Notes ðaÞ ¼ ð3EÞ (b) ðcÞ ¼ ðaÞ* ½1 2 ðbÞ ðdÞ ¼ ð8AÞ ðeÞ ¼ ðcÞ=ðdÞ ðfÞ ¼ ð6EÞ ðgÞ ¼ ðeÞ=ðfÞ ðhÞ ¼ ð14EÞ ðiÞ ¼ ðgÞ* ðhÞ VOL. 37 NO. 4 2009 STRATEGY & LEADERSHIP PAGE 31 j j Notes 1. ‘‘Economic focus – diagnosing depression,’’ The Economist, January 3-9, 2009, p. 57. 2. Jessica Hall, ‘‘Distress M&A not for faint of heart,’’ Thomson Reuters, April 3, 2009, www.reuters. com/article/reutersEdge/idUSTRE53255220090403 3. $471 million ¼ 101:8 million shares outstanding* $4:625=share. Data source: Jason Auerbach and Benjamin Esty, Bankruptcy and Restructuring at Marvel Entertainment Group, HBS Case Services #9-298-059, 7/2/1998, pp. 5 and 9. 4. For more information see Auerbach and Esty (1998). 5. Gilson (1995), cited above, p. 23. 6. There is obviously a great deal more to this bankruptcy than we can cover in this overview.......

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...NeuroImage 25 (2005) 1256–1265 point p. The type I error (a level) for the multiple hypotheses testing would be a ¼ Pðreject at least one H0 ð pÞjH0 trueÞ ! ¼P sup T ð pÞ N h for some h: paBX The distribution of suppaX T( p) is asymptotically given as ! 2 X P sup T ð pÞN h % /d ðBXÞqd ðhÞ paBX d¼0 where / d are the d-dimensional Minkowski functionals of BX and q d are the d-dimensional Euler characteristic (EC) density of t-field (Worsley, 1994). In order for the random field theory for multiple comparison to be valid, the data must be Gaussian and smooth. Heat kernel smoothed cortical thickness measurements tend to reasonably follow random field assumptions when large band width is used. Fig. 6 demonstrates that the data are increasingly Gaussian with larger band width. See Chung et al. (2003) for the demonstration of increasing Gaussianness using Lilliefors statistic. The Minkowski functionals are / 0 = 2, / 1 = 0, / 2 = l(BX)/2 = 49, 616 mm2, the half area of the template cortex BX. The EC densities q 0 and q 2 are given by q0 ðhÞ ¼ Z l À dþ1 Á k ð2pÞ3=2 ðdþ1Þ x2 À 2 dx; Àd Á 1 þ d C 2 1=2 ðdpÞ h q2 ðhÞ ¼ C 2 À Á ðdÀ1Þ C dþ1 h2 À 2 2 h; À d Á1=2 À d Á 1 þ d C 2 2 where k measures the smoothness of fields (Worsley et al., 1992). If we assume e to be a unit variance isotropic field, the smoothness of an isotropic random field can be defined as the covariance of......

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