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20 Jun 2018: Physical description: 26.5 cm. x 24 cm. Small lacunae in the middle. ... Physical description: 27 cm. x 24.5 cm. Small lacunae in the middle.

20 Jun 2018: ScriptFinal'alit extends below the connecting stroke. Iim, ha' and ka' are usuallywritten with a single curved stroke. ... Papyrus no: C842. Date: 3rd century A.H.l9th century A.D. Physical description: 24 cm.

20 Jun 2018: Date: 3rd century A.H.l9th century A.D. Physical description: 28 cm. x 24 cm. ... Verso, written in black ink,24 lines, parallel to the fibres, 2 lines in right margin.

20 Jun 2018: Physical description: 38.5 cm. x 24 cm. Large pieces missing in the middle. ... Physical description: 24 cm. x 23.5 cm. Small lacunae in the middle.

20 Feb 2018: kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise. ... 24, no. 4, pp. 562–588, 2010. [13] M Gašić, C Breslin, M Henderson, D Kim, M Szummer,B Thomson, P Tsiakoulis, and S Young, “POMDP-based dia-logue manager adaptation

20 Feb 2018: kA(a,a′). For a training sequence of belief state-action pairs B = [(b0,a0),. , ... kA(a,a′) = δa(a. ′) (6). where δa(a′) = 1 iff a = a′, 0 otherwise.

8 Feb 2018: International Finance, Pembroke Visiting. 2017. Turalay Kenc (to 24 Mar. 2018) (Visiting Professorship). ... Oncological Pathology. 2003. Ming-Qing Du, HH. Oncology, Li Ka Shing. 2015.

20 Feb 2018: is factored into separate kernels over thesummary state and action spaces kC(c,c′)kA(a,a′). ... 3.3. The agenda-based simulated user. The agenda-based user simulator [24, 25] factorises the user stateinto an agenda and a goal.

20 Dec 2018: 18. 7 Hamming’s breakthrough 20. 8 General considerations 24. 9 Some elementary probability 27. ... 24 Exercise Sheet 1 85. 25 Exercise Sheet 2 90. 26 Exercise Sheet 3 95.

7 Jan 2018: 14. Lighting and Shading (a quick refresher). Recall the classic lighting equation: I = kA kD(N•L) kS(E•R). ... shader. much, much more! 24. Antialiasing on the GPU. Hardware antialiasing can dramatically improve image quality.

22 Feb 2018: 14. Lighting and Shading (a quick refresher). Recall the classic lighting equation: I = kA kD(N•L) kS(E•R). ... shader. much, much more! 24. Antialiasing on the GPU. Hardware antialiasing can dramatically improve image quality.

11 Sep 2018: Advanced Shader. Techniques. 1. Lighting and Shading (a quick refresher). Recall the classic lighting equation: I = kA kD(N•L) kS(E•R). ... ce. 24. Tessellation shaders. How it works: You tell OpenGL how many vertices a single.

13 Mar 2018: áà-ØÙcä)Ü@ÝØàØÙ6ØäÙcÚä_ÙcÚàåfä_ØÞÛÝÜ@ÙcØáâSàÕ'Ü!Kä/WÕcß)Þ ØÙÜ@áà+)ÚÕ_Ø-ØÈáâÙcMÜ@ÙØÙàßÚäØáàÚÚàQâ)ÚáÚ ... áÙcÚ2Öß)ØÖÚß:äcÖ

20 Jun 2018: llm, /J-a'and ka' are usually written with a single curved stroke. ... Date: 3rd century A.H.l9th century A.D. Physical description: 24.5 cm. x 13 cm.

20 Feb 2018: The summary action kernel is defined as:. kA(a,a′) = δa(a. ′) (3). ... 24. Figure 13: The number of times each system queries the user for feedback during on-linepolicy optimisation as a function of the number of training dialogues.

20 Feb 2018: 211. 2121. 1. aannna. annnnn. ka. rd (13). Here nr specifies the number of events that occurred r times and a fixed discounting factor was used if they are zero. ... 52 class n-gram 26.3 25.0 24.9 HVS_52 21.7 20.4 20.1. Table 1: Perplexity for models of

20 Feb 2018: function,Q(b, a) GP (0, k((b, a), (b, a))) wherethe kernelk(, ) is factored into separate kernels over thesummary state and action spaceskB(b, b)kA(a, a). ... 24, no. 4, pp. 562–588, 2010. [10] M. Gǎsić, S. Keizer, F.

15 Oct 2018: 2011-06-28 10:37:24 F1246E04. 2011-06-28 10:37:59 F1241354. 2011-06-28 10:38:34 F1244328. 2011-06-28 10:39:08 F1247348. ... Another is certificates. Sam signs Alice’s public key (and/or signature verification key) CA = sigS{TS,L,A,KA,VA}. •

23 Oct 2018: 1514.23 Reed-Solomon Error Correction. 1554.24 The Queens Problem. 1724.25 Sudoku. 1754.26 The Sliding Blocks Puzzle. ... 24 CHAPTER 3. INTRODUCTION TO LINUX. half with a decription of the possible help commands that are available.

20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]