2525 |
fe_ocean |
DIMACS10 |
143,437 |
143,437 |
819,186 |
Undirected Graph |
2000 |
Matrix Market
2526 |
fe_rotor |
DIMACS10 |
99,617 |
99,617 |
1,324,862 |
Undirected Graph |
2000 |
Matrix Market
2527 |
fe_sphere |
DIMACS10 |
16,386 |
16,386 |
98,304 |
Undirected Graph |
2000 |
Matrix Market
2528 |
fe_tooth |
DIMACS10 |
78,136 |
78,136 |
905,182 |
Undirected Graph |
2000 |
Matrix Market
2529 |
m14b |
DIMACS10 |
214,765 |
214,765 |
3,358,036 |
Undirected Graph |
2000 |
Matrix Market
2530 |
t60k |
DIMACS10 |
60,005 |
60,005 |
178,880 |
Undirected Graph |
2000 |
Matrix Market
2531 |
uk |
DIMACS10 |
4,824 |
4,824 |
13,674 |
Undirected Graph |
2000 |
Matrix Market
2532 |
wing |
DIMACS10 |
62,032 |
62,032 |
243,088 |
Undirected Graph |
2000 |
Matrix Market
2533 |
wing_nodal |
DIMACS10 |
10,937 |
10,937 |
150,976 |
Undirected Graph |
2000 |
Matrix Market
2534 |
HTC_336_4438 |
IPSO |
226,340 |
226,340 |
783,496 |
Power Network Problem |
2011 |
Matrix Market
2535 |
HTC_336_9129 |
IPSO |
226,340 |
226,340 |
762,969 |
Power Network Problem |
2011 |
Matrix Market
2536 |
OPF_10000 |
IPSO |
43,887 |
43,887 |
426,898 |
Power Network Problem |
2011 |
Matrix Market
2537 |
OPF_3754 |
IPSO |
15,435 |
15,435 |
141,478 |
Power Network Problem |
2011 |
Matrix Market
2538 |
OPF_6000 |
IPSO |
29,902 |
29,902 |
274,697 |
Power Network Problem |
2011 |
Matrix Market
2539 |
TSC_OPF_1047 |
IPSO |
8,140 |
8,140 |
2,012,833 |
Power Network Problem |
2011 |
Matrix Market
2540 |
TSC_OPF_300 |
IPSO |
9,774 |
9,774 |
820,783 |
Power Network Problem |
2011 |
Matrix Market
2541 |
Serena |
Janna |
1,391,349 |
1,391,349 |
64,131,971 |
Structural Problem |
2011 |
Matrix Market
2542 |
Emilia_923 |
Janna |
923,136 |
923,136 |
40,373,538 |
Structural Problem |
2011 |
Matrix Market
2543 |
Fault_639 |
Janna |
638,802 |
638,802 |
27,245,944 |
Structural Problem |
2011 |
Matrix Market
2544 |
Flan_1565 |
Janna |
1,564,794 |
1,564,794 |
114,165,372 |
Structural Problem |
2011 |
Matrix Market
2545 |
Geo_1438 |
Janna |
1,437,960 |
1,437,960 |
60,236,322 |
Structural Problem |
2011 |
Matrix Market
2546 |
Hook_1498 |
Janna |
1,498,023 |
1,498,023 |
59,374,451 |
Structural Problem |
2011 |
Matrix Market
2547 |
StocF-1465 |
Janna |
1,465,137 |
1,465,137 |
21,005,389 |
Computational Fluid Dynamics Problem |
2011 |
Matrix Market
2548 |
Cube_Coup_dt0 |
Janna |
2,164,760 |
2,164,760 |
124,406,070 |
Structural Problem |
2012 |
Matrix Market
2549 |
Cube_Coup_dt6 |
Janna |
2,164,760 |
2,164,760 |
124,406,070 |
Structural Problem |
2012 |
Matrix Market
2550 |
Long_Coup_dt0 |
Janna |
1,470,152 |
1,470,152 |
84,422,970 |
Structural Problem |
2012 |
Matrix Market
2551 |
Long_Coup_dt6 |
Janna |
1,470,152 |
1,470,152 |
84,422,970 |
Structural Problem |
2012 |
Matrix Market
2552 |
N_biocarta |
Schulthess |
1,922 |
1,996 |
4,335 |
Biochemical Network |
2012 |
Matrix Market
2553 |
N_pid |
Schulthess |
3,625 |
3,923 |
8,054 |
Biochemical Network |
2012 |
Matrix Market
2554 |
N_reactome |
Schulthess |
10,204 |
16,559 |
43,816 |
Biochemical Network |
2012 |
Matrix Market
2555 |
Chevron1 |
Chevron |
37,365 |
37,365 |
330,633 |
2D/3D Problem |
2012 |
Matrix Market
2556 |
Chevron2 |
Chevron |
90,249 |
90,249 |
803,173 |
2D/3D Problem |
2012 |
Matrix Market
2557 |
Chevron3 |
Chevron |
381,381 |
381,381 |
3,413,113 |
2D/3D Problem |
2012 |
Matrix Market
2558 |
Chevron4 |
Chevron |
711,450 |
711,450 |
6,376,412 |
2D/3D Problem |
2012 |
Matrix Market
2559 |
cz148 |
CPM |
148 |
148 |
1,527 |
2D/3D Problem |
2012 |
Matrix Market
2560 |
cz308 |
CPM |
308 |
308 |
3,182 |
2D/3D Problem |
2012 |
Matrix Market
2561 |
cz628 |
CPM |
628 |
628 |
6,346 |
2D/3D Problem |
2012 |
Matrix Market
2562 |
cz1268 |
CPM |
1,268 |
1,268 |
12,786 |
2D/3D Problem |
2012 |
Matrix Market
2563 |
cz2548 |
CPM |
2,548 |
2,548 |
25,674 |
2D/3D Problem |
2012 |
Matrix Market
2564 |
cz5108 |
CPM |
5,108 |
5,108 |
51,412 |
2D/3D Problem |
2012 |
Matrix Market
2565 |
cz10228 |
CPM |
10,228 |
10,228 |
102,876 |
2D/3D Problem |
2012 |
Matrix Market
2566 |
cz20468 |
CPM |
20,468 |
20,468 |
206,076 |
2D/3D Problem |
2012 |
Matrix Market
2567 |
cz40948 |
CPM |
40,948 |
40,948 |
412,148 |
2D/3D Problem |
2012 |
Matrix Market
2568 |
FX_March2010 |
Davis |
1,319 |
9,498 |
301,899 |
Term/Document Graph |
2010 |
Matrix Market
2569 |
CurlCurl_0 |
Bodendiek |
11,083 |
11,083 |
113,343 |
Model Reduction Problem |
2012 |
Matrix Market
2570 |
CurlCurl_1 |
Bodendiek |
226,451 |
226,451 |
2,472,071 |
Model Reduction Problem |
2012 |
Matrix Market
2571 |
CurlCurl_2 |
Bodendiek |
806,529 |
806,529 |
8,921,789 |
Model Reduction Problem |
2012 |
Matrix Market
2572 |
CurlCurl_3 |
Bodendiek |
1,219,574 |
1,219,574 |
13,544,618 |
Model Reduction Problem |
2012 |
Matrix Market
2573 |
CurlCurl_4 |
Bodendiek |
2,380,515 |
2,380,515 |
26,515,867 |
Model Reduction Problem |
2012 |
Matrix Market
2574 |
G_n_pin_pout |
DIMACS10 |
100,000 |
100,000 |
1,002,396 |
Random Undirected Graph |
2011 |
Matrix Market
2575 |
preferentialAttachment |
DIMACS10 |
100,000 |
100,000 |
999,970 |
Random Undirected Graph |
2011 |
Matrix Market
2576 |
smallworld |
DIMACS10 |
100,000 |
100,000 |
999,996 |
Random Undirected Graph |
2011 |
Matrix Market
2577 |
333SP |
DIMACS10 |
3,712,815 |
3,712,815 |
22,217,266 |
Undirected Graph |
2011 |
Matrix Market
2578 |
AS365 |
DIMACS10 |
3,799,275 |
3,799,275 |
22,736,152 |
Undirected Graph |
2011 |
Matrix Market
2579 |
M6 |
DIMACS10 |
3,501,776 |
3,501,776 |
21,003,872 |
Undirected Graph |
2011 |
Matrix Market
2580 |
NACA0015 |
DIMACS10 |
1,039,183 |
1,039,183 |
6,229,636 |
Undirected Graph |
2011 |
Matrix Market
2581 |
NLR |
DIMACS10 |
4,163,763 |
4,163,763 |
24,975,952 |
Undirected Graph |
2011 |
Matrix Market
2582 |
ak2010 |
DIMACS10 |
45,292 |
45,292 |
217,098 |
Undirected Weighted Graph |
2010 |
Matrix Market
2583 |
al2010 |
DIMACS10 |
252,266 |
252,266 |
1,230,482 |
Undirected Weighted Graph |
2010 |
Matrix Market
2584 |
ar2010 |
DIMACS10 |
186,211 |
186,211 |
904,310 |
Undirected Weighted Graph |
2010 |
Matrix Market
2585 |
az2010 |
DIMACS10 |
241,666 |
241,666 |
1,196,094 |
Undirected Weighted Graph |
2010 |
Matrix Market
2586 |
ca2010 |
DIMACS10 |
710,145 |
710,145 |
3,489,366 |
Undirected Weighted Graph |
2010 |
Matrix Market
2587 |
co2010 |
DIMACS10 |
201,062 |
201,062 |
974,574 |
Undirected Weighted Graph |
2010 |
Matrix Market
2588 |
ct2010 |
DIMACS10 |
67,578 |
67,578 |
336,352 |
Undirected Weighted Graph |
2010 |
Matrix Market
2589 |
de2010 |
DIMACS10 |
24,115 |
24,115 |
116,056 |
Undirected Weighted Graph |
2010 |
Matrix Market
2590 |
fl2010 |
DIMACS10 |
484,481 |
484,481 |
2,346,294 |
Undirected Weighted Graph |
2010 |
Matrix Market
2591 |
ga2010 |
DIMACS10 |
291,086 |
291,086 |
1,418,056 |
Undirected Weighted Graph |
2010 |
Matrix Market
2592 |
hi2010 |
DIMACS10 |
25,016 |
25,016 |
124,126 |
Undirected Weighted Graph |
2010 |
Matrix Market
2593 |
ia2010 |
DIMACS10 |
216,007 |
216,007 |
1,021,170 |
Undirected Weighted Graph |
2010 |
Matrix Market
2594 |
id2010 |
DIMACS10 |
149,842 |
149,842 |
728,264 |
Undirected Weighted Graph |
2010 |
Matrix Market
2595 |
il2010 |
DIMACS10 |
451,554 |
451,554 |
2,164,464 |
Undirected Weighted Graph |
2010 |
Matrix Market
2596 |
in2010 |
DIMACS10 |
267,071 |
267,071 |
1,281,716 |
Undirected Weighted Graph |
2010 |
Matrix Market
2597 |
ks2010 |
DIMACS10 |
238,600 |
238,600 |
1,121,798 |
Undirected Weighted Graph |
2010 |
Matrix Market
2598 |
ky2010 |
DIMACS10 |
161,672 |
161,672 |
787,778 |
Undirected Weighted Graph |
2010 |
Matrix Market
2599 |
la2010 |
DIMACS10 |
204,447 |
204,447 |
980,634 |
Undirected Weighted Graph |
2010 |
Matrix Market
2600 |
ma2010 |
DIMACS10 |
157,508 |
157,508 |
776,610 |
Undirected Weighted Graph |
2010 |
Matrix Market
2601 |
md2010 |
DIMACS10 |
145,247 |
145,247 |
700,378 |
Undirected Weighted Graph |
2010 |
Matrix Market
2602 |
me2010 |
DIMACS10 |
69,518 |
69,518 |
335,476 |
Undirected Weighted Graph |
2010 |
Matrix Market
2603 |
mi2010 |
DIMACS10 |
329,885 |
329,885 |
1,578,090 |
Undirected Weighted Graph |
2010 |
Matrix Market
2604 |
mn2010 |
DIMACS10 |
259,777 |
259,777 |
1,227,102 |
Undirected Weighted Graph |
2010 |
Matrix Market
2605 |
mo2010 |
DIMACS10 |
343,565 |
343,565 |
1,656,568 |
Undirected Weighted Graph |
2010 |
Matrix Market
2606 |
ms2010 |
DIMACS10 |
171,778 |
171,778 |
839,980 |
Undirected Weighted Graph |
2010 |
Matrix Market
2607 |
mt2010 |
DIMACS10 |
132,288 |
132,288 |
638,668 |
Undirected Weighted Graph |
2010 |
Matrix Market
2608 |
nc2010 |
DIMACS10 |
288,987 |
288,987 |
1,416,620 |
Undirected Weighted Graph |
2010 |
Matrix Market
2609 |
nd2010 |
DIMACS10 |
133,769 |
133,769 |
625,946 |
Undirected Weighted Graph |
2010 |
Matrix Market
2610 |
ne2010 |
DIMACS10 |
193,352 |
193,352 |
913,854 |
Undirected Weighted Graph |
2010 |
Matrix Market
2611 |
nh2010 |
DIMACS10 |
48,837 |
48,837 |
234,550 |
Undirected Weighted Graph |
2010 |
Matrix Market
2612 |
nj2010 |
DIMACS10 |
169,588 |
169,588 |
829,912 |
Undirected Weighted Graph |
2010 |
Matrix Market
2613 |
nm2010 |
DIMACS10 |
168,609 |
168,609 |
830,970 |
Undirected Weighted Graph |
2010 |
Matrix Market
2614 |
nv2010 |
DIMACS10 |
84,538 |
84,538 |
416,998 |
Undirected Weighted Graph |
2010 |
Matrix Market
2615 |
ny2010 |
DIMACS10 |
350,169 |
350,169 |
1,709,544 |
Undirected Weighted Graph |
2010 |
Matrix Market
2616 |
oh2010 |
DIMACS10 |
365,344 |
365,344 |
1,768,240 |
Undirected Weighted Graph |
2010 |
Matrix Market
2617 |
ok2010 |
DIMACS10 |
269,118 |
269,118 |
1,274,148 |
Undirected Weighted Graph |
2010 |
Matrix Market
2618 |
or2010 |
DIMACS10 |
196,621 |
196,621 |
979,512 |
Undirected Weighted Graph |
2010 |
Matrix Market
2619 |
pa2010 |
DIMACS10 |
421,545 |
421,545 |
2,058,462 |
Undirected Weighted Graph |
2010 |
Matrix Market
2620 |
ri2010 |
DIMACS10 |
25,181 |
25,181 |
125,750 |
Undirected Weighted Graph |
2010 |
Matrix Market
2621 |
sc2010 |
DIMACS10 |
181,908 |
181,908 |
893,160 |
Undirected Weighted Graph |
2010 |
Matrix Market
2622 |
sd2010 |
DIMACS10 |
88,360 |
88,360 |
410,722 |
Undirected Weighted Graph |
2010 |
Matrix Market
2623 |
tn2010 |
DIMACS10 |
240,116 |
240,116 |
1,193,966 |
Undirected Weighted Graph |
2010 |
Matrix Market
2624 |
tx2010 |
DIMACS10 |
914,231 |
914,231 |
4,456,272 |
Undirected Weighted Graph |
2010 |
Matrix Market
2625 |
ut2010 |
DIMACS10 |
115,406 |
115,406 |
572,066 |
Undirected Weighted Graph |
2010 |
Matrix Market
2626 |
va2010 |
DIMACS10 |
285,762 |
285,762 |
1,402,128 |
Undirected Weighted Graph |
2010 |
Matrix Market
2627 |
vt2010 |
DIMACS10 |
32,580 |
32,580 |
155,598 |
Undirected Weighted Graph |
2010 |
Matrix Market
2628 |
wa2010 |
DIMACS10 |
195,574 |
195,574 |
947,432 |
Undirected Weighted Graph |
2010 |
Matrix Market
2629 |
wi2010 |
DIMACS10 |
253,096 |
253,096 |
1,209,404 |
Undirected Weighted Graph |
2010 |
Matrix Market
2630 |
wv2010 |
DIMACS10 |
135,218 |
135,218 |
662,922 |
Undirected Weighted Graph |
2010 |
Matrix Market
2631 |
wy2010 |
DIMACS10 |
86,204 |
86,204 |
427,586 |
Undirected Weighted Graph |
2010 |
Matrix Market
2632 |
vsp_barth5_1Ksep_50in_5Kout |
DIMACS10 |
32,212 |
32,212 |
203,610 |
Random Unweighted Graph |
2011 |
Matrix Market
2633 |
vsp_bcsstk30_500sep_10in_1Kout |
DIMACS10 |
58,348 |
58,348 |
4,033,156 |
Random Unweighted Graph |
2011 |
Matrix Market
2634 |
vsp_befref_fxm_2_4_air02 |
DIMACS10 |
14,109 |
14,109 |
196,448 |
Random Unweighted Graph |
2011 |
Matrix Market
2635 |
vsp_bump2_e18_aa01_model1_crew1 |
DIMACS10 |
56,438 |
56,438 |
601,602 |
Random Unweighted Graph |
2011 |
Matrix Market
2636 |
vsp_c-30_data_data |
DIMACS10 |
11,023 |
11,023 |
124,368 |
Random Unweighted Graph |
2011 |
Matrix Market
2637 |
vsp_c-60_data_cti_cs4 |
DIMACS10 |
85,830 |
85,830 |
482,160 |
Random Unweighted Graph |
2011 |
Matrix Market
2638 |
vsp_data_and_seymourl |
DIMACS10 |
9,167 |
9,167 |
111,732 |
Random Unweighted Graph |
2011 |
Matrix Market
2639 |
vsp_finan512_scagr7-2c_rlfddd |
DIMACS10 |
139,752 |
139,752 |
1,104,040 |
Random Unweighted Graph |
2011 |
Matrix Market
2640 |
vsp_mod2_pgp2_slptsk |
DIMACS10 |
101,364 |
101,364 |
778,736 |
Random Unweighted Graph |
2011 |
Matrix Market
2641 |
vsp_model1_crew1_cr42_south31 |
DIMACS10 |
45,101 |
45,101 |
379,952 |
Random Unweighted Graph |
2011 |
Matrix Market
2642 |
vsp_msc10848_300sep_100in_1Kout |
DIMACS10 |
21,996 |
21,996 |
2,442,056 |
Random Unweighted Graph |
2011 |
Matrix Market
2643 |
vsp_p0291_seymourl_iiasa |
DIMACS10 |
10,498 |
10,498 |
107,736 |
Random Unweighted Graph |
2011 |
Matrix Market
2644 |
vsp_sctap1-2b_and_seymourl |
DIMACS10 |
40,174 |
40,174 |
281,662 |
Random Unweighted Graph |
2011 |
Matrix Market
2645 |
vsp_south31_slptsk |
DIMACS10 |
39,668 |
39,668 |
379,828 |
Random Unweighted Graph |
2011 |
Matrix Market
2646 |
vsp_vibrobox_scagr7-2c_rlfddd |
DIMACS10 |
77,328 |
77,328 |
871,172 |
Random Unweighted Graph |
2011 |
Matrix Market
2647 |
CoupCons3D |
Janna |
416,800 |
416,800 |
17,277,420 |
Structural Problem |
2012 |
Matrix Market
2648 |
ML_Laplace |
Janna |
377,002 |
377,002 |
27,582,698 |
Structural Problem |
2012 |
Matrix Market
2649 |
Transport |
Janna |
1,602,111 |
1,602,111 |
23,487,281 |
Structural Problem |
2012 |
Matrix Market
2650 |
ML_Geer |
Janna |
1,504,002 |
1,504,002 |
110,686,677 |
Structural Problem |
2012 |
Matrix Market
2651 |
Delor64K |
ANSYS |
64,719 |
1,785,345 |
652,140 |
Least Squares Problem |
2011 |
Matrix Market
2652 |
Delor295K |
ANSYS |
295,734 |
1,823,928 |
2,401,323 |
Least Squares Problem |
2011 |
Matrix Market
2653 |
Delor338K |
ANSYS |
343,236 |
887,058 |
4,211,599 |
Least Squares Problem |
2011 |
Matrix Market
2654 |
Linux_call_graph |
Sorensen |
324,085 |
324,085 |
1,208,908 |
Directed Weighted Graph |
2013 |
Matrix Market
2655 |
LeGresley_2508 |
LeGresley |
2,508 |
2,508 |
16,727 |
Power Network Problem |
2013 |
Matrix Market
2656 |
LeGresley_4908 |
LeGresley |
4,908 |
4,908 |
30,482 |
Power Network Problem |
2013 |
Matrix Market
2657 |
LeGresley_87936 |
LeGresley |
87,936 |
87,936 |
593,276 |
Power Network Problem |
2013 |
Matrix Market
2658 |
specular |
Brogan |
477,976 |
1,600 |
7,647,040 |
Computer Vision Problem |
2014 |
Matrix Market
2659 |
Bump_2911 |
Janna |
2,911,419 |
2,911,419 |
127,729,899 |
2D/3D Problem |
2014 |
Matrix Market
2660 |
Queen_4147 |
Janna |
4,147,110 |
4,147,110 |
316,548,962 |
2D/3D Problem |
2014 |
Matrix Market
2661 |
PFlow_742 |
Janna |
742,793 |
742,793 |
37,138,461 |
2D/3D Problem |
2014 |
Matrix Market
2662 |
Freescale2 |
Freescale |
2,999,349 |
2,999,349 |
14,313,235 |
Circuit Simulation Matrix |
2015 |
Matrix Market
2663 |
MISKnowledgeMap |
Moqri |
2,427 |
2,427 |
57,022 |
Undirected Weighted Graph |
2015 |
Matrix Market
2664 |
bundle_adj |
Mazaheri |
513,351 |
513,351 |
20,207,907 |
Computer Vision Problem |
2015 |
Matrix Market
2665 |
dynamicSoaringProblem_1 |
VDOL |
647 |
647 |
5,367 |
Optimal Control Problem |
2015 |
Matrix Market
2666 |
dynamicSoaringProblem_2 |
VDOL |
1,591 |
1,591 |
15,588 |
Optimal Control Problem |
2015 |
Matrix Market
2667 |
dynamicSoaringProblem_3 |
VDOL |
2,871 |
2,871 |
32,022 |
Optimal Control Problem |
2015 |
Matrix Market
2668 |
dynamicSoaringProblem_4 |
VDOL |
3,191 |
3,191 |
36,516 |
Optimal Control Problem |
2015 |
Matrix Market
2669 |
dynamicSoaringProblem_5 |
VDOL |
3,271 |
3,271 |
36,789 |
Optimal Control Problem |
2015 |
Matrix Market
2670 |
dynamicSoaringProblem_6 |
VDOL |
3,431 |
3,431 |
36,741 |
Optimal Control Problem |
2015 |
Matrix Market
2671 |
dynamicSoaringProblem_7 |
VDOL |
3,511 |
3,511 |
37,680 |
Optimal Control Problem |
2015 |
Matrix Market
2672 |
dynamicSoaringProblem_8 |
VDOL |
3,543 |
3,543 |
38,136 |
Optimal Control Problem |
2015 |
Matrix Market
2673 |
freeFlyingRobot_1 |
VDOL |
798 |
798 |
5,246 |
Optimal Control Problem |
2015 |
Matrix Market
2674 |
freeFlyingRobot_2 |
VDOL |
1,338 |
1,338 |
11,600 |
Optimal Control Problem |
2015 |
Matrix Market
2675 |
freeFlyingRobot_3 |
VDOL |
1,718 |
1,718 |
12,922 |
Optimal Control Problem |
2015 |
Matrix Market
2676 |
freeFlyingRobot_4 |
VDOL |
2,358 |
2,358 |
18,218 |
Optimal Control Problem |
2015 |
Matrix Market
2677 |
freeFlyingRobot_5 |
VDOL |
2,878 |
2,878 |
24,582 |
Optimal Control Problem |
2015 |
Matrix Market
2678 |
freeFlyingRobot_6 |
VDOL |
3,358 |
3,358 |
27,030 |
Optimal Control Problem |
2015 |
Matrix Market
2679 |
freeFlyingRobot_7 |
VDOL |
3,918 |
3,918 |
31,046 |
Optimal Control Problem |
2015 |
Matrix Market
2680 |
freeFlyingRobot_8 |
VDOL |
4,398 |
4,398 |
34,958 |
Optimal Control Problem |
2015 |
Matrix Market
2681 |
freeFlyingRobot_9 |
VDOL |
4,778 |
4,778 |
39,964 |
Optimal Control Problem |
2015 |
Matrix Market
2682 |
freeFlyingRobot_10 |
VDOL |
5,218 |
5,218 |
40,080 |
Optimal Control Problem |
2015 |
Matrix Market
2683 |
freeFlyingRobot_11 |
VDOL |
5,438 |
5,438 |
40,054 |
Optimal Control Problem |
2015 |
Matrix Market
2684 |
freeFlyingRobot_12 |
VDOL |
5,578 |
5,578 |
41,940 |
Optimal Control Problem |
2015 |
Matrix Market
2685 |
freeFlyingRobot_13 |
VDOL |
5,718 |
5,718 |
43,994 |
Optimal Control Problem |
2015 |
Matrix Market
2686 |
freeFlyingRobot_14 |
VDOL |
5,958 |
5,958 |
43,298 |
Optimal Control Problem |
2015 |
Matrix Market
2687 |
freeFlyingRobot_15 |
VDOL |
6,038 |
6,038 |
43,378 |
Optimal Control Problem |
2015 |
Matrix Market
2688 |
freeFlyingRobot_16 |
VDOL |
6,078 |
6,078 |
44,006 |
Optimal Control Problem |
2015 |
Matrix Market
2689 |
goddardRocketProblem_1 |
VDOL |
831 |
831 |
8,498 |
Optimal Control Problem |
2015 |
Matrix Market
2690 |
goddardRocketProblem_2 |
VDOL |
867 |
867 |
9,058 |
Optimal Control Problem |
2015 |
Matrix Market
2691 |
hangGlider_1 |
VDOL |
360 |
360 |
3,477 |
Optimal Control Problem |
2015 |
Matrix Market
2692 |
hangGlider_2 |
VDOL |
1,647 |
1,647 |
14,754 |
Optimal Control Problem |
2015 |
Matrix Market
2693 |
hangGlider_3 |
VDOL |
10,260 |
10,260 |
92,703 |
Optimal Control Problem |
2015 |
Matrix Market
2694 |
hangGlider_4 |
VDOL |
15,561 |
15,561 |
149,532 |
Optimal Control Problem |
2015 |
Matrix Market
2695 |
hangGlider_5 |
VDOL |
16,011 |
16,011 |
155,246 |
Optimal Control Problem |
2015 |
Matrix Market
2696 |
kineticBatchReactor_1 |
VDOL |
2,052 |
2,052 |
20,600 |
Optimal Control Problem |
2015 |
Matrix Market
2697 |
kineticBatchReactor_2 |
VDOL |
4,361 |
4,361 |
44,840 |
Optimal Control Problem |
2015 |
Matrix Market
2698 |
kineticBatchReactor_3 |
VDOL |
5,082 |
5,082 |
53,219 |
Optimal Control Problem |
2015 |
Matrix Market
2699 |
kineticBatchReactor_4 |
VDOL |
7,105 |
7,105 |
74,869 |
Optimal Control Problem |
2015 |
Matrix Market
2700 |
kineticBatchReactor_5 |
VDOL |
7,641 |
7,641 |
80,767 |
Optimal Control Problem |
2015 |
Matrix Market
2701 |
kineticBatchReactor_6 |
VDOL |
7,773 |
7,773 |
82,500 |
Optimal Control Problem |
2015 |
Matrix Market
2702 |
kineticBatchReactor_7 |
VDOL |
7,968 |
7,968 |
84,508 |
Optimal Control Problem |
2015 |
Matrix Market
2703 |
kineticBatchReactor_8 |
VDOL |
8,052 |
8,052 |
85,194 |
Optimal Control Problem |
2015 |
Matrix Market
2704 |
kineticBatchReactor_9 |
VDOL |
8,115 |
8,115 |
86,183 |
Optimal Control Problem |
2015 |
Matrix Market
2705 |
lowThrust_1 |
VDOL |
584 |
584 |
6,133 |
Optimal Control Problem |
2015 |
Matrix Market
2706 |
lowThrust_2 |
VDOL |
2,312 |
2,312 |
26,261 |
Optimal Control Problem |
2015 |
Matrix Market
2707 |
lowThrust_3 |
VDOL |
7,064 |
7,064 |
80,645 |
Optimal Control Problem |
2015 |
Matrix Market
2708 |
lowThrust_4 |
VDOL |
13,562 |
13,562 |
160,947 |
Optimal Control Problem |
2015 |
Matrix Market
2709 |
lowThrust_5 |
VDOL |
16,262 |
16,262 |
198,369 |
Optimal Control Problem |
2015 |
Matrix Market
2710 |
lowThrust_6 |
VDOL |
16,928 |
16,928 |
207,349 |
Optimal Control Problem |
2015 |
Matrix Market
2711 |
lowThrust_7 |
VDOL |
17,378 |
17,378 |
211,561 |
Optimal Control Problem |
2015 |
Matrix Market
2712 |
lowThrust_8 |
VDOL |
17,702 |
17,702 |
216,445 |
Optimal Control Problem |
2015 |
Matrix Market
2713 |
lowThrust_9 |
VDOL |
18,044 |
18,044 |
219,589 |
Optimal Control Problem |
2015 |
Matrix Market
2714 |
lowThrust_10 |
VDOL |
18,260 |
18,260 |
222,005 |
Optimal Control Problem |
2015 |
Matrix Market
2715 |
lowThrust_11 |
VDOL |
18,368 |
18,368 |
223,801 |
Optimal Control Problem |
2015 |
Matrix Market
2716 |
lowThrust_12 |
VDOL |
18,458 |
18,458 |
224,593 |
Optimal Control Problem |
2015 |
Matrix Market
2717 |
lowThrust_13 |
VDOL |
18,476 |
18,476 |
224,897 |
Optimal Control Problem |
2015 |
Matrix Market
2718 |
orbitRaising_1 |
VDOL |
442 |
442 |
2,906 |
Optimal Control Problem |
2015 |
Matrix Market
2719 |
orbitRaising_2 |
VDOL |
739 |
739 |
6,526 |
Optimal Control Problem |
2015 |
Matrix Market
2720 |
orbitRaising_3 |
VDOL |
761 |
761 |
6,098 |
Optimal Control Problem |
2015 |
Matrix Market
2721 |
orbitRaising_4 |
VDOL |
915 |
915 |
7,790 |
Optimal Control Problem |
2015 |
Matrix Market
2722 |
reorientation_1 |
VDOL |
677 |
677 |
7,326 |
Optimal Control Problem |
2015 |
Matrix Market
2723 |
reorientation_2 |
VDOL |
1,544 |
1,544 |
17,910 |
Optimal Control Problem |
2015 |
Matrix Market
2724 |
reorientation_3 |
VDOL |
2,513 |
2,513 |
32,166 |
Optimal Control Problem |
2015 |
Matrix Market
2725 |
reorientation_4 |
VDOL |
2,717 |
2,717 |
33,630 |
Optimal Control Problem |
2015 |
Matrix Market
2726 |
reorientation_5 |
VDOL |
2,904 |
2,904 |
35,590 |
Optimal Control Problem |
2015 |
Matrix Market
2727 |
reorientation_6 |
VDOL |
3,006 |
3,006 |
37,342 |
Optimal Control Problem |
2015 |
Matrix Market
2728 |
reorientation_7 |
VDOL |
3,057 |
3,057 |
37,150 |
Optimal Control Problem |
2015 |
Matrix Market
2729 |
reorientation_8 |
VDOL |
3,108 |
3,108 |
37,894 |
Optimal Control Problem |
2015 |
Matrix Market
2730 |
spaceShuttleEntry_1 |
VDOL |
560 |
560 |
6,891 |
Optimal Control Problem |
2015 |
Matrix Market
2731 |
spaceShuttleEntry_2 |
VDOL |
1,428 |
1,428 |
24,073 |
Optimal Control Problem |
2015 |
Matrix Market
2732 |
spaceShuttleEntry_3 |
VDOL |
1,834 |
1,834 |
28,757 |
Optimal Control Problem |
2015 |
Matrix Market
2733 |
spaceShuttleEntry_4 |
VDOL |
2,450 |
2,450 |
39,669 |
Optimal Control Problem |
2015 |
Matrix Market
2734 |
spaceStation_1 |
VDOL |
99 |
99 |
927 |
Optimal Control Problem |
2015 |
Matrix Market
2735 |
spaceStation_2 |
VDOL |
329 |
329 |
5,817 |
Optimal Control Problem |
2015 |
Matrix Market
2736 |
spaceStation_3 |
VDOL |
467 |
467 |
5,103 |
Optimal Control Problem |
2015 |
Matrix Market
2737 |
spaceStation_4 |
VDOL |
950 |
950 |
13,626 |
Optimal Control Problem |
2015 |
Matrix Market
2738 |
spaceStation_5 |
VDOL |
1,019 |
1,019 |
15,219 |
Optimal Control Problem |
2015 |
Matrix Market
2739 |
spaceStation_6 |
VDOL |
1,111 |
1,111 |
17,595 |
Optimal Control Problem |
2015 |
Matrix Market
2740 |
spaceStation_7 |
VDOL |
1,134 |
1,134 |
18,252 |
Optimal Control Problem |
2015 |
Matrix Market
2741 |
spaceStation_8 |
VDOL |
1,157 |
1,157 |
18,945 |
Optimal Control Problem |
2015 |
Matrix Market
2742 |
spaceStation_9 |
VDOL |
1,180 |
1,180 |
19,674 |
Optimal Control Problem |
2015 |
Matrix Market
2743 |
spaceStation_10 |
VDOL |
1,272 |
1,272 |
17,478 |
Optimal Control Problem |
2015 |
Matrix Market
2744 |
spaceStation_11 |
VDOL |
1,364 |
1,364 |
18,954 |
Optimal Control Problem |
2015 |
Matrix Market
2745 |
spaceStation_12 |
VDOL |
1,410 |
1,410 |
19,728 |
Optimal Control Problem |
2015 |
Matrix Market
2746 |
spaceStation_13 |
VDOL |
1,617 |
1,617 |
23,649 |
Optimal Control Problem |
2015 |
Matrix Market
2747 |
spaceStation_14 |
VDOL |
1,640 |
1,640 |
24,324 |
Optimal Control Problem |
2015 |
Matrix Market
2748 |
tumorAntiAngiogenesis_1 |
VDOL |
205 |
205 |
1,783 |
Optimal Control Problem |
2015 |
Matrix Market
2749 |
tumorAntiAngiogenesis_2 |
VDOL |
305 |
305 |
2,699 |
Optimal Control Problem |
2015 |
Matrix Market
2750 |
tumorAntiAngiogenesis_3 |
VDOL |
410 |
410 |
3,952 |
Optimal Control Problem |
2015 |
Matrix Market
2751 |
tumorAntiAngiogenesis_4 |
VDOL |
455 |
455 |
4,593 |
Optimal Control Problem |
2015 |
Matrix Market
2752 |
tumorAntiAngiogenesis_5 |
VDOL |
460 |
460 |
4,690 |
Optimal Control Problem |
2015 |
Matrix Market
2753 |
tumorAntiAngiogenesis_6 |
VDOL |
465 |
465 |
4,795 |
Optimal Control Problem |
2015 |
Matrix Market
2754 |
tumorAntiAngiogenesis_7 |
VDOL |
475 |
475 |
4,597 |
Optimal Control Problem |
2015 |
Matrix Market
2755 |
tumorAntiAngiogenesis_8 |
VDOL |
490 |
490 |
4,776 |
Optimal Control Problem |
2015 |
Matrix Market
2756 |
iChem_Jacobian |
Meng |
274,087 |
274,087 |
4,137,369 |
Computational Chemistry Problem |
2015 |
Matrix Market
2757 |
JP |
Harvard_Seismology |
87,616 |
67,320 |
13,734,559 |
Tomography Problem |
2015 |
Matrix Market
2758 |
mycielskian2 |
Mycielski |
2 |
2 |
2 |
Undirected Graph |
2018 |
Matrix Market
2759 |
mycielskian3 |
Mycielski |
5 |
5 |
10 |
Undirected Graph |
2018 |
Matrix Market
2760 |
mycielskian4 |
Mycielski |
11 |
11 |
40 |
Undirected Graph |
2018 |
Matrix Market
2761 |
mycielskian5 |
Mycielski |
23 |
23 |
142 |
Undirected Graph |
2018 |
Matrix Market
2762 |
mycielskian6 |
Mycielski |
47 |
47 |
472 |
Undirected Graph |
2018 |
Matrix Market
2763 |
mycielskian7 |
Mycielski |
95 |
95 |
1,510 |
Undirected Graph |
2018 |
Matrix Market
2764 |
mycielskian8 |
Mycielski |
191 |
191 |
4,720 |
Undirected Graph |
2018 |
Matrix Market
2765 |
mycielskian9 |
Mycielski |
383 |
383 |
14,542 |
Undirected Graph |
2018 |
Matrix Market
2766 |
mycielskian10 |
Mycielski |
767 |
767 |
44,392 |
Undirected Graph |
2018 |
Matrix Market
2767 |
mycielskian11 |
Mycielski |
1,535 |
1,535 |
134,710 |
Undirected Graph |
2018 |
Matrix Market
2768 |
mycielskian12 |
Mycielski |
3,071 |
3,071 |
407,200 |
Undirected Graph |
2018 |
Matrix Market
2769 |
mycielskian13 |
Mycielski |
6,143 |
6,143 |
1,227,742 |
Undirected Graph |
2018 |
Matrix Market
2770 |
mycielskian14 |
Mycielski |
12,287 |
12,287 |
3,695,512 |
Undirected Graph |
2018 |
Matrix Market
2771 |
mycielskian15 |
Mycielski |
24,575 |
24,575 |
11,111,110 |
Undirected Graph |
2018 |
Matrix Market
2772 |
mycielskian16 |
Mycielski |
49,151 |
49,151 |
33,382,480 |
Undirected Graph |
2018 |
Matrix Market
2773 |
mycielskian17 |
Mycielski |
98,303 |
98,303 |
100,245,742 |
Undirected Graph |
2018 |
Matrix Market
2774 |
mycielskian18 |
Mycielski |
196,607 |
196,607 |
300,933,832 |
Undirected Graph |
2018 |
Matrix Market
2775 |
mycielskian19 |
Mycielski |
393,215 |
393,215 |
903,194,710 |
Undirected Graph |
2018 |
Matrix Market
2776 |
mycielskian20 |
Mycielski |
786,431 |
786,431 |
2,710,370,560 |
Undirected Graph |
2018 |
Matrix Market
2777 |
CollegeMsg |
SNAP |
1,899 |
1,899 |
20,296 |
Directed Temporal Multigraph |
2009 |
Matrix Market
2778 |
com-Amazon |
SNAP |
334,863 |
334,863 |
1,851,744 |
Undirected Graph With Communities |
2012 |
Matrix Market
2779 |
com-DBLP |
SNAP |
317,080 |
317,080 |
2,099,732 |
Undirected Graph With Communities |
2012 |
Matrix Market
2780 |
com-Friendster |
SNAP |
65,608,366 |
65,608,366 |
3,612,134,270 |
Undirected Graph With Communities |
2012 |
Matrix Market
2781 |
com-LiveJournal |
SNAP |
3,997,962 |
3,997,962 |
69,362,378 |
Undirected Graph With Communities |
2012 |
Matrix Market
2782 |
com-Orkut |
SNAP |
3,072,441 |
3,072,441 |
234,370,166 |
Undirected Graph With Communities |
2012 |
Matrix Market
2783 |
com-Youtube |
SNAP |
1,134,890 |
1,134,890 |
5,975,248 |
Undirected Graph With Communities |
2012 |
Matrix Market
2784 |
email-Eu-core |
SNAP |
1,005 |
1,005 |
25,571 |
Directed Graph With Communities |
2007 |
Matrix Market
2785 |
email-Eu-core-temporal |
SNAP |
1,005 |
1,005 |
24,929 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2786 |
higgs-twitter |
SNAP |
456,626 |
456,626 |
14,855,842 |
Directed Temporal Multigraph |
2015 |
Matrix Market
2787 |
loc-Brightkite |
SNAP |
58,228 |
58,228 |
428,156 |
Undirected Graph |
2011 |
Matrix Market
2788 |
loc-Gowalla |
SNAP |
196,591 |
196,591 |
1,900,654 |
Undirected Graph |
2011 |
Matrix Market
2789 |
soc-Pokec |
SNAP |
1,632,803 |
1,632,803 |
30,622,564 |
Directed Graph |
2012 |
Matrix Market
2790 |
soc-sign-bitcoin-alpha |
SNAP |
3,783 |
3,783 |
24,186 |
Directed Weighted Temporal Graph |
2016 |
Matrix Market
2791 |
soc-sign-bitcoin-otc |
SNAP |
5,881 |
5,881 |
35,592 |
Directed Weighted Temporal Graph |
2016 |
Matrix Market
2792 |
sx-askubuntu |
SNAP |
159,316 |
159,316 |
596,933 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2793 |
sx-mathoverflow |
SNAP |
24,818 |
24,818 |
239,978 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2794 |
sx-stackoverflow |
SNAP |
2,601,977 |
2,601,977 |
36,233,450 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2795 |
sx-superuser |
SNAP |
194,085 |
194,085 |
924,886 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2796 |
twitter7 |
SNAP |
41,652,230 |
41,652,230 |
1,468,365,182 |
Directed Graph |
2010 |
Matrix Market
2797 |
wiki-RfA |
SNAP |
11,380 |
11,380 |
188,077 |
Directed Weighted Temporal Multigraph |
2014 |
Matrix Market
2798 |
wiki-talk-temporal |
SNAP |
1,140,149 |
1,140,149 |
3,309,592 |
Directed Temporal Multigraph |
2017 |
Matrix Market
2799 |
wiki-topcats |
SNAP |
1,791,489 |
1,791,489 |
28,511,807 |
Directed Graph With Communities |
2011 |
Matrix Market
2800 |
mawi_201512012345 |
MAWI |
18,571,154 |
18,571,154 |
38,040,320 |
Undirected Weighted Graph |
2015 |
Matrix Market
2801 |
mawi_201512020000 |
MAWI |
35,991,342 |
35,991,342 |
74,485,420 |
Undirected Weighted Graph |
2015 |
Matrix Market
2802 |
mawi_201512020030 |
MAWI |
68,863,315 |
68,863,315 |
143,414,960 |
Undirected Weighted Graph |
2015 |
Matrix Market
2803 |
mawi_201512020130 |
MAWI |
128,568,730 |
128,568,730 |
270,234,840 |
Undirected Weighted Graph |
2015 |
Matrix Market
2804 |
mawi_201512020330 |
MAWI |
226,196,185 |
226,196,185 |
480,047,894 |
Undirected Weighted Graph |
2015 |
Matrix Market
2805 |
kmer_A2a |
GenBank |
170,728,175 |
170,728,175 |
360,585,172 |
Undirected Graph |
2015 |
Matrix Market
2806 |
kmer_P1a |
GenBank |
139,353,211 |
139,353,211 |
297,829,984 |
Undirected Graph |
2015 |
Matrix Market
2807 |
kmer_U1a |
GenBank |
67,716,231 |
67,716,231 |
138,778,562 |
Undirected Graph |
2015 |
Matrix Market
2808 |
kmer_V1r |
GenBank |
214,005,017 |
214,005,017 |
465,410,904 |
Undirected Graph |
2015 |
Matrix Market
2809 |
kmer_V2a |
GenBank |
55,042,369 |
55,042,369 |
117,217,600 |
Undirected Graph |
2015 |
Matrix Market
2810 |
MOLIERE_2016 |
Sybrandt |
30,239,687 |
30,239,687 |
6,669,254,694 |
Undirected Weighted Graph |
2017 |
Matrix Market
2811 |
TEM27623 |
Guettel |
27,623 |
27,623 |
439,469 |
Electromagnetics Problem |
2015 |
Matrix Market
2812 |
TEM152078 |
Guettel |
152,078 |
152,078 |
6,459,326 |
Electromagnetics Problem |
2015 |
Matrix Market
2813 |
TEM181302 |
Guettel |
181,302 |
181,302 |
7,839,010 |
Electromagnetics Problem |
2015 |
Matrix Market
2814 |
windtunnel_evap2d |
Grueninger |
8,256 |
8,256 |
109,368 |
Computational Fluid Dynamics Problem |
2017 |
Matrix Market
2815 |
windtunnel_evap3d |
Grueninger |
40,816 |
40,816 |
803,978 |
Computational Fluid Dynamics Problem |
2017 |
Matrix Market
2816 |
power197k |
PowerSystem |
197,156 |
197,156 |
805,839 |
Power Network Problem |
2017 |
Matrix Market
2817 |
Goodwin_010 |
Goodwin |
1,182 |
1,182 |
32,282 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2818 |
Goodwin_013 |
Goodwin |
1,965 |
1,965 |
56,069 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2819 |
Goodwin_017 |
Goodwin |
3,317 |
3,317 |
97,773 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2820 |
Goodwin_023 |
Goodwin |
6,005 |
6,005 |
182,168 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2821 |
Goodwin_030 |
Goodwin |
10,142 |
10,142 |
312,814 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2822 |
Goodwin_040 |
Goodwin |
17,922 |
17,922 |
561,677 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2823 |
Goodwin_054 |
Goodwin |
32,510 |
32,510 |
1,030,878 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2824 |
Goodwin_071 |
Goodwin |
56,021 |
56,021 |
1,797,934 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2825 |
Goodwin_095 |
Goodwin |
100,037 |
100,037 |
3,226,066 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2826 |
Goodwin_127 |
Goodwin |
178,437 |
178,437 |
5,778,545 |
Computational Fluid Dynamics |
2018 |
Matrix Market
2827 |
ACTIVSg2000 |
TAMU_SmartGridCenter |
4,000 |
4,000 |
28,505 |
Power Network Problem |
2018 |
Matrix Market
2828 |
ACTIVSg10K |
TAMU_SmartGridCenter |
20,000 |
20,000 |
135,888 |
Power Network Problem |
2018 |
Matrix Market
2829 |
ACTIVSg70K |
TAMU_SmartGridCenter |
69,999 |
69,999 |
238,627 |
Power Network Problem |
2018 |
Matrix Market
2830 |
ifiss_mat |
Embree |
96,307 |
96,307 |
3,599,932 |
Computational Fluid Dynamics |
2015 |
Matrix Market
2831 |
Hardesty1 |
Hardesty |
938,905 |
938,905 |
12,143,314 |
Computer Graphics/Vision Problem |
2013 |
Matrix Market
2832 |
Hardesty2 |
Hardesty |
929,901 |
303,645 |
4,020,731 |
Computer Graphics/Vision Problem |
2015 |
Matrix Market
2833 |
Hardesty3 |
Hardesty |
8,217,820 |
7,591,564 |
40,451,632 |
Computer Graphics/Vision Problem |
2015 |
Matrix Market


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Prompt for Wednesday, Feb 10. D3 axes, Calculating π.
A scale is an abstract mapping from data to aesthetics. Scales are closely related to guides and axes. Here's an example of a useful d3 tool for building axes for position scales. We'll use it to create a stochastic estimate of π.


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{{- partialCached "utils/css.html" (dict "context" . "filefolder" "object" "filename" "breadcrumb") "breadcrumb" -}}


<EMAIL_ADDRESS>Tufts University, Class of 2017
Bachelor of Science in Computer Science, School of Engineering
Minor in Human Factors
Web Programming, Machine Structure & Assembly-Language Programming, Data Structures, Discrete Mathematics, Inventive Design, Engineering Psychology, Intro to Human Factors And Ergonomics, Electrical Systems, Mechanics: Statics and Dynamics, Differential Equations, Computing in Engineering
AP Scholar with Honor Award, 2012
National Merit Scholar Letter of Commendation, 2012
Dean's List, Fall 2013, Spring 2014, Fall 2014
Pre-Matriculation Credit: Based on nine AP tests, seven 5's, two 4's
ACT: English 35, Math 35, Reading 36, Science 32, Writing 10/10.
Composite = 34/36
09/2014 - Present
Summer 2013
Summer 2012
06/2012 - Present
C, C++, VBA, HTML5, CSS, Java, R
Excel, Adobe Photoshop, PowerPoint, MATLAB
Mock Trial: 2010-2013. Witness, Pre-Trial Attorney, Trial Attorney.
Placed 3rd in California (March 2013).
Tufts Engineering Mentors, 2014-2015.
Chapter Development Committee, Alpha Omicron Pi, 2014-2015.
M-BEST (Menlo’s Bridge to Engineering, Science, Math, and Technology)
Scholar, 2011-2013. This program encourages girls to study STEM topics
through talks and workshops.


Naming Without Context
One of the great things about trees is that they provide natural context for content. And
when people edit content inside a tree, they are aware of that context. That is the reason
why an author will know that in SVG a title
element inside a
circle
element will be the title for that circle, and not for the entire
document.
Failing to use context in naming elements or attributes entails a loss in language fluidity.
Some vocabularies that have strong roots in SGML have an excuse for this approach since DTDs
named elements globally. This is visible in DocBook for instance: it feels daft to call
every item in a list a listitem
since it appears as the child of one of
DocBook’s many list elements, but that was necessary in those days from before broadcast
radio and widespread tap water.
More recent languages have no such excuse. For instance SVG has a long list of elements
beginning with "fe": feDistantLight
, feSpotLight
,
feColorMatrix
, feConvolveMatrix
, feGaussianBlur
,
feTurbulence
... That "fe" is meant to signify "filter effect". But the fact is
that these elements can only appear as children of the filter
element — they
are always going to be in a filter effect defining context. The prefix just makes them more
clumsy, as if all SVG elements began with "svg".


The Auth0 authenticator allows configuration of an auth0 domain. The authenticator will use the web-based (popup) Authorization Code Flow for Single Page Applications. This means there is an additional button to trigger authentication and potentially multi-factor verification.
The domain and client ID can be obtained from the auth0 tenant configuration. The display name is used as label for the additional login button on the login page.


First off I am 19 and well researched and experienced regarding psychoactives of all kinds. My report is concerning my last experience with a gram of 10x extracted leaf I have purchased off the net. I had an exactly .15 gram dose of 10x Salvia extract remaining and planned to undergo one more experience. I had my last experience earlier in the day and due to its intensity I decided to wait awhile before I would smoke my last dose. For these were my initial intentions. What ended up happening was in fact quite different.
I ended up drinking around ten shots of vodka and became quite intoxicated. Around three hours later I was feeling better, but still heavily buzzed. I wandered into my friend Brian’s room and he somehow found out that I had received my Salvia extract and that I had some left. Being a lover of drugs, my friend pressured me into letting him try the extract since I had already experienced it. I felt his desire and soon agreed to his request. I packed the .15 gram into the bowl and told him to rip it as hard as he could. My friend took three huge hits and held all of them in very long.
I saw that he had not even finished off the bowl, so I decided I would take the last hit. Bad idea! I took a big hit to finish off the bowl and within seconds I ceased to perceive reality in any conventional manor. I felt an intense psychedelic rush and reacted in terror rather than euphoria. I felt like I was catapulted into another dimension and proceeded to stumble down the hall towards my room immersed in a completely dissociated state. I recall standing in the entrance to my dorm and feeling totally alien to my surroundings. Familiarity was an extinct player in an extraterrestrial game in which I felt no escape. Everything seemed to be a lose-lose situation. I was lost in the truest sense of the word. I felt misplaced in a dimension where I didn’t belong. I was truly in a state of panic. I had no recollection of basic reality and felt that I didn’t have a proper place anywhere in the universe. I failed to be aware of things as basic as where my room was located.
This entire period of chaos lasted a mere 5 minutes. The terror I felt however, seemed to last far longer. When I made my way back to the room I had so clumsily exited five minutes previous, Brian had returned back to normal and spoke of his experience being the most intense of his life. Mind you, this is an individual whom has consumed a quarter ounce of mushrooms and holds extensive experience with drugs of all kinds. Brian explained that he felt reduced in size as if he was a small player in a video game. He said he felt like he was being watched and that the people around him were condemning him. He was so shocked by the intensity of the experience that he couldn’t stop talking about it and vowed to one day purchase a gram of the extract and have any disbelievers try it for themselves.
My prior alcohol consumption indefinitely played a role in the craziness of my experience. I feel this way because, though I was no longer heavily intoxicated, the alcohol was still very much in my system. I assume that the alcohol altered the course of the experience because I had never achieved such dramatic results from only one inhalation of the extract. Upon taking the one hit I was thrown into a dissociated state that resembled effects from three separate levels of Salvia experience. The effects from the alcohol were most likely felt regarding the trips amnesic effects.
The alcohol consumption also could have aided the ushering in of an immaterial existence aspect of the trip, which would have never been experienced by myself with less than a full smoking dose. Because my one-hit trip was so intense, I clearly realize that alcohol alters the overall intensity of the Salvia experience. My mixture of the two resulted in terror and what anyone would call a “bad trip.” For this reason I would strongly advise against this crazy combination. Salvia alone is far more powerful than most could handle. Overall, my 5th and final major experience once again left me speechless in light of Salvia’s unique power. The experience best resembled a terrible nightmare, yet still proved worthwhile and provided me with a psychedelic ordeal I will not soon forget.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The Running Route
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A Table Showing That dB ain't easy


Roberto Santana and Unai Garciarena
Department of Computer Science and Artificial Intelligence
University of the Basque Country
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
I. Goodfellow and Y. Bengio and A. Courville. Deep Learning.. MIT Press. 2016.
P. Domingos. A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. 2012.
Representation |
Evaluation |
Optimization |
Instances |
Accuracy/Error rate |
Combinatorial optimization |
--K-nearest neighbor |
Precision and recall |
--Greedy search |
-- Support vector machines |
Squared error |
--Beam search |
Hyperplanes |
Likelihood |
--Branch-and-bound |
--Naive Bayes |
Posterior probability |
Continuous optimization |
--Logistic regression |
Information gain |
-Unconstrained |
Decision trees |
KL divergence |
--Gradient descent |
Set of rules |
Cost/Utility |
--Conjugate gradient |
--Propositional rules |
Margin |
--Quasi-Newton methods |
--Logic programs |
-Constrained |
|
Neural networks |
--Linear programming |
|
Graphical models |
--Quadratic programming |
|
--Bayesian networks |
Let \( p({\bf{x}}) \) be a probability distribution defined on a discrete feature \( {\bf{x}} \). \( p({\bf{x}}) \) satisfies the following:
\[ p[{\bf{X}} = {\bf{x}}] = p({\bf{x}}) \]
\[ p({\bf{x}}) \geq 0 \; \; \forall {\bf{x}} \]
\[ \sum_{{\bf{x}}} p({\bf{x}}) = 1 \]
C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press. 2005.
P. Domingos. A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. 2012.
A set of \(N\) tuples \( (x^1,y^1), \dots (x^N,y^N) \) is given, where \(y\) is the target or dependent variable and \(x\) is the covariate, independent variable or predictor. The task is to predict \(y\) given \(x\).
General regression model: \[ y = f(x) + \epsilon \]
where \( \epsilon \) is the irreducible error that does not depend on x.
Linear regression model: \[ f(x) = \beta_1 x + \beta_0 \]
Linear regression estimate: \[ \hat{y} = \hat{\beta_1} x + \hat{\beta_0} \]
The residual error is the difference between the prediction and the true value. \[ e^i = y^i - \hat{y}^i \]
K. P. Murphy. Machine learning. A probabilistic perspective. MIT Press. 2012.
The mean squared error is usually used: \[ MSE = \frac{1}{N} \sum_{i=1}^N (y^i - \hat{y}^i)^2 \]
The parameters of the model that minimize this error are learned: \[ \argmin_{\beta_0,\beta_1} \frac{1}{N} \sum_{i=1}^N (y^i - (\beta_1 x^i + \beta_0 - \hat{y}^i))^2 \]
After differentiating with respect to \( \beta_0,\beta_1 \) and equalling to \(0\), we get: \[ \hat{\beta_0} = \bar{y} - \hat{\beta_1}\bar{x}; \; \; \hat{\beta_1} = \frac{\sum_{i=1}^N x^i y^i - N \bar{x} \bar{y}} {\sum_{i=1}^N (x^i)^2 - N \bar{x}^2} \] where \(\bar{x}\) and \(\bar{y}\) are the mean of \(x\) and \(y\) as computed from the data.
K. P. Murphy. Machine learning. A probabilistic perspective. MIT Press. 2012.
In multiple linear regression we have multiple covariates, represented as a vector \({\bf{x}} \). The model is linear on the covariates.
Multiple linear regression model: \[ f(x) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \beta_n x_n + \epsilon \]
Let \( {\bf{w}} \) be the model weight vector containing \(\beta\) values, , \( {\bf{w}}^T {\bf{x}} \) represents the inner or scalar product between the input vector \( {\bf{x}} \) and the weight vector.
Then, the multiple linear regression model in matrix form is expressed as: \[ y({\bf{x}}) = {\bf{w}}^T {\bf{x}} + \epsilon = \sum_{j=1}^{n} w_jx_j + \epsilon \]
Estimates of \(w\) are found by minimizing the MSE in a way similar to the case of a single covariate. That way the parameters of the model are learned.
K. P. Murphy. Machine learning. A probabilistic perspective. MIT Press. 2012.
Computing the prediction with LDA: \[ p(y=1|x) = sigm \left( {\bf{w}}^T ({\bf{x}} - {\bf{x}}_0) \right) \]
Assuming that the a-priori probability of the two classes is the same: \[ \begin{align} w &= \Sigma^{-1} (\mu_1 - \mu_0) \\ {\bf{x}}_0 &= \frac{1}{2} (\mu_1 + \mu_0) \end{align} \]
Let the Bernoulli distribution be defined as: \[ Ber({\bf{x}},p) = p^{{\bf{x}}} (1-p)^{1-{\bf{x}}} \; \forall {\bf{x}} \in \{0,1\} \]
and the sigmoid function defined as: \[ g(\eta) = \frac{1}{1+e^{-\eta}} = \frac{e^{\eta}}{e^{\eta}+1} \]
Then the logistic regression classifier is defined as: \[ p(y \mid {\bf{x}}, {\bf{w}}) = Ber(y \mid g({\bf{w}}^T {\bf{x}})) \]
K. P. Murphy. Machine learning. A probabilistic perspective. MIT Press. 2012.
S. Russell and P. Norvig. Artificial Intelligence. A Modern Approach. Third Edition. Pearson Press. 2010.
S. Russell and P. Norvig. Artificial Intelligence. A Modern Approach. Third Edition. Pearson Press. 2010.
| Criteria/Flat | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | C1 | C2 | C3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Price | high | low | med. | high | low | med. | med. | high | med. | high | low |
| Distance to University | far | far | close | close | close | close | close | close | far | far | close |
| Parking | no | no | no | no | no | yes | no | no | no | no | yes |
| Cool Roommates? | cool | cool | cool | no | no | cool | cool | cool | cool | cool | no |
| Flat owner | nice | nice | not nice | nice | not nice | not nice | not nice | ? | nice | ? | ? |
| Heating for winter | no | no | no | yes | yes | no | yes | yes | no | no | yes |
| Distance to Bus | close | close | close | far | close | close | far | far | far | close | close |
| Room space | med. | large | small | small | small | med. | small | small | med. | small | small |
| Noisy area | no | yes | yes | no | no | yes | yes | no | no | no | no |
| Mother advice | yes | ? | no | ? | no | yes | yes | no | yes | no | no |
| Cat | no | yes | no | no | yes | yes | no | yes | yes | no | no |
| Kitchen | small | small | large | med. | med. | small | small | med. | large | small | small |
| Distance to beach | far | far | close | close | far | far | far | far | far | far | far |
| Floor | 2 | 7 | 1 | 1 | 0 | 3 | 1 | 2 | 4 | 0 | 3 |
| Elevator | no | yes | no | no | no | no | no | no | no | yes | yes |
| Bars around | yes | yes | yes | yes | no | yes | no | no | no | yes | no |
| Did (Will) I like it? | no | yes | no | no | no | yes | yes | no | ? | ? | ? |
| Criteria/Flat | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | C1 | C2 | C3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Price | high | low | med. | high | low | med. | med. | high | med. | high | low |
| Distance to University | far | far | close | close | close | close | close | close | far | far | close |
| Cool Roommates? | cool | cool | cool | no | no | cool | cool | cool | cool | cool | no |
| Mother advice | yes | ? | no | ? | no | yes | yes | no | yes | no | no |
| Cat | no | yes | no | no | yes | yes | no | yes | yes | no | no |
| Did (Will) I like it? | no | yes | no | no | no | yes | yes | no | ? | ? | ? |
S. Russell and P. Norvig. Artificial Intelligence. A Modern Approach. Third Edition. Pearson Press. 2010.
| Criteria/Flat | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 |
|---|---|---|---|---|---|---|---|---|
| Price | high | low | med. | high | low | med. | med. | high |
| Distance to University | far | far | close | close | close | close | close | close |
| Cool Roommates? | cool | cool | cool | no | no | cool | cool | cool |
| Mother advice | yes | ? | no | ? | no | yes | yes | no |
| Cat | no | yes | no | no | yes | yes | no | yes |
| Did (Will) I like it? | no | yes | no | no | no | yes | yes | no |
| Criteria/Value | p(class=yes) | p(class=no) | Impurity | Entropy | Error | <
|---|---|---|---|---|---|
| Price/high | p(class=yes)=0 | p(class=no)=1 | 0 | 0 | 0 | <
| Price/low | p(class=yes)=0.5 | p(class=no)=0.5 | 0.5 | \(-2*0.5^2*log(0.5) \) | 0.5 | <
| cat/yes | p(class=yes)=0.5 | p(class=no)=0.5 | 0.5 | \(-2*0.5^2*log(0.5) \) | 0.5 | <
| cat/no | p(class=yes)=0.25 | p(class=no)=0.75 | ? | ? | ? | <
S. Russell and P. Norvig. Artificial Intelligence. A Modern Approach. Third Edition. Pearson Press. 2010.


OpenXC Dashboard
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Settings
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Just changed:
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In this lab, we will use Cloud9, a Cloud-based IDE, to easily build a remote development environment and create a client for the FleetIQ example.
With Cloud9, you can build a consistent online integrated development environment without any changes to the local environment or difficulty in configuring the development environment.
Access to Cloud9 console. (https://ap-northeast-2.console.aws.amazon.com/cloud9/home/product)
Set name as FleetIQClient
Wait for creation of Cloud9. It will take few minutes.
When Cloud9 IDE is executed, create file client.py and paste following code.
import socket
import boto3
client = boto3.client('gamelift')
# Claim Game Server
def claim_game_server():
pass
#
response = claim_game_server()
#endpoint = response['GameServer']['ConnectionInfo']
endpoint = ''
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect((endpoint, 5000))
while True:
data = input("SEND(TYPE q or Q to Quit):")
if(data == 'q' or data == 'Q'):
client_socket.send(data.encode())
client_socket.close()
break
else:
client_socket.send(data.encode())
data = client_socket.recv(512).decode()
print("RECEIVED:", data)
print("Socket Closed... END")
This code is a simple TCP echo client implementation. However, if you look closely, the server’s endpoint is not specified. Now let’s implement this part.
def claim_game_server():
response = client.claim_game_server(
GameServerGroupName='GameServerGroups'
)
print(response)
return response
Function’s return value contains Game Server endpoint. Remove comment and set endpoint like below.
endpoint = response['GameServer']['ConnectionInfo']
import socket
import boto3
client = boto3.client('gamelift', 'ap-northeast-2')
# Claim Game Server
def claim_game_server():
response = client.claim_game_server(
GameServerGroupName='GameServerGroups'
)
print(response)
return response
#
response = claim_game_server()
endpoint = response['GameServer']['ConnectionInfo']
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect((endpoint, 5000))
while True:
data = input("SEND(TYPE q or Q to Quit):")
if(data == 'q' or data == 'Q'):
client_socket.send(data.encode())
client_socket.close()
break
else:
client_socket.send(data.encode())
data = client_socket.recv(512).decode()
print("RECEIVED:", data)
print("Socket Closed... END")
In the lab, only a simple TCP connection is configured for the purpose of learning to run the FleetIQ API, but the code is simple, but you can practice with more complex game logic.
Now let’s see how the server and client interact with the one we created earlier.


Speedrunner API Documentation
Version 0.2 - now with Sqlite3 database.
Supported GET URIs:
return format:
[
"The Legend of Zelda: Ocarina of Time",
...
"Hollow Knight"
]
- /games/categories/all - returns list of all game categories for all titles
return format:
[
"Inbounds",
...
"All Bosses"
]
- /games/catgories/<game_title> - returns list of all categories for <game_title>
return format:
[
"Inbounds",
...
"Out of Bounds"
]
return format:
{
"Any%": [
{
"duration": "0:00:42",
"player": "S. P. Lunker"
},
...
{
"duration": "0:13:37",
"player": "ToadKing"
}
],
"Hell%": [
{
"duration": "0:02:54",
"player": "S. P. Lunker"
},
...
{
"duration": "0:13:37",
"player": "ToadKing"
}
]
}
- /speedruns/games/<game_title>/<category> - returns list of speedruns (player names and durations) for game_title and category sorted by fastest duration
- Supports optional "/<count>" parameter to limit speedrun results
return format:
[
{
"duration": "0:02:54",
"player": "S. P. Lunker"
},
...
{
"duration": "0:13:37",
"player": "ToadKing"
}
]
- /speedruns/players/<player_name>/<category> - returns list of speedruns (game title, category, and durations) for <player_name> sorted by fastest duration
- Supports optional "/<count>" parameter to limit speedrun results
[
{
"category": "Any%",
"duration": "0:13:37",
"game_title": "Spelunky"
},
...
{
"category": "Any%",
"duration": "0:19:42",
"game_title": "The Legend of Zelda: Ocarina of Time"
}
]
Supported POST URIs:
All POST requests require header of "Content-Type: application/json"
- /games/add/ - adds game title and optional categories OR updates an existing game title with additional categories in the format(s)
- {"game_title":<title>, "categories":[<categories>]}
- {"game_title":<title>}
- /games/categories/add/ - adds a list of categories to a game title in the format
- {"game_title":<title>, "categories":[<categories>]}
- /speedruns/add/ - adds a speedrun in the format
- {"game_title":<title>, "category":<category>, "player_name":<player_name>, "duration":<duration>}
Notes:


Please enter new location name, e.g. Gresham
Please enter new location ID, e.g. GreshamId
Please enter new location Anchor, e.g. GreshamAnchor
Please enter new location Time Slots, e.g. 8am,9am,10am,11am...etc.
Please enter new minimum amount of Pizzas sold, e.g. 0,0,3,4,4,...etc.
Please enter new maximum amount of Pizzas sold, e.g. 8,14,35,35,22,...etc.
Please enter new minimum number of Deliveries, e.g. 1,1,4,4,6,...etc.
Please enter new maximum number of Deliveries, e.g. 1,4,6,6,8,...etc.


{% extends 'layout.html' %} {% set version = "v5" %} {% set heading = "We're sorry, the email address has already been used" %} {% set mainClasses = "nhsuk-main-wrapper--s" %} {% block beforeMain %} {% endblock %} {% block content %}
We've checked, and the email address<EMAIL_ADDRESS>has already been used to sign up.
If this email address is wrong, you can tell us the right email address to use.
If you've already signed up for this service, you can withdraw your permission to be contacted about coronavirus vaccine studies at any time.


{% extends 'administration/base_admin.html' %} {% block content %}
Group with this name already exists!
Group with this Name already exists and User with this E-Mail doesn't exist!
User with this E-Mail doesn't exist
You need to fill in all Fields!
This group doesn't exist!
Another admin has already deleted this group!
{{ gro.name }}
{{ group.name }}
If you delete This Category, the data will be permanently lost.
Would you like to rethink your decision?


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Welcome, Java Hipster!
This application is a microservice, which has been generated using
JHipster.
- It does not have a front-end. The front-end should be generated on a JHipster gateway.
- It is serving REST APIs, under the '/api' URLs.
-
To manage this microservice, you will probably want to use the
JHipster Registry:
-
To run the JHipster Registry locally, you can use Docker:
docker compose -f src/main/docker/jhipster-registry.yml up --wait
-
Its default URL is http://localhost:8761/ and its default login/password is
admin/admin
-
OpenAPI documentation endpoint for those APIs is at /v3/api-docs, but if you want access to the full
Swagger UI, you should use a JHipster gateway or a JHipster Registry, which will serve as API developer portals.
If you have any question on JHipster:
If you like JHipster, don't forget to give us a star on
GitHub!


Inside the file cicada.c lives an array called cicadaLanguage[] which defines basically every symbol we find inside of a script. Each array element is a commandTokenType structure variable that defines one operator in the Cicada language:
typedef struct {
const char *cmdString;
ccInt precedence;
const char *rtrnTypeString;
const char *translation;
} commandTokenType;
The first string, cmdString, is the operator symbol or name as written in Cicada. Then we give the precedence level of the operator (see Table 2). Next comes a string that explains to the Cicada compiler what type(s) of object this operator ‘returns’ to the surrounding expression. The final string, translation, either encodes the operator directly as bytecode (Cicada’s native language), or else ‘expands’ the operator in terms of other Cicada commands. The first batch of operators have direct bytecode translation, indicated by the fact that their translation begins with a inbytecode marker (which concatenates to the following string as an unprintable character). But for the second group of operators, towards the end and lacking a inbytecode marker, the translation is just a fragment of a script built from the previous operators.
To add a new operator into the language, simply add a new entry to the end of the cicadaLanguage[] array, in the indicated space, and fill in the four fields.
cmdString:
The command string defines what sort of object in the script will be recognized as the operator. For most operators, operators are recognized by some string of letters, symbols, etc. that marks the operator in the script. (There is really no restriction on this string other than it consist of printable characters.) For example, looking through the array we find simple operators like \ (written with two backslashes in the C string) and exit.
Many of the operators take left-hand and/or right-hand arguments, as indicated by keywords like type3arg to the left and/or right of the operator string. These keywords are part of the command string -- they are just unprintable characters that get concatenated to the operator strings (because they are only separated by a space). For example, the full command string of the define operator is
type2arg "::" type6arg
The define operator requires both a left-hand expression or argument (the member to define) and a right-hand argument, which can be a member name but also a type like bool. The two arguments therefore have a different ‘type’. Looking at the comment before the cicadaLanguage[] array, we see that a type 2 argument represents a variable or function, and a type 6 argument represents a code-containing expression, as expected. Having different types allows the compiler to throw type-mismatch errors when expressions don’t make sense.
In some cases two different operators will have the same operator string. For example, compare ‘*’ as a multiplication operator versus the void, or ‘-’ as either subtraction or negation. This is only allowed if one of the operators expects a left-hand argument and the other doesn’t, so that the compiler will immediately know which of the two operators it is looking at when it sees the operator string in a script. For example, when it stumbles upon a ‘-’, that symbol will be interpreted as a subtraction if there is a dangling expression just to the left, and a negation otherwise.
More complex definitions like while ... do can involve several operator strings.
"while" type5arg "do" type1arg
The pattern is always: operator strings like ‘do’ alternating with arguments. Sometimes, these complex definitions involve a optionalargs keyword: everything before the keyword is required, but everything afterwards is optional. For example, the if command
"if" type5arg "then" type1arg optionalargs "else" type1arg
requires an if and a then, but the else is optional.
There are 10 allowed argument types: type0arg through type9arg. (Cicada only uses 9 of these types). There are also a few special types. A typeXarg accepts any type of argument, and is used by the (...) operator with no left-hand argument (i.e. the grouping operator, not a function call) to allow the user to group any sort of expression, even entire commands. The commentarg keyword denotes an block of text to be entirely ignored until the next operator string is encountered (i.e. everything from a comment bar ‘|’ to an end-of-line is skipped). chararg and stringarg treat the argument as text containing one or several characters respectively.
Finally, there are several special operators that don’t have any operator string at all. If the operator string is simply int_constant, then the operator is read when the compiler encounters a number that it deems to be an integer; and the operator whose operator string is double_constant corresponds to a floating-point number. The variable_name operator is assumed to apply whenever the compiler encounters a novel word beginning with a letter (which may be followed by underscores and numbers). In these three cases the number or word should be thought of as an argument, insofar as the bytecode is concerned.
The final class of special operator strings is the adapters, an important element of Cicada scripting that is explained in the next section. Suffice to say that there is an adapter for each of the 10 argument types, type0arg_adapter through type9arg_adapter, along with a noarg_adapter.
precedence:
The precedence level determines how operators are bound into expressions. The high-precedence operators are grouped most tightly to their neighbors, and evaluated before the low-precedence operators. Thus A = 2 * B - 2 is grouped: A = ( (2*B) - 2) because of the three gluing operators ‘= * -’, multiplication ‘*’ has the highest precedence and assignment ‘=’ has the lowest precedence. The precedence level is just an integer, although notice that cicada.c predefines a keyword for each precedence level and uses that names instead in the operator definitions.
The cicadaLanguageAssociativity[] array in cicada.c explains how to group operators of the same precedence level, when there are no parentheses to break the tie. This can be important. For example, multiplication and division operators have precedence level 11, and the eleventh entry of the associativity array (i.e. cicadaLanguageAssociativity[10]) is l_to_r signifying left-to-right grouping. Therefore the expression 8/2/4 groups as (8/2)/4 which equals 1, as opposed to 8/(2/4) which equals 16. On the other hand, assignment works at precedence level 5, which has r_to_l or right-to-left grouping. Therefore A = B = C = 2 groups as A = (B = (C = 2)), so that 2 copies to C, then to B, then to A. If the grouping were the other way, then each assignment would rewrite A.
The size of the cicadaLanguageAssociativity[] array determines the allowed precedence levels. So by adding an entry the maximum allowable operator precedence level will be 16. Anything outside the interval [1, max_precedence] will cause an out-of-range compiler error.
rtrnTypeString:
Many operators ‘return’ a value to the enclosing expression, and which type(s) of value they are allowed to return is encoded in the return-types string. For example, the addition operator has the return-types string "456", so its return value can be construed as being of type 4, 5 or 6. The return types correspond to the argument types from the cmdString field, so the expression A = 2+5 is legal (because the assignment operator expects a type-5 right-hand argument) but 2 + 5 = A is illegal (because the left-hand argument of the assignment operator should be type 2). Notice how each ‘type’ is really an operator argument type: arguments have one type and entire operators have many, which is maybe backwards to the way we usually think.
There is a special argXtype return type which is paired with a typeXarg argument type. This is used by the grouping operator (...), causing the type inside the parentheses (its ‘argument’) to be the type returned back to the enclosing expression. The parentheses only force a grouping, without affecting the type of the enclosed expression.
An entire script must be of type 0 -- Cicada enforces this using adapters (see below).
translation:
The last field of an operator definition explains how it will be translated into bytecode. If the bytecode string begins with a inbytecode keyword, then the string contains a list of integers which are the bytecode representation of the operator. For ease of reading, the bytecode translations in cicada.c are built from string macros defined in cicada.h. If there is no inbytecode keyword, then the string is interpreted as a fragment of Cicada code that will be translated into bytecode using previously-defined operators -- so it is best to define these operators last.
The translation strings of bytecode-coded operators have strings of numbers separated by spaces in their translation strings, but also some funny letters: ‘a’, ‘j’ and ‘p’. The ‘a’ letter stands for an argument that is to be substituted into the bytecode at the given location, and is followed immediately (no space) by a number from 1 to 9 indicating which argument. (Cicada only supports up to 9 arguments in an operator.) For example, the assignment operator has a bytecode string
inbytecode "8 1 a1 a2"
meaning that the operation consists of two integers (8 followed by 1), then the first (left-hand) argument, and last the second (right-hand) argument. Each of these arguments can themselves be expressions (think f().a = 5+cos(b)), in which case the entire expression translated into bytecode is substituted for the argument. In cicada.c the macro bcArg(x) produces the “ax” string, so the assignment operator reads
inbytecode bc_define(equFlags) bcArg(1) bcArg(2)
where bc_define(equFlags) produces a define operator with equate flags: "8 1".
The ‘j’ and ‘p’ bytecode symbols are used to specify jump offsets (‘j’ -- effectively gotos) and jump positions (‘p’) in the bytecode. Offsets are the number of code words to jump ahead from the offset word (negative offsets jump backwards), and the cicada compiler calculates these as the difference between a jump (j) marker and a target position (p) marker. Each of position/jump marker is followed immediately by a number 1-9 indicating which position to define/jump to. For example, the bytecode string of the if-then-else command which potentially takes 3 arguments is:
inbytecode "3 j1 a1 a2 1 j2 p1 a3 p2"
In cicada.c the position markers are produced using bcPosition() macros, and the jump operators have dedicated macros taking the jump offsets as arguments, so this same operator definition reads
inbytecode bc_jump_if_false(1) bcArg(1) bcArg(2)
bc_jump_always(2) bcPosition(1) bcArg(3) bcPosition(2)
In this case the first bytecode command -- bytecode operator 3 which is the jump-if-false command -- jumps to the position of the first position marker, so the compiler calculates this offset by taking the difference in the code position between the j1 command and p1 (which is effectively the beginning of argument 3 because p1 is not a code word) and puts that value in place of the j1 word. Likewise, there is an unconditional jump later on to the end (j2). The third argument a3 may or may not exist because the third argument is in the optional else block: if there is no else then a3 is basically ignored, but the second position marker is still defined.
Many of the adapter operators (explained in the next section) have anonymousmember keywords in their bytecode. These are replaced by unique (and negative) member IDs that are found nowhere else in the script: the first use of a anonymousmember in the bytecode becomes the number -1, the second use represents a -2 in the bytecode, etc. These are used to construct hidden members that won’t bother anyone by conflicting with user-defined members.
Scripted operators -- those without inbytecode keywords -- work basically the same as bytecoded operators except that unfortunately the arguments have to be encoded with special keywords (arg1 through arg9) rather than directly in the string. For example, a cicadaLibraryFunction() function is defined at the beginning of cicada.c in bytecode, and the call() function uses its definition in its script translation:
"cicadaLibraryFunction#0(" arg1 ")"
So the command call("myF", 12) is first translated into cicadaLibraryFunction#0("myF", 12) before being converted into bytecode.
Some operators (usually comments) have no effect on the bytecode whatsoever, and for those we give neither bytecode nor a script translation but instead write removedexpression for their translation string. The |* ... *| comment block uses this keyword, as does the line-continuation operator & which ignores everything to the end of the line. Oddly enough the single-line comment | ... doesn’t use this keyword, and the reason is that it always breaks two separate commands -- so in terms of bytecode it works a lot like a comma or end-of-line.
Last update: July 25, 2021


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Animations in SwiftUI
SwiftUI brings declarative and straightforward approach in building User Interfaces. We have List and Form components and Bindings. All of these things make SwiftUI so easy to use and very powerful. But today we are going to talk about another feature of SwiftUI, and it is Animations.
Animation
You can smoothly animate any change in SwiftUI by wrapping it into withAnimation block. By default, SwiftUI uses fade in and fade out for animating changes. Let’s take a look at a small example.
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struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Button(action: {
withAnimation {
self.isButtonVisible.toggle()
}
}) {
Text("Press me")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}
}
}
}
}
In the current example, we wrap the State change with withAnimation block, and it produces nice fade in animation. You can modify animation by passing timing and spring values. Another option can be attaching animation modifier to the animating view.
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struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Button(action: {
self.isButtonVisible.toggle()
}) {
Text("Press me")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}.animation(.easeInOut)
}
}
}
}
In the code sample above, we achieve the same animation by simply adding animation modifier. We use easeInOut animation, but you can pass custom animation properties.
Sometimes we have a situation where multiple views depend on some state, and we want to animate all depending view changes together. For this case, we have animatable bindings.
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struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Toggle(isOn: $isButtonVisible.animation()) {
Text("Show/Hide button")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}
}
}
}
}
As you can see, we can easily convert our binding into animatable binding by calling animation method on it. This method wraps every change of binding value into an animation block. You can pass animation settings as parameters to this method. More about bindings you can read in my previous post.
Transitions
As I said before, SwiftUI uses fade in and fade out transition by default, but we can apply any other transition we want. Let’s replace fading with moving.
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struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Toggle(isOn: $isButtonVisible.animation()) {
Text("Show/Hide button")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}.transition(.move(edge: .trailing))
}
}
}
}
In the example above, we attach transition modifier to the view.  We can combine them into a single transition. Let’s take a look at the example.
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extension AnyTransition {
static func moveAndScale(edge: Edge) -> AnyTransition {
AnyTransition.move(edge: edge).combined(with: .scale())
}
}
struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Toggle(isOn: $isButtonVisible.animation()) {
Text("Show/Hide button")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}.transition(.moveAndScale(edge: .trailing))
}
}
}
}
We created a moveAndScale transition, which is basically a combination of move and scale transitions. SwiftUI applies the current transition symmetrically according to timing or spring values which you pass into the animation method.
SwiftUI provides a way of building asymmetric transitions also. Assume that you need a move transition on insertion and a fade transition on removal. For those cases, we have an asymmetric method on AnyTransition struct, which we can use to build asymmetric transitions.
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extension AnyTransition {
static func moveOrFade(edge: Edge) -> AnyTransition {
AnyTransition.asymmetric(
insertion: .move(edge: edge),
removal: .opacity
)
}
}
struct ContentView : View {
@State private var isButtonVisible = true
var body: some View {
VStack {
Toggle(isOn: $isButtonVisible.animation()) {
Text("Show/Hide button")
}
if isButtonVisible {
Button(action: {}) {
Text("Hidden Button")
}.transition(.moveOrFade(edge: .trailing))
}
}
}
}
As you can see, we use asymmetric method to pass two transitions, the first one for insertion and another one for removal.
Bonus tip: We can pass here combined transition which we created before.
Conclusion
Today we discussed multiple ways of animating changes in SwiftUI. It totally depends on you and on use-case which way you have to choose. By spending more and more time with SwiftUI, I understand that it is already a compelling framework.


"I have loved you with a Florentine fourteenth-century frenzy for full fifteen years!". |
| Colonel Calverley, Officer of the Dragoon Guards | Mr. Richard Temple |
| Major Murgatroyd, Officer of the Dragoon Guards | Mr. Frank Thornton |
| Lieut. The Duke of Dunstable, Officer of the Dragoon Guards |
Mr. Durward Lely |
| Reginald Bunthorne, a Fleshly Poet | Mr. George Grossmith |
| Archibald Grosvenor, an Idyllic Poet | Mr. Rutland Barrington |
| Mr Bunthorne's Solicitor | Mr. G. Bowley |
| The Lady Angela, a rapturous maiden | Miss Jessie Bond |
| The Lady Saphir, a rapturous maiden | Miss Julia Gwynne |
| The Lady Ella, a rapturous maiden | Miss M. Fortescue |
| The Lady Jane, a rapturous maiden | Miss Alice Barnett |
| Patience, a dairy maid | Miss Leonora Braham |
Chorus of rapturous maiden and Officers of the Dragoon Guards. |
| Original Costumes |
Leonara Braham as Patience |
Alice Barnett as The Lady Jane |
Frank Thornton as Major Murgatroyd |
George Grossmith as Reginald Bunthorne |
| Return to the Patience main page |
| Check out the history of Patience in Australia. |
| Check out these other historic photos: |


31
1 Yahweh spoke to Moses,
2 "See, I have called by name Bezalel son of Uri son of Hur, from the tribe of Judah.
3 I have filled Bezalel with my spirit, to give him wisdom, understanding, and knowledge, for all kinds of craftsmanship,
4 to make artistic designs and to work in gold, silver, and bronze;
5 also to cut and set stones and to carve wood—to do all kinds of craftsmanship.
6 In addition to him, I have appointed Oholiab son of Ahisamach, from the tribe of Dan. I have put skill into the hearts of all who are wise so that they may make all that I have commanded you. This includes
7 the tent of meeting, the ark of the covenant decrees, the atonement lid on the ark, and all the furniture of the tent—
8 the table and its utensils, the pure lampstand with all its equipment, the incense altar,
9 the altar for burnt offerings with all its equipment, and the large basin with its base.
10 This also includes the finely woven garments—the holy garments for Aaron the priest and those of his sons, reserved for me so that they may serve as priests.
11 This also includes the anointing oil and the sweet incense for the holy place. These craftsmen must make all these things just as I have commanded you."
12 Yahweh spoke to Moses:
13 "Tell the Israelites: 'You must certainly keep Yahweh's Sabbath days, for these will be a sign between him and you throughout your people's generations so that you may know that he is Yahweh, who sets you apart for himself.
14 So you must keep the Sabbath, for it must be treated by you as holy, reserved for him. Everyone who defiles it must surely be put to death. Whoever works on the Sabbath, that person must surely be cut off from his people.
15 Work will be done for six days, but the seventh day is to be a Sabbath of complete rest, holy, reserved for Yahweh's honor. Whoever does any work on the Sabbath day must surely be put to death.
16 Therefore the Israelites must keep the Sabbath. They must observe it throughout their people's generations as a permanent law.
17 The Sabbath will always be a sign between Yahweh and the Israelites, for in six days Yahweh made heaven and earth, and on the seventh day he rested and was refreshed.'"
18 When God had finished talking with Moses on Mount Sinai, he gave him two tablets of covenant decrees, made of stone, written on by his own hand.


Basic example
Custom sequence and features example
Category order, category exsclusions and pdb id inclusions sequence example


I am experienced in leveraging agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
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Trinity
Morpheus
Neo
What role did Trinity play in The Matrix
Barista
Corporate Lawyer
Real World Revolutionary
Who relied on Trinity the most?
The Oracle
Smith
Neo
How did Trinity die in the 2nd movie?
Smith punched through her chest
An agent pushed her into oncoming traffic
An agent shot her while they were falling from a multiple story building
Submit
What role did Morpheus play in The Matrix
Cabby
Airline Pilot
Prophet
Who relied on Morpheus the most?

Smith punched through his helmet
An agent pushed him into a tanker truck
Unless you saw a different movie than I did, he didn't die
Submit
What role did Neo play in The Matrix
The Junkie
The Waiter
The one
Who got captured after brining Neo to see the Oracle?
Trinity
Switch
Morpheus
How did Neo die in the 3rd movie?


| DOSE: |
|
smoked |
Crack |
(powder / crystals) |
I've been trying to stay away from crack since February of this year but it still seems so hard. Yesterday my younger sister invited a couple of guys she met at the club over to our town so we could drink and talk and shit. I was buzzing on beer and weed so we started talking about drugs and they asked if we had ever tried speed.  They took out a foil paper and a hollow pen. I was thinking this doesn't look like speed so they took out a small rock and immediately I started inhailing that rock as it burned on the foil paper. It din't bring back that head rush that I used to feel when I was a crackhead and i just stayed with the cravings of hitting up some more rock. But there wasn't no more.
I don't recall much about last night and my sister told me that I was tripping hardcore. She said I was cussing at the guys and driving very fast on the way home and I even forgot where I lived. I think my body is just trying to recover from those crack days but I don't seem to be helping much. I'm not even hungry and my head feels so light today. I wish I had more crack but I don't want to fall again. I'm never the same. I always think of crack and remember those 'good old days'. Well to me that's what they seemed, my younger sister doesn't like for me to do that she says i'm scary and violent. I think I reacted that way because there was no more crack last night and I wanted some more.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Q: Why do I need MetaMask to send a donation or create a contract? A: MetaMask is a browser plugin that allows users to make Ethereum transactions through regular websites. It facilitates the adoption of Ethereum because it bridges the gap between the user interfaces for Ethereum (eg. Mist browsers, Dapps) and the regular web (eg. Chrome, Firefox, websites).


Eveleyn, four, fell into manhole on school run
"It was horrible how she was looking at me, clinging on for dear life.”
by Neil ShawA four-year-old girl 'disappeared' as she was walking to school with her mum when she fell down an open MANHOLE.
Evelyn Walker was lucky to escape serious injury when she plunged into the deep hole - because she managed to cling on to the lid.
Mum Saffron Walker was walking little Evelyn to school in Burghfield, Berks., when the mishap occurred.
She was trying to sort out her daughter’s coat and schoolbag when Evelyn just ‘disappeared’.
The beautician, from Burghfield, had parked by her sister’s home so she could walk around the corner to Mrs Bland’s Infant School.
She said: “My daughter went to knock on my sister’s door, and I was like, ‘no, come back to the car, I need to get your coat and your schoolbag ready.’
“She came back to the car, stood on the corner of the drain, and she’s just gone.
“I looked up, and I couldn’t see her.”
Evelyn had tumbled down a six-foot manhole but was clinging on to the lid.
Her foot had become jammed in the drain but the shaken and terrified mum was able to retrieve her.
She said: “She’s cut her knee open.
“She must have bruises coming out on her back, her arms, her hands.

Saffron has demanded answers from the council, because of she thinks her daughter was lucky to escape serious injury.
She said: “I am fuming.

"It was horrible how she was looking at me, clinging on for dear life.”
Saffron’s mother Debbie said the manhole had been left exposed during recent works.
She said: “Somebody’s got to take responsibility for this, it’s disgusting.
“A job like this, you’d have to go health and safety to the extreme.
“We could’ve lost a life.”
A West Berkshire council spokesperson said: "The council made the area around the manhole safe as soon as they were made aware of the incident.
"It is currently unclear how the damage to the manhole cover occurred, however, we have instructed our contractor to barrier the area off until we are able to make a full repair."


Write a program to solve the rat in a maze puzzle utilizing backtracking algorithm. Test your program with the 2D array shown above (Initialize the 2D array in your program directly). Find the shortest path and determine the number of steps in finding the shortest path. Display the maze and the shortest path on the screen.
result:


{% extends "layout.html" %} {% block page_title %} Blank page {% endblock %} {% block content %} {% block phase_banner %} {% include "includes/phase_banner_alpha.html" %} {% endblock %} Home Companies House Forms Strike off a company from the register (DS01) Approving on behalf of another director Some directors may need a Power of Attorney or someone else to sign on their behalf if their deceased. Do any of your company directors need someone to sign on their behalf? Do any of the directors need someone to approve this application on their behalf? Yes No {% endblock %}
Some directors may need a Power of Attorney or someone else to sign on their behalf if their deceased.


This is the partial for view 1.
Use a capture variable for this
when using the controllerAs
syntax.
Choose a consistent variable name such as vm
, which stands for ViewModel.
Read more from Angular Style Guide by John Papa
https://github.com/johnpapa/angular-styleguide
| {{ person.firstname }} | {{ person.lastname }} |
Active: {{ vm.activePerson.firstname + ' ' + vm.activePerson.lastname }}


Good afternoon,Jason!
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Capcom Has Announced Street Fighter V: Champion Edition, A Bundle That Includes Almost Everything Re
by Luke PlunkettCapcom has announced Street Fighter V: Champion Edition, a bundle that includes almost everything released as part of both Street Fighter V and its Arcade Edition. That’s “40 characters, 34 stages and over 200 costumes”. It’s out in February 2020 for PS4 and Steam and will cost $US30 ($44).


{% extends '../tutorials/tutbaselayout.html' %} {% load static %} {% block content %}
A function is a group of statements that together perform a task. Every C program has at least one function, which is main(), and all the most trivial programs can define additional functions.
You can divide up your code into separate functions. How you divide up your code among different functions is up to you, but logically the division is such that each function performs a specific task.
A function declaration tells the compiler about a function's name, return type, and parameters. A function definition provides the actual body of the function.
The C standard library provides numerous built-in functions that your program can call. For example, strcat() to concatenate two strings, memcpy() to copy one memory location to another location, and many more functions.
A function can also be referred as a method or a sub-routine or a procedure, etc.
The general form of a function definition in C programming language is as follows −
return_type function_name( parameter list ) { body of the function }
A function definition in C programming consists of a function header and a function body. Here are all the parts of a function −
Given below is the source code for a function called max(). This function takes two parameters num1 and num2 and returns the maximum value between the two −
/* function returning the max between two numbers */
int max(int num1, int num2) {
/* local variable declaration */
int result;
if (num1 > num2)
result = num1;
else
result = num2;
return result;
}
A function declaration tells the compiler about a function name and how to call the function. The actual body of the function can be defined separately. A function declaration has the following parts −
return_type function_name( parameter list );
For the above defined function max(), the function declaration is as follows −
int max(int num1, int num2);
Parameter names are not important in function declaration only their type is required, so the following is also a valid declaration
int max(int, int);
Function declaration is required when you define a function in one source file and you call that function in another file. In such case, you should declare the function at the top of the file calling the function.
While creating a C function, you give a definition of what the function has to do. To use a function, you will have to call that function to perform the defined task.
When a program calls a function, the program control is transferred to the called function. A called function performs a defined task and when its return statement is executed or when its function-ending closing brace is reached, it returns the program control back to the main program.
To call a function, you simply need to pass the required parameters along with the function name, and if the function returns a value, then you can store the returned value. For example −
#include <stdio.h>
/* function declaration */
int max(int num1, int num2);
int main() {
/* local variable definition */
int a = 100;
int b = 200;
int ret ;
/* calling a function to get max value */
ret = max(a, b);
printf( "Max value is : %d\n", ret );
return 0;
}
/* function returning the max between two numbers */
int max(int num1, int num2) {
/* local variable declaration */
int result;
if (num1 > num2)
result = num1;
else
result = num2;
return result;
}
We have kept max() along with main() and compiled the source code. While running the final executable, it would produce the following result −
Max value is : 200
If a function is to use arguments, it must declare variables that accept the values of the arguments. These variables are called the formal parameters of the function.
Formal parameters behave like other local variables inside the function and are created upon entry into the function and destroyed upon exit.
While calling a function, there are two ways in which arguments can be passed to a function −
| Sr. No. | Call Type & Description |
|---|---|
| 1 | This method copies the actual value of an argument into the formal parameter of the function. In this case, changes made to the parameter inside the function have no effect on the argument. |
| 2 | This method copies the address of an argument into the formal parameter. Inside the function, the address is used to access the actual argument used in the call. This means that changes made to the parameter affect the argument. |
By default, C uses call by value to pass arguments. In general, it means the code within a function cannot alter the arguments used to call the function.
{% endblock %}


{% extends "header.html" %} {% block body %}
Besides 3D scatter plots, we can also do 3D bar charts. This again allows us to compare the relationship of three variables rather than just two. 3D bar charts with matplotlib are slightly more complex than your scatter plots, because the bars have 1 more characteristic: depth. Basically, the "thickness" of the bars is also define-able. For most, this will be simply a pre-determined set of data, but you can actually use this for one more "dimension" to your plot.
Here is the sample code for the 3D bar charts:
from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax1 = fig.add_subplot(111, projection='3d') xpos = [1,2,3,4,5,6,7,8,9,10] ypos = [2,3,4,5,1,6,2,1,7,2] num_elements = len(xpos) zpos = [0,0,0,0,0,0,0,0,0,0] dx = np.ones(10) dy = np.ones(10) dz = [1,2,3,4,5,6,7,8,9,10] ax1.bar3d(xpos, ypos, zpos, dx, dy, dz, color='#00ceaa') plt.show()


--- title: Benjamin Bichsel position: PhD Student image: beni.jpg email<EMAIL_ADDRESS>office: CNB H100.5 website: phone: +41 44 632 53 97 cv: ---
I am Benjamin Bichsel, a PhD student at the Department of Computer Science, ETH Zürich. I am part of the Secure, Reliable, and Intelligent Systems Lab, supervised by Martin Vechev, since September 2017.
ETH Zurich, September 2012 – September 2017: B.Sc. and M.Sc. in Computer Science


1 write to _lastTimeProgressWasWritten
Microsoft.PowerShell.Commands.Utility (1)
commands\utility\ImplicitRemotingCommands.cs (1)
1819
_lastTimeProgressWasWritten
= DateTime.UtcNow;
1 reference to _lastTimeProgressWasWritten
Microsoft.PowerShell.Commands.Utility (1)
commands\utility\ImplicitRemotingCommands.cs (1)
1812
TimeSpan timeSinceProgressWasWrittenLast = DateTime.UtcNow -
_lastTimeProgressWasWritten
;


Q: This won't scale. Won't it be way too slow as the number of users (and thus directory entries) increase?
A: I realize a lot of filesystems are deficient when it comes to directories with a large number of entries. Fortunately, FreeBSD has UFS with DIRHASH and Linux has Reiserfs which don't suffer from these shortcomings. As for performance, this scheme still probably isn't going to beat a Berkeley DB file (or CDB file) or possibly even reading sequentially from a flat /etc/passwd file, but the way I see it is, the increase in flexibility more than makes up for the (possibly negligible) performance loss.
Q: How about a directory hashing scheme so it's not all under one big directory?
A: A hashing scheme (i.e. /etc/users/d/r/drench/) would break the very clean and simple API of disk I/O on files under /etc/users/USERNAME/, unless we can all agree on a consistent "depth" (highly unlikely). As I said above, if performance on large directories is an issue, that's a filesystem problem, and it's one that's been largely solved.
Q: Why not make SHELL a symlink to the shell itself or DIR a symlink to the user's home directory?
A: While it would be nice to be able to cd /etc/users/drench/DIR to get to my home directory or execute /etc/users/drench/SHELL directly, I think it's even more important that shell scripts be able to do things like HOME=`cat /etc/users/drench/DIR`. It also then ceases to be an envdir (i.e. I don't want the contents of the variable SHELL to be the binary contents of the executable). I also like consistency: you can change any field with echo 'value' > FIELDNAME in the current scheme. I don't want to go down the path where some need to be symlinks, and some need to be plain files. Plus, this is Unix. If you want to execute your shell, type `cat /etc/users/mylogin/SHELL`.
Q: Wouldn't it be more appropriate to use "HOME" for the user's home directory instead of "DIR"?
A: I considered using different names for the files, but I decided that being a drop-in replacement, it would be appropriate to not deviate from the names as they are defined in struct passwd. So the filenames are identical to the structure, except that I uppercased them. You're always free to symlink HOME to DIR if you like.
Q: Why /etc/users? Why not register a name under slashpackage?
A: At first I thought about putting it under /config/users or /users, but I settled on /etc/users because the fact is there are many reactionaries in the Unix community who would dismiss the concept out of hand if I had done that. Witness the hostility toward the work of D.J. Bernstein. Still, I'm sure a lot of people are going to dismiss userdir out of hand just because it's still too radically different from /etc/passwd. As for slashpackage, I'll consider it, but ultimately I want to see userdir as an integral part of every Unix system, not an optional package. If I'm misunderstanding the concept of slashpackage, please correct me.
Q: Why not just use /home? It's already there and already includes user metadata in the form of dotfiles and the like.
A: That's an interesting idea. I need to ponder that, since I do have a few concerns about it. For one, most system administrators let users own their own home directories, and there are certain fields (UID in particular) that only the system administrator should be able to modify (simple root exploit that would work under the current implementation under /home: rm ~/UID && echo 0 > ~/UID). There are ways to lock this down, certainly, but I'd like to make the transition as painless as possible. This would also break envdir unless we created another subdirectory that contains the Userdir fields, like ~/.userdir/. There's also potential confusion: I have a lot of users whose home directories are not under /home. So for example, /home/pgsql/DIR might contain /usr/local/pgsql. Not really a big deal, and it's something fairly easy to work around with symlinks. If you like, you can try it under /home right now: ln -s /home /etc/users.


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