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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'clicknewsID', 'rewrite_titles', 'userid', 'posnewID'}) and 9 missing columns ({'ClicknewsID', 'start', 'dwelltime', 'neg', 'pos', 'dwelltime_pos', 'UserID', 'exposure_time', 'end'}).

This happened while the csv dataset builder was generating data using

hf://datasets/THEATLAS/PENS/personalized_test.tsv (at revision 181ff06919ea35f4c55514b882ab9eb02913a195)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              userid: string
              clicknewsID: string
              posnewID: string
              rewrite_titles: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 739
              to
              {'UserID': Value(dtype='string', id=None), 'ClicknewsID': Value(dtype='string', id=None), 'dwelltime': Value(dtype='string', id=None), 'exposure_time': Value(dtype='string', id=None), 'pos': Value(dtype='string', id=None), 'neg': Value(dtype='string', id=None), 'start': Value(dtype='string', id=None), 'end': Value(dtype='string', id=None), 'dwelltime_pos': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1387, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'clicknewsID', 'rewrite_titles', 'userid', 'posnewID'}) and 9 missing columns ({'ClicknewsID', 'start', 'dwelltime', 'neg', 'pos', 'dwelltime_pos', 'UserID', 'exposure_time', 'end'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/THEATLAS/PENS/personalized_test.tsv (at revision 181ff06919ea35f4c55514b882ab9eb02913a195)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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UserID
string
ClicknewsID
string
dwelltime
string
exposure_time
string
pos
string
neg
string
start
string
end
string
dwelltime_pos
string
U335175
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116 23 59 146 661 303 59 199 165 144 110 29 327 105 43 23 127 89 76 607 615 30 58 59 7 32 38 61 129 89 44 82 54 17 700 320
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N55476 N103556 N52756 N58377 N98303
N48119 N92507 N92467 N35759 N16584 N56727 N44985 N116560 N22545 N73265 N85178 N66205 N68668 N113976 N61932 N72480 N80095 N76018 N20196 N100054 N29975 N90902 N46163 N41315 N111419 N34593 N91129 N111369 N87250 N96810 N14269 N12771 N47610 N47217 N44681 N68466 N81398
7/3/2019 6:43:49 AM
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34 83 79 234 16
U146053
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105 62 41 11 52 300 55 17 73 52 145 24 96 38 33 75 105 109 114 8 131 15 141 19 52 85 0 1223 47 406 62 39 306 15 107 21 25 50 86 53 30 7 10 68 19 8 39 153 85 76 43 35 1 141 53 48 30 20 41 416 3 51 141 32 74 311 83 2 102 4 31 162 43 157 462 100 34 108 228 119 62 21 132 49 368 50 17 55 75 115 111 13 233 151 35 80 389 66 35 157 36 13 183 8 28 117 98 43 61 13 97 51 126 58 35 173 161 104 20 76 53 43 72 43 110 50 91 56 8 5 332 89 41 9 27 13 79 7 57 55 25 17 162 10 98 9 156 84 111 54 22 73 192 953 223 123 30 102 78 54 131 55 114 116 65 68 87 37 97 216 300 90 56 176 47 36 411 385 30 63 42 76 135 35 33
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6/23/2019 10:28:02 AM
6/23/2019 10:54:50 AM
5 34 90
U158889
N104156 N103864 N51305 N109347 N70019 N106791 N92116
175 58 2 871 169 69 10
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N72751 N62314
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6/28/2019 8:50:00 AM
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246 137
U22232
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4 15 3 31 9 26 216 208 58 7 481 32 5 29 13 2 10 5 38 147 89 31 5 6 10 25 18 1100 15 22 59 13 1 56
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N31619 N98303
N90820 N44438 N43527 N55476 N23227 N49161 N56417 N63630 N13051 N58025 N24542 N91882 N116232 N103167 N100294 N19252 N102198 N59484 N60278 N63644 N57085 N91792 N92075 N106367 N105732 N111744 N108505 N39324 N63933 N105706 N111658 N68466 N91571 N85067 N81398
7/3/2019 2:27:50 PM
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20 8
U32515
N55509 N15992 N27093 N78610 N13691 N103166 N117586 N40251 N60692 N28810 N90242 N91436 N104931 N94459 N39614 N77212 N14844 N22108 N61223 N40147 N91883 N41399 N26774 N38766 N76792
47 123 54 166 29 56 10 131 298 1592 146 48 45 10 142 959 431 266 455 215 438 317 178 291 9
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N78883 N56068
N111634 N19030 N122006 N32123 N50002 N42107 N29570 N97332 N66423 N35149 N121939 N25286 N122297 N121550
6/26/2019 9:20:16 AM
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105 279
U260848
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U116027
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N10164
N34615 N43378 N101667 N57764 N52708 N19179 N33408 N106422 N17235 N117791 N98399 N52196 N21961 N61657
7/1/2019 1:50:35 PM
7/1/2019 2:11:14 PM
987
U227465
N48218 N76978 N82756 N121349
80 163 286 506
6/16/2019 8:07:47 AM#TAB#6/16/2019 8:09:09 AM#TAB#6/16/2019 8:12:20 AM#TAB#6/16/2019 8:18:57 AM
N19264 N103938
N61154 N91436 N85806 N87864 N64635 N57729 N30480 N90754 N50528 N103844 N96583 N23894 N65118 N104156 N69266 N90215 N80169 N31693 N100449 N62386 N100679 N79001 N38780 N95147 N78207 N18295 N37252 N104931 N39096 N46285 N110080 N98288 N15022
6/17/2019 2:29:43 PM
6/17/2019 2:35:56 PM
70 206
U126292
N38263 N53399 N39488 N108227 N54216 N61912
923 605 1464 270 88 77
6/16/2019 12:05:16 PM#TAB#6/19/2019 6:13:17 AM#TAB#6/23/2019 9:28:56 AM#TAB#6/26/2019 3:56:45 AM#TAB#6/28/2019 6:03:44 AM#TAB#6/29/2019 8:00:25 AM
N40182 N12752
N74972 N35866 N97088 N87769 N22363 N24820 N79990 N21481 N75267 N72885 N58210 N49504 N16349 N103594 N62356 N10279 N110907 N18995 N105667 N13367 N112969 N66318 N27340 N67842 N79080 N34333 N61129 N51947 N22016 N61199 N116871 N23482 N64049 N107836 N56218 N106668 N49819 N19741 N108775
6/30/2019 8:22:30 AM
6/30/2019 8:48:05 AM
28 1056
U392096
N43145 N104113 N86423
146 34 3
6/14/2019 6:32:38 AM#TAB#6/14/2019 2:58:57 PM#TAB#6/14/2019 2:59:32 PM
N55079
N20548 N79468 N20249 N29341 N102239 N29409 N38888 N79391 N29609 N28810 N122875 N78100 N54306 N22607 N109656 N121200 N31307 N97815 N60692 N91754 N113804 N118009 N15270 N122374 N121666 N93290 N98087 N58762 N113715 N98989 N81795 N30532 N98578
6/16/2019 4:54:59 PM
6/16/2019 4:56:51 PM
22
U167237
N95202 N17380
66 424
6/14/2019 10:22:59 AM#TAB#6/17/2019 3:47:08 AM
N112960
N79723 N90247 N105690 N117839 N86613 N83288 N69542 N23679 N53549 N15693 N42322 N18385 N55093 N105995 N113788 N61629 N67918 N109347 N14844 N34345 N47779 N94701 N29244 N87169 N31523 N106815 N120214 N101546 N47757 N99452 N100653 N58957 N62337 N39451 N64197
6/20/2019 3:43:06 AM
6/20/2019 3:51:42 AM
357
U52926
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202 158 375 43 543 62 49 211 1615 15 3 19 19 65 233 312 72 245 69 91 58 70 151 36 316 31 14 101 83 155 21 268 26 101 69 178 396 140 232 163 142 400 44 293 24 439 5 61 26 131 55 190 82 85 32 26 133 115 92 72 61 164 245 6 2 427 76 684 332 158 441 171 620 165 116 60 622 92 33 83 781 105 56 570 196 266 637 619 64 123 162 463 67 106 114 23 240 529 118 86 276 337 75 551 32 22 31 70 95 472 395 141 11 82 2 243 148
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N89649 N67404 N98455
N97236 N37853 N73500 N116793 N74725 N98748 N93871 N72271 N63337 N54268 N107638 N36828 N37761 N26689 N46272 N49046 N77255 N23026 N30795 N79115 N101672 N58228 N59983 N10942 N83882 N80304 N55674 N78845 N115409 N49284 N47217 N25218 N87110
7/2/2019 10:10:39 AM
7/2/2019 10:23:39 AM
350 149 148
U16752
N28453 N43558 N28072 N31230 N39685 N15693 N52103 N116770 N111857
58 183 269 104 203 601 68 37 185
6/14/2019 7:36:20 AM#TAB#6/14/2019 7:37:33 AM#TAB#6/14/2019 7:41:34 AM#TAB#6/19/2019 5:24:48 AM#TAB#6/19/2019 5:28:19 AM#TAB#6/20/2019 6:05:27 AM#TAB#6/25/2019 5:05:29 AM#TAB#6/25/2019 9:38:03 AM#TAB#6/25/2019 9:41:05 AM
N37853
N97236 N74725 N119481 N86042 N15401 N114062 N72271 N19281 N114030 N63337 N54268 N107638 N36828 N89649 N37761 N17797 N33118 N67404 N26689 N96643 N80044 N46272 N49046 N77255 N121000 N35419 N111470 N30795 N25771 N84800 N79115 N71947 N65643 N74406 N98455 N55239 N101672 N55475 N46634 N99700 N94814 N47720 N10942 N80304 N63338 N64493
7/2/2019 8:25:42 AM
7/2/2019 9:03:53 AM
50
U23110
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U182115
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U57336
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37
U233541
N72931 N68652 N88352 N89579
378 12 4 47
6/17/2019 7:24:10 AM#TAB#6/17/2019 8:09:17 AM#TAB#6/17/2019 8:09:48 AM#TAB#6/17/2019 9:32:45 AM
N90094 N44474 N42583
N91695 N94579 N11341 N24788 N51008 N108433 N92024 N56678 N12095 N53158 N94044 N27333 N116833 N86089 N31391 N95251 N11804 N11809 N112765 N118868 N40997 N103211 N30579 N61760 N101671 N104279 N59188 N105649 N62585 N31114 N100773 N61448 N15761 N69333 N10028 N113834 N12695 N18071 N104663
6/18/2019 1:48:58 PM
6/18/2019 3:12:02 PM
918 2 89
U99450
N58774 N107795 N103166 N55055 N89576 N123553 N35557 N28810 N11254 N119816 N17333 N103504 N20763 N121349 N106444 N81795 N118009 N98578 N88470 N98288 N51924 N18236 N61154 N23894 N105259 N108068 N114501 N122382 N55111 N56083 N19170 N104096 N39112 N96281 N114357 N87665 N76427 N13029 N73398 N20724 N43826 N84809
3 26 13 16 49 47 31 33 142 606 67 3 265 84 406 35 61 2 300 85 46 88 28 37 157 29 50 139 613 35 4 13 21 15 107 36 68 53 81 50 41 16
6/15/2019 5:04:06 PM#TAB#6/15/2019 5:04:24 PM#TAB#6/15/2019 5:06:16 PM#TAB#6/15/2019 5:06:49 PM#TAB#6/15/2019 5:07:22 PM#TAB#6/16/2019 6:44:58 AM#TAB#6/16/2019 6:48:13 AM#TAB#6/16/2019 6:49:02 AM#TAB#6/16/2019 6:54:10 AM#TAB#6/16/2019 6:56:34 AM#TAB#6/16/2019 9:25:19 AM#TAB#6/16/2019 9:26:34 AM#TAB#6/16/2019 9:30:58 AM#TAB#6/16/2019 8:34:14 PM#TAB#6/16/2019 8:35:44 PM#TAB#6/16/2019 8:42:59 PM#TAB#6/16/2019 8:44:11 PM#TAB#6/16/2019 8:46:08 PM#TAB#6/17/2019 5:51:03 AM#TAB#6/17/2019 5:58:17 AM#TAB#6/17/2019 6:00:11 AM#TAB#6/17/2019 6:01:18 AM#TAB#6/17/2019 6:09:49 AM#TAB#6/17/2019 6:10:25 AM#TAB#6/17/2019 6:11:27 AM#TAB#6/17/2019 6:15:16 AM#TAB#6/17/2019 6:18:30 AM#TAB#6/17/2019 6:19:20 AM#TAB#6/17/2019 6:19:43 AM#TAB#6/19/2019 5:03:03 PM#TAB#6/19/2019 5:03:40 PM#TAB#6/19/2019 5:03:50 PM#TAB#6/19/2019 5:04:11 PM#TAB#6/19/2019 5:04:44 PM#TAB#6/19/2019 6:30:31 PM#TAB#6/19/2019 6:34:45 PM#TAB#6/19/2019 6:35:57 PM#TAB#6/19/2019 6:37:55 PM#TAB#6/21/2019 7:15:02 AM#TAB#6/21/2019 7:16:44 AM#TAB#6/21/2019 7:17:40 AM#TAB#6/21/2019 7:21:19 AM
N77761 N31593 N96955 N114804 N104861
N41512 N54453 N13801 N54294 N105387 N50311 N23152 N12782 N24360 N51752 N103034 N47410 N93930 N51441 N28736 N53793 N54333 N60374 N61309 N40185 N43877 N79851 N56067 N80731 N95754 N53204 N33745 N35349 N89528 N33451 N26809 N25224 N37654 N58412
6/27/2019 5:31:06 AM
6/27/2019 5:43:04 AM
8 59 182 44 36
U298624
N15248 N95202 N66631 N34599 N37510 N51717 N100715 N81109 N49989 N16538 N25041 N116110 N13725 N48343 N113191 N69366 N94044 N90064 N121047 N18071 N123025 N103211 N14451 N105649 N40778 N109004 N120253 N104663 N99430 N31115 N121693 N18912 N58331 N31487 N22227 N110954 N39056 N48341 N33837 N34345 N91454 N67437 N22108 N50278 N118949 N70019 N39221 N94492 N79723 N111024 N43216 N121044 N68268 N21824 N42193 N88771 N110015 N30838 N87468 N52080 N56158 N117135 N25525
46 113 9 77 43 67 9 12 112 16 41 198 122 540 181 18 83 13 203 29 58 92 94 90 14 410 31 2 130 58 135 105 88 106 53 135 159 2 22 55 133 3 174 96 100 55 20 27 12 126 7 85 7 273 101 30 264 22 43 59 79 53 94
6/14/2019 4:58:15 AM#TAB#6/14/2019 10:19:45 AM#TAB#6/14/2019 10:30:10 AM#TAB#6/14/2019 11:17:19 AM#TAB#6/14/2019 11:32:15 AM#TAB#6/14/2019 11:36:12 AM#TAB#6/14/2019 11:46:10 AM#TAB#6/14/2019 11:49:01 AM#TAB#6/17/2019 5:47:24 AM#TAB#6/17/2019 5:50:34 AM#TAB#6/17/2019 5:57:23 AM#TAB#6/17/2019 6:01:05 AM#TAB#6/18/2019 5:13:57 AM#TAB#6/18/2019 8:45:09 AM#TAB#6/18/2019 8:54:59 AM#TAB#6/18/2019 12:10:15 PM#TAB#6/18/2019 12:11:28 PM#TAB#6/18/2019 12:15:57 PM#TAB#6/18/2019 12:16:11 PM#TAB#6/18/2019 12:47:31 PM#TAB#6/18/2019 12:47:57 PM#TAB#6/18/2019 1:08:47 PM#TAB#6/18/2019 1:11:57 PM#TAB#6/18/2019 1:13:40 PM#TAB#6/18/2019 1:20:53 PM#TAB#6/18/2019 1:20:58 PM#TAB#6/18/2019 1:37:34 PM#TAB#6/18/2019 1:48:42 PM#TAB#6/19/2019 5:20:44 AM#TAB#6/19/2019 5:26:34 AM#TAB#6/19/2019 5:28:01 AM#TAB#6/19/2019 1:04:36 PM#TAB#6/19/2019 1:08:08 PM#TAB#6/19/2019 1:14:42 PM#TAB#6/19/2019 1:21:00 PM#TAB#6/19/2019 1:21:24 PM#TAB#6/19/2019 1:26:51 PM#TAB#6/19/2019 1:27:05 PM#TAB#6/19/2019 1:29:49 PM#TAB#6/20/2019 5:14:44 AM#TAB#6/20/2019 9:24:30 AM#TAB#6/20/2019 11:39:50 AM#TAB#6/20/2019 11:40:40 AM#TAB#6/20/2019 11:44:53 AM#TAB#6/20/2019 11:48:16 AM#TAB#6/20/2019 1:09:56 PM#TAB#6/20/2019 1:11:04 PM#TAB#6/20/2019 1:17:17 PM#TAB#6/20/2019 1:19:39 PM#TAB#6/20/2019 1:20:43 PM#TAB#6/20/2019 1:23:42 PM#TAB#6/21/2019 9:38:53 AM#TAB#6/21/2019 9:46:23 AM#TAB#6/21/2019 9:46:31 AM#TAB#6/21/2019 9:48:36 AM#TAB#6/21/2019 9:51:11 AM#TAB#6/21/2019 1:33:09 PM#TAB#6/21/2019 1:42:13 PM#TAB#6/21/2019 1:49:08 PM#TAB#6/21/2019 1:50:27 PM#TAB#6/24/2019 5:16:27 AM#TAB#6/24/2019 5:18:53 AM#TAB#6/24/2019 5:19:56 AM
N18133 N58481
N59829 N79510 N42862 N56489 N68125 N75354 N85987 N31017 N49355 N83191 N22640 N119226 N116989 N44594 N28688 N81302 N26416 N69937 N25251 N52760 N80140 N79855 N82706 N95738 N84101 N87986 N17491 N95003 N112738 N37403 N114400 N69103
6/25/2019 6:19:38 AM
6/25/2019 6:48:38 AM
162 78
U299988
N52481 N61763 N107795 N46954 N43826
328 196 104 73 61
6/14/2019 10:08:36 AM#TAB#6/16/2019 5:21:10 AM#TAB#6/16/2019 5:27:07 AM#TAB#6/18/2019 7:10:55 PM#TAB#6/21/2019 9:57:15 AM
N69599 N58500
N39964 N14872 N108858 N118951 N71699 N89429 N66336 N17442 N18659 N59713 N44139 N84470 N25990 N74501 N95521 N74267 N49919 N48408 N95906 N26218 N15467 N48906 N92626 N111593 N56261 N110386 N121758 N115478 N56547 N75117 N112696 N46487 N18523 N36857 N99138 N64645 N85909 N105957 N66660 N72416 N39191 N99785
6/22/2019 3:44:13 PM
6/22/2019 4:13:21 PM
100 56
U429177
N55111 N12470 N13696 N100679 N94044 N12695 N119155 N92115 N55167 N101671 N114725 N15761 N19024 N48858
50 42 100 41 90 60 139 71 39 17 16 128 76 65
6/17/2019 5:42:49 AM#TAB#6/18/2019 5:16:22 AM#TAB#6/18/2019 5:45:36 AM#TAB#6/18/2019 5:52:59 AM#TAB#6/18/2019 8:43:59 AM#TAB#6/18/2019 8:51:29 AM#TAB#6/18/2019 9:44:03 AM#TAB#6/18/2019 12:07:27 PM#TAB#6/18/2019 12:10:52 PM#TAB#6/18/2019 1:06:17 PM#TAB#6/19/2019 5:29:25 AM#TAB#6/19/2019 5:33:21 AM#TAB#6/19/2019 8:49:56 AM#TAB#6/19/2019 8:52:50 AM
N81145
N113051 N111360 N120443 N87546 N119985 N84809 N93019 N64404 N81907 N106486 N42576 N64846 N107618 N28149 N56180 N43334 N54014 N110015 N95038 N121451 N43826 N108781 N24847 N89907 N78798 N74429 N10898 N72126 N23948 N68456 N89085 N114715 N109436 N55938 N20724 N119047 N89196 N80819 N89208 N22865 N86743
6/21/2019 4:38:21 AM
6/21/2019 4:50:32 AM
104
U217448
N43558 N77086 N122550
725 46 699
6/14/2019 8:15:43 AM#TAB#6/14/2019 12:36:10 PM#TAB#6/17/2019 3:48:23 PM
N56674 N86226
N81948 N84343 N111808 N104733 N80645 N20031 N119531 N31437 N97643 N87665 N92602 N111452 N92972 N27570 N56386 N99572 N69139 N31115 N57315 N46954 N104263 N47528 N60443 N39614 N112492 N104096 N121292 N46244 N123024 N77361 N93379 N49794 N84273 N46252 N117076 N31573 N54516 N95007 N79600 N21782 N11156 N94956 N113677 N52578 N85749 N58206 N34483
6/19/2019 10:09:02 AM
6/19/2019 10:34:24 AM
134 84
U118907
N75768 N58081 N25368 N113705 N98288 N11143 N78088 N51184 N71657 N38517 N40778
16 19 315 47 2 12 109 5 16 61 222
6/14/2019 9:36:42 AM#TAB#6/14/2019 9:36:46 AM#TAB#6/14/2019 9:37:12 AM#TAB#6/17/2019 8:42:06 AM#TAB#6/17/2019 8:42:33 AM#TAB#6/17/2019 8:44:21 AM#TAB#6/18/2019 10:10:07 AM#TAB#6/18/2019 10:14:26 AM#TAB#6/18/2019 10:14:51 AM#TAB#6/18/2019 10:15:47 AM#TAB#6/18/2019 10:24:19 AM
N63252 N99572 N47528 N75058
N116937 N81948 N84343 N111808 N104733 N22227 N80645 N23921 N20031 N31437 N97643 N87665 N50599 N55139 N18457 N111452 N92972 N56386 N49383 N18912 N69139 N46954 N39614 N104096 N77212 N46244 N77361 N93379 N49794 N109636 N46252 N36405 N86196 N74743 N37719 N48341 N25862 N48999 N44759 N117076 N31573 N110954 N54516 N21413 N79600 N81223 N11248 N43382 N11156 N65059 N94956 N53438 N46765 N52578 N58206 N105557 N34483
6/19/2019 11:41:36 AM
6/19/2019 12:11:41 PM
15 13 41 89
U317643
N27499 N37813 N47697 N81048 N123402 N94579 N76864 N24914 N88991 N79300 N122835 N98578 N103854 N84319
75 30 21 142 18 31 13 7 51 179 151 243 8 469
6/14/2019 5:20:46 AM#TAB#6/14/2019 8:15:51 AM#TAB#6/14/2019 9:00:48 AM#TAB#6/14/2019 9:05:29 AM#TAB#6/14/2019 9:09:57 AM#TAB#6/14/2019 9:14:24 AM#TAB#6/14/2019 9:14:49 AM#TAB#6/14/2019 9:14:51 AM#TAB#6/14/2019 10:42:23 AM#TAB#6/14/2019 11:22:05 AM#TAB#6/14/2019 11:35:13 AM#TAB#6/14/2019 12:07:36 PM#TAB#6/14/2019 12:11:39 PM#TAB#6/14/2019 1:29:09 PM
N103844
N85806 N39990 N93732 N115495 N51924 N59685 N14961 N88470 N81726 N91267 N119833 N115534 N112255 N110099 N72064
6/17/2019 11:15:40 AM
6/17/2019 11:27:39 AM
28
U288300
N44141 N73870 N22545
121 8 2
7/1/2019 4:07:51 PM#TAB#7/2/2019 3:50:10 PM#TAB#7/3/2019 3:11:36 AM
N96373
N69635 N65982 N92295 N40211 N110935 N83257 N98752 N93042 N57523 N74516 N69952 N28533 N11071 N97496
7/4/2019 8:34:28 PM
7/4/2019 8:38:53 PM
68
U2582
N67649 N58081 N95202
29 200 17
6/14/2019 1:55:11 AM#TAB#6/14/2019 10:36:29 AM#TAB#6/14/2019 12:25:56 PM
N92168
N11893 N88026 N39897 N119816 N67166 N75461 N123553 N111562 N59993
6/16/2019 4:25:38 AM
6/16/2019 4:52:23 AM
39
U92676
N123225 N11255 N58081 N50771 N121334 N98923 N79300 N20589 N109222 N57729 N59308
106 105 30 1 111 48 46 60 38 301 3
6/14/2019 6:00:53 AM#TAB#6/14/2019 6:02:50 AM#TAB#6/14/2019 6:43:14 AM#TAB#6/14/2019 6:43:20 AM#TAB#6/14/2019 7:35:26 AM#TAB#6/14/2019 7:37:18 AM#TAB#6/14/2019 8:39:54 AM#TAB#6/17/2019 2:31:13 PM#TAB#6/17/2019 2:32:55 PM#TAB#6/17/2019 6:40:48 PM#TAB#6/17/2019 6:40:52 PM
N111808
N81948 N84343 N92972 N18912 N31115 N39614 N104096 N123024 N77361 N93379 N109636 N48999 N44759 N117076 N110954 N79600 N21782 N56975 N86226
6/19/2019 11:07:17 AM
6/19/2019 11:10:16 AM
94
U193564
N27807 N82259 N118791 N27093 N24612 N102644 N23912
9 22 41 68 72 31 73
6/13/2019 5:05:33 PM#TAB#6/13/2019 5:37:34 PM#TAB#6/14/2019 6:34:16 PM#TAB#6/14/2019 6:34:33 PM#TAB#6/14/2019 6:34:46 PM#TAB#6/14/2019 6:44:48 PM#TAB#6/14/2019 6:46:49 PM
N94579 N90064
N96530 N50461 N101248 N91695 N90094 N116546 N11341 N92024 N54273 N56678 N110105 N35418 N95456 N86089 N11804 N37501 N38939 N95772 N20178 N101671 N104651 N19160 N31114 N18094 N15761 N114725 N34830 N121466 N18071
6/18/2019 6:51:51 PM
6/18/2019 7:00:33 PM
11 20
U10003
N113715 N82259 N30838 N67437 N61223 N47034 N10817 N37308 N49515 N103951 N47934 N117433 N56485 N13691 N96955 N43877 N15119
9 31 11 167 31 387 72 3 606 7 55 6 90 51 333 518 50
6/13/2019 6:24:58 PM#TAB#6/13/2019 6:25:08 PM#TAB#6/13/2019 6:25:24 PM#TAB#6/20/2019 7:37:13 PM#TAB#6/20/2019 7:40:57 PM#TAB#6/23/2019 6:30:49 PM#TAB#6/23/2019 6:37:34 PM#TAB#6/23/2019 6:38:46 PM#TAB#6/23/2019 6:51:27 PM#TAB#6/23/2019 6:51:40 PM#TAB#6/24/2019 1:08:26 PM#TAB#6/24/2019 1:08:39 PM#TAB#6/24/2019 1:09:22 PM#TAB#6/24/2019 1:41:59 PM#TAB#6/27/2019 6:53:23 AM#TAB#6/27/2019 6:59:06 AM#TAB#6/29/2019 7:45:44 PM
N122527 N75168
N88045 N120122 N108379 N34615 N66402 N88819 N96884 N29363 N84898 N38386 N43378 N12913 N121240 N123483 N101667 N22514 N30600 N57764 N52708 N43104 N33408 N36079 N119568 N106373 N117791 N68345 N86945 N41719 N122614 N112168 N61657 N36067 N113102 N52847
7/1/2019 1:33:08 PM
7/1/2019 1:52:40 PM
29 63
U101398
N13696 N19101 N82453 N41392 N98516 N122803
98 908 16 23 169 367
6/19/2019 4:52:43 AM#TAB#6/21/2019 2:31:33 PM#TAB#6/21/2019 2:47:57 PM#TAB#6/21/2019 2:48:59 PM#TAB#6/21/2019 2:51:22 PM#TAB#6/22/2019 8:10:09 AM
N112743
N27508 N77334 N47748 N72751 N53464 N11234 N114263 N14396 N71612 N64005 N105817 N59858 N71728
6/28/2019 9:34:40 AM
6/28/2019 9:35:21 AM
30
U46343
N83606 N43145 N39195 N69326 N84162 N60541 N39995 N119816 N14376 N85514 N16840 N23870 N114578 N40251 N104138 N39094 N15124 N122334 N29427 N54647 N38780 N41447 N32132 N42583 N66523 N30104 N31487 N69542 N86613 N108981 N41392 N87253 N98516 N82910 N65832 N92786 N54265 N40558 N71699 N39191 N96346 N93807 N51035 N48963 N15023
180 178 48 199 618 57 654 47 145 24 9 210 87 41 322 71 827 1059 125 107 83 143 48 116 139 924 68 176 139 418 152 219 280 677 45 26 1789 7 11 15 9 255 209 77 566
6/14/2019 5:55:42 AM#TAB#6/14/2019 6:56:23 AM#TAB#6/14/2019 8:10:55 AM#TAB#6/14/2019 8:11:54 AM#TAB#6/14/2019 1:14:38 PM#TAB#6/14/2019 1:45:27 PM#TAB#6/14/2019 1:47:01 PM#TAB#6/15/2019 10:06:27 AM#TAB#6/15/2019 10:09:56 AM#TAB#6/16/2019 4:51:36 AM#TAB#6/16/2019 4:51:42 AM#TAB#6/16/2019 5:39:55 AM#TAB#6/16/2019 5:51:37 AM#TAB#6/16/2019 5:54:05 AM#TAB#6/16/2019 8:51:11 AM#TAB#6/16/2019 5:33:07 PM#TAB#6/17/2019 6:49:16 AM#TAB#6/17/2019 7:10:43 AM#TAB#6/17/2019 3:39:15 PM#TAB#6/18/2019 6:44:03 AM#TAB#6/18/2019 6:46:00 AM#TAB#6/18/2019 6:47:45 AM#TAB#6/18/2019 6:50:56 AM#TAB#6/18/2019 12:33:25 PM#TAB#6/18/2019 12:35:29 PM#TAB#6/18/2019 12:38:16 PM#TAB#6/19/2019 4:35:57 PM#TAB#6/20/2019 7:25:14 AM#TAB#6/20/2019 7:28:24 AM#TAB#6/20/2019 7:34:20 AM#TAB#6/21/2019 1:35:14 PM#TAB#6/21/2019 1:40:46 PM#TAB#6/21/2019 2:21:53 PM#TAB#6/21/2019 2:27:26 PM#TAB#6/22/2019 9:33:04 AM#TAB#6/22/2019 9:37:22 AM#TAB#6/22/2019 9:37:30 AM#TAB#6/22/2019 10:26:22 AM#TAB#6/22/2019 11:20:09 AM#TAB#6/22/2019 11:24:21 AM#TAB#6/23/2019 8:25:26 AM#TAB#6/23/2019 9:33:52 AM#TAB#6/24/2019 8:39:36 AM#TAB#6/24/2019 8:47:56 AM#TAB#6/24/2019 8:50:06 AM
N102300
N110460 N70065 N41979 N98034 N38633 N42107 N101558 N64949 N104066 N67373 N65931 N45846 N62288 N25568 N53826 N108673 N27719 N105874 N49846 N93930 N100594 N28736 N85801 N67174 N52277 N31593 N43877 N21194 N109447 N24092 N97569 N39027 N96275 N15119
6/26/2019 6:45:31 PM
6/26/2019 6:46:31 PM
37
U335396
N28453 N78688 N61215 N114543 N117282 N81109 N93595 N70174 N121624 N40378 N99572 N110505 N39056 N87207 N94956 N31487 N69266 N121926 N65016 N51461 N78424 N81173 N34075 N109348 N50742 N105995 N34572 N110194 N115025 N63309 N73547 N100774 N82910 N57880 N23271 N23627 N88272 N73842 N19101 N109007 N67502 N95241 N56485 N48963 N49715 N41621 N30054 N24462 N114664 N25273 N37403 N119720 N69937 N26047 N20284 N70816 N115207 N33474 N42359 N44266 N70710 N103573 N118668 N23441 N75626 N30400 N69103 N108227 N35224 N72944 N18804 N95365 N96088 N96964 N25286 N117350 N34493 N96955 N39236 N51752 N103034 N32119 N63922 N118547
76 480 298 830 54 62 19 508 40 246 104 127 68 366 5 160 383 266 790 50 96 25 307 794 399 952 609 85 435 588 1799 697 354 9 258 52 975 423 1059 364 85 936 386 576 64 215 139 566 115 396 54 875 614 49 267 229 1790 51 273 69 89 1134 80 265 277 81 51 207 104 5 65 113 1074 203 93 154 246 152 265 80 37 776 223 518
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6/20/2019 8:27:14 AM
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631
U417753
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U277796
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U27007
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U97018
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525
U43526
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U212945
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7/1/2019 8:31:56 AM
7/1/2019 8:57:24 AM
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U345533
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6/27/2019 8:37:42 AM
6/27/2019 9:33:21 AM
14 178 127 8
U420796
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6/20/2019 4:05:59 PM
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235 39
U296924
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6/27/2019 5:48:57 PM
6/27/2019 5:51:11 PM
104
U371934
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U15036
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6/27/2019 7:37:47 AM
6/27/2019 7:58:41 AM
69 106 30 30 736
U267403
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30 23 3 33 51 68 8 148 34 7 97 17 32 4 15 25 3 13 125 29 21 35 141 14 67 4 52 673 53 8 9 33 4 49 25 126 12 9 42 28 15 46 41 90 47
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6/26/2019 7:23:32 PM
6/26/2019 7:24:24 PM
19
U396469
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6/14/2019 8:21:55 PM#TAB#6/16/2019 8:25:49 PM#TAB#6/16/2019 8:29:47 PM
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6/18/2019 6:29:31 AM
6/18/2019 6:39:11 AM
197 3
U21637
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66
6/14/2019 8:09:10 AM
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6/17/2019 11:35:11 AM
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U40732
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N68462
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6/26/2019 7:06:08 AM
6/26/2019 7:12:15 AM
46
U309201
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8 270 113 167 174 729 1151 88 567 167 137 457 107 45 113 213 135 225 158 89 196 84 39 86 26 332 172 255 44 17 192 1181 28 30 620 59 82 210 111 15 32 71 69 120 451 62 21 150 179 206 406 490 1464 128 72
6/17/2019 9:15:41 AM#TAB#6/17/2019 9:36:27 AM#TAB#6/17/2019 9:41:13 AM#TAB#6/17/2019 9:53:35 AM#TAB#6/17/2019 10:25:57 AM#TAB#6/17/2019 10:29:48 AM#TAB#6/17/2019 12:20:17 PM#TAB#6/17/2019 1:15:02 PM#TAB#6/17/2019 1:16:51 PM#TAB#6/17/2019 1:26:32 PM#TAB#6/17/2019 1:30:36 PM#TAB#6/17/2019 2:09:20 PM#TAB#6/17/2019 2:18:38 PM#TAB#6/17/2019 2:23:08 PM#TAB#6/17/2019 2:26:08 PM#TAB#6/17/2019 2:41:11 PM#TAB#6/18/2019 5:22:42 AM#TAB#6/18/2019 5:24:58 AM#TAB#6/18/2019 5:30:12 AM#TAB#6/18/2019 5:32:51 AM#TAB#6/18/2019 7:40:23 AM#TAB#6/18/2019 7:46:38 AM#TAB#6/18/2019 8:37:51 AM#TAB#6/18/2019 9:31:46 AM#TAB#6/18/2019 10:45:16 AM#TAB#6/18/2019 11:42:13 AM#TAB#6/18/2019 1:03:33 PM#TAB#6/18/2019 1:06:34 PM#TAB#6/18/2019 1:11:17 PM#TAB#6/18/2019 1:12:01 PM#TAB#6/18/2019 2:52:01 PM#TAB#6/19/2019 9:03:48 AM#TAB#6/19/2019 9:24:23 AM#TAB#6/19/2019 2:08:00 PM#TAB#6/19/2019 2:41:19 PM#TAB#6/19/2019 2:55:39 PM#TAB#6/19/2019 3:57:18 PM#TAB#6/19/2019 3:58:52 PM#TAB#6/19/2019 4:03:29 PM#TAB#6/19/2019 4:07:25 PM#TAB#6/20/2019 7:43:32 AM#TAB#6/20/2019 7:44:54 AM#TAB#6/20/2019 7:56:56 AM#TAB#6/20/2019 8:00:31 AM#TAB#6/20/2019 8:45:28 AM#TAB#6/20/2019 8:53:43 AM#TAB#6/20/2019 9:00:13 AM#TAB#6/20/2019 9:09:50 AM#TAB#6/20/2019 9:15:57 AM#TAB#6/20/2019 9:18:57 AM#TAB#6/20/2019 9:39:34 AM#TAB#6/20/2019 9:47:46 AM#TAB#6/20/2019 1:57:49 PM#TAB#6/20/2019 2:22:49 PM#TAB#6/20/2019 2:26:16 PM
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6/27/2019 5:27:01 AM
6/27/2019 5:38:35 AM
463 83
U271234
N72931 N78965 N48117 N122382 N65974 N90242 N12581 N90215 N81726 N46845 N78202 N55111 N87417 N13696 N39572 N102515 N94053 N93306 N38780 N29420 N49800 N13555 N80907 N12607 N108433 N38971 N111556 N90425 N90064 N80871 N67918 N69542 N47779 N87169 N94492 N73098 N48341 N80819 N105712 N109436 N20967 N63716 N120673 N97150
62 48 51 10 18 22 135 199 125 1537 66 302 13 322 15 39 89 586 66 36 275 35 147 708 39 44 36 216 25 19 109 64 101 96 13 2 1455 32 79 8 50 18 86 506
6/17/2019 4:42:56 AM#TAB#6/17/2019 4:44:02 AM#TAB#6/17/2019 4:44:50 AM#TAB#6/17/2019 4:46:31 AM#TAB#6/17/2019 4:47:11 AM#TAB#6/17/2019 4:47:46 AM#TAB#6/17/2019 4:49:53 AM#TAB#6/17/2019 10:51:41 AM#TAB#6/17/2019 10:56:09 AM#TAB#6/17/2019 10:58:22 AM#TAB#6/17/2019 11:24:22 AM#TAB#6/17/2019 11:25:51 AM#TAB#6/17/2019 11:30:54 AM#TAB#6/18/2019 4:14:56 AM#TAB#6/18/2019 4:24:44 AM#TAB#6/18/2019 4:26:18 AM#TAB#6/18/2019 4:44:21 AM#TAB#6/18/2019 4:46:37 AM#TAB#6/18/2019 4:56:52 AM#TAB#6/18/2019 4:58:23 AM#TAB#6/18/2019 7:26:19 AM#TAB#6/18/2019 7:31:02 AM#TAB#6/18/2019 7:31:52 AM#TAB#6/18/2019 7:34:28 AM#TAB#6/18/2019 7:49:32 AM#TAB#6/18/2019 7:50:13 AM#TAB#6/18/2019 7:51:09 AM#TAB#6/18/2019 7:52:37 AM#TAB#6/18/2019 8:02:24 AM#TAB#6/20/2019 4:55:23 AM#TAB#6/20/2019 4:56:12 AM#TAB#6/20/2019 4:59:08 AM#TAB#6/20/2019 5:00:17 AM#TAB#6/20/2019 5:02:12 AM#TAB#6/20/2019 7:51:16 AM#TAB#6/20/2019 11:09:33 AM#TAB#6/20/2019 11:11:05 AM#TAB#6/21/2019 4:58:34 AM#TAB#6/21/2019 7:28:24 AM#TAB#6/21/2019 7:29:58 AM#TAB#6/21/2019 7:30:53 AM#TAB#6/21/2019 7:32:59 AM#TAB#6/21/2019 7:34:44 AM#TAB#6/21/2019 12:05:30 PM
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6/25/2019 4:11:10 AM
6/25/2019 4:30:27 AM
123 31 51 109 49 161
U424171
N11192 N95521
427 205
6/21/2019 2:05:55 PM#TAB#6/22/2019 4:11:01 AM
N112448
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6/29/2019 5:56:54 AM
6/29/2019 5:59:25 AM
108
U24635
N27499 N39862 N112052 N37762 N58081 N55509 N84542 N112590 N11857 N51238 N11338 N35834 N106444 N75338 N59685 N120005 N102239 N23870 N54647 N12607 N79595 N85531 N65691 N89169 N81020 N109041 N30664 N15022 N83978 N65623 N56004 N75100 N37501 N47341 N90064 N104651 N42773 N93808 N59188 N55159 N66005 N74139 N69542 N29244 N47757 N93025 N37938 N110523 N54797 N104263 N122445 N12313 N11282 N109347 N94492 N23247 N15778 N95228 N17435 N23679 N105995 N48546 N98484 N83288 N90221 N26280 N117503 N15823 N24180 N42752 N73547 N89907 N73398 N68456 N46307 N77206 N44943 N123062 N65212 N64350 N32729 N91753 N10898 N93019
107 33 14 23 9 8 3 302 12 12 179 206 5 191 78 18 16 34 204 70 171 123 24 118 335 290 86 342 131 35 568 185 101 17 10 156 16 8 64 282 1019 180 126 111 30 606 55 611 210 16 221 26 155 55 35 36 50 12 43 101 61 38 24 70 6 13 8 42 23 39 495 88 612 183 63 66 41 51 23 57 16 26 116 38
6/14/2019 3:12:35 AM#TAB#6/14/2019 3:22:41 AM#TAB#6/14/2019 3:23:08 AM#TAB#6/14/2019 3:26:23 AM#TAB#6/14/2019 3:26:59 AM#TAB#6/14/2019 3:28:20 AM#TAB#6/14/2019 3:38:47 AM#TAB#6/14/2019 3:38:51 AM#TAB#6/14/2019 3:40:33 AM#TAB#6/14/2019 3:41:37 AM#TAB#6/14/2019 3:41:58 AM#TAB#6/17/2019 12:04:06 AM#TAB#6/17/2019 12:08:15 AM#TAB#6/17/2019 12:08:24 AM#TAB#6/17/2019 12:11:51 AM#TAB#6/17/2019 12:15:01 AM#TAB#6/17/2019 12:22:32 AM#TAB#6/17/2019 12:22:49 AM#TAB#6/17/2019 10:18:17 PM#TAB#6/17/2019 10:22:47 PM#TAB#6/17/2019 10:24:14 PM#TAB#6/17/2019 10:28:20 PM#TAB#6/17/2019 10:30:32 PM#TAB#6/17/2019 10:31:05 PM#TAB#6/17/2019 10:33:45 PM#TAB#6/17/2019 10:39:39 PM#TAB#6/17/2019 10:46:04 PM#TAB#6/18/2019 12:18:27 AM#TAB#6/18/2019 12:20:18 AM#TAB#6/18/2019 12:23:45 AM#TAB#6/18/2019 1:10:39 AM#TAB#6/18/2019 3:16:23 AM#TAB#6/18/2019 8:48:45 PM#TAB#6/18/2019 8:50:33 PM#TAB#6/18/2019 8:54:10 PM#TAB#6/18/2019 10:57:36 PM#TAB#6/18/2019 11:00:19 PM#TAB#6/18/2019 11:01:11 PM#TAB#6/19/2019 1:08:21 AM#TAB#6/19/2019 1:09:40 AM#TAB#6/19/2019 1:14:34 AM#TAB#6/19/2019 1:31:53 AM#TAB#6/19/2019 10:23:05 PM#TAB#6/19/2019 10:25:18 PM#TAB#6/19/2019 10:29:39 PM#TAB#6/19/2019 10:30:51 PM#TAB#6/19/2019 11:19:38 PM#TAB#6/19/2019 11:22:27 PM#TAB#6/20/2019 12:17:47 AM#TAB#6/20/2019 12:22:05 AM#TAB#6/20/2019 12:23:03 AM#TAB#6/20/2019 2:19:37 AM#TAB#6/20/2019 2:20:02 AM#TAB#6/20/2019 2:23:20 AM#TAB#6/20/2019 2:38:20 AM#TAB#6/20/2019 3:22:05 AM#TAB#6/20/2019 3:22:42 AM#TAB#6/20/2019 3:23:33 AM#TAB#6/20/2019 3:23:46 AM#TAB#6/20/2019 3:24:29 AM#TAB#6/20/2019 3:26:11 AM#TAB#6/20/2019 3:27:13 AM#TAB#6/20/2019 3:28:53 AM#TAB#6/20/2019 3:30:29 AM#TAB#6/20/2019 3:30:35 AM#TAB#6/20/2019 3:30:38 AM#TAB#6/20/2019 3:30:40 AM#TAB#6/20/2019 3:30:41 AM#TAB#6/20/2019 3:30:44 AM#TAB#6/20/2019 3:30:49 AM#TAB#6/20/2019 9:55:26 PM#TAB#6/20/2019 10:03:57 PM#TAB#6/20/2019 10:05:56 PM#TAB#6/21/2019 12:08:40 AM#TAB#6/21/2019 12:16:19 AM#TAB#6/21/2019 12:17:22 AM#TAB#6/21/2019 12:18:29 AM#TAB#6/21/2019 12:20:32 AM#TAB#6/21/2019 12:21:23 AM#TAB#6/21/2019 12:21:26 AM#TAB#6/21/2019 12:21:41 AM#TAB#6/21/2019 12:21:57 AM#TAB#6/21/2019 3:30:41 AM#TAB#6/21/2019 3:42:58 AM
N89617 N47034 N25041
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6/23/2019 8:30:32 PM
6/23/2019 8:36:20 PM
5 51 68
U35582
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38 130 35 101 90 253 365 78 84 173 93 240 71 12 58 1895 137 466 63 16 4 57 190 593 11 788 71 254 437
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N122296
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6/24/2019 9:08:07 AM
6/24/2019 9:14:40 AM
180
U255919
N32616 N35057 N43870 N32376 N60556 N95202 N104902 N102284 N81866 N14869 N56273 N20569 N118791 N64845 N95302 N24616 N84284 N30191 N85195 N46901 N95888 N39567 N78602 N78692 N62724 N97407 N25399 N120034 N116986 N62746 N17678 N120463 N37068 N60665 N88517 N84047 N117159 N103136 N116761 N49275 N67166 N33444 N111562 N79439 N83094 N96151 N123255 N113804 N71793 N116316 N60692 N19097 N68510 N90825 N101920 N92870 N79391 N22968 N59030 N66686 N81795 N55111 N89157 N39862 N46845 N34212 N122382 N64027 N51959 N63201 N42055 N65118 N50552 N48546 N18236 N94494 N93732 N115639 N71950 N95144 N49059 N53754 N65623 N110080 N109222 N99715 N121035 N73739 N15978 N43590 N104663 N94800 N12607 N17723 N88434 N62585 N39637 N48343 N59188 N94554 N103211 N43507 N71960 N122712 N87866 N21443 N121292 N113375 N76989 N46244 N74033 N99430 N56674 N64187 N18401 N19160 N32305 N33527 N117735 N116598 N95616 N54259 N13541 N35483 N65212 N102271 N42740 N104096 N42970 N41179 N17185 N21782 N86613 N47757 N92299 N50023 N88921 N61808 N84255 N122471 N114883 N34856 N43334 N117082 N61343 N56849 N123576 N54014 N80475 N90282 N110386 N84470 N115478 N95521 N18798 N122631 N78403 N15124 N24765 N102718 N74267 N85923 N13042 N116564 N77177 N14064 N43380 N102856 N29737 N105956 N96346 N122906 N25149 N72333 N26553 N57276 N34961 N24855 N58635 N115842 N111918 N48963 N120222 N70388 N24548 N86982 N91883 N31878 N44945 N27094 N99177 N62806 N29531 N76253 N111878 N119412 N101843 N50846 N102360 N78899 N28779 N38766 N43484 N115027 N68348 N96251 N25251 N52760 N115883 N28753 N105381 N96088 N108536 N56487 N29428 N12536 N38631 N74614 N23544 N42862 N68425 N15199 N57990 N106301 N85912 N122297 N121088 N25286 N30097 N97907 N31008 N108676 N81369 N107836 N119606 N31373 N50773 N31286 N78453 N15474 N89052 N74040 N95981 N50596 N34290 N107070 N35119 N14852 N88501 N10273 N67842 N114303 N102424 N70428 N59937 N11182 N53960 N44579 N74251 N77138 N72652 N22016 N76977 N110561 N80763
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N32327
N86042 N114062 N114030 N36828 N49411 N77255 N80304
7/2/2019 8:50:54 AM
7/2/2019 8:55:48 AM
230
U29835
N65623
560
6/17/2019 1:01:39 PM
N42576
N122726 N76965 N64846 N84898 N87468 N81145 N108781 N21824 N33190 N72126 N20724 N119047 N69164 N105182 N27277 N20113 N54754 N42193 N24765 N30933 N121044 N72656 N81217 N72108 N73324 N109307 N27315 N26492 N112308 N80758
6/21/2019 10:36:30 AM
6/21/2019 11:26:49 AM
221
U380506
N29419 N95868 N28779 N46311 N25251 N26774 N59983 N60748
4 101 99 201 318 138 210 166
6/14/2019 9:04:24 AM#TAB#6/20/2019 6:28:24 PM#TAB#6/24/2019 3:59:22 PM#TAB#6/24/2019 4:01:17 PM#TAB#6/24/2019 4:07:31 PM#TAB#6/24/2019 4:13:50 PM#TAB#7/2/2019 2:30:32 PM#TAB#7/2/2019 4:06:50 PM
N70442
N64320 N89321 N55476 N97555 N58242 N13051 N42869 N116232 N96463 N80562 N68263 N59484 N64139 N91792 N100440 N84562 N106367 N118581 N51710 N28387 N118053 N69737 N52876 N68466 N62274 N98303 N91571 N116811
7/3/2019 5:11:42 PM
7/3/2019 5:21:50 PM
3
U98537
N37222 N103844 N42407
97 17 7
6/13/2019 6:12:16 PM#TAB#6/17/2019 10:28:51 AM#TAB#6/17/2019 6:17:58 PM
N99430 N99572
N81948 N84343 N104733 N22227 N80645 N119531 N31437 N50599 N41771 N108917 N79564 N55139 N18457 N111452 N56674 N63252 N56386 N49383 N86222 N18912 N69139 N31115 N104263 N21031 N47528 N60443 N39614 N112492 N121292 N46244 N121693 N77361 N93379 N84273 N109636 N46252 N14133 N117076 N31573 N110954 N54516 N79600 N11156 N86226 N85749 N58206 N105557 N34483
6/19/2019 10:00:55 AM
6/19/2019 10:27:42 AM
135 92
U196892
N37463 N122875
13 12
6/14/2019 7:42:56 AM#TAB#6/14/2019 7:44:13 AM
N107152 N67892 N85810 N86376 N78562 N21967 N114664 N58875
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6/24/2019 10:08:51 AM
6/24/2019 10:40:02 AM
19 23 105 11 118 38 95 20
U148238
N23463 N16150 N17380 N55111 N84282 N86016 N61448 N56180 N68456 N95521 N115478 N113104 N63458
21 137 190 120 46 91 207 71 185 138 57 209 65
6/15/2019 10:18:55 AM#TAB#6/15/2019 5:14:26 PM#TAB#6/17/2019 4:22:21 AM#TAB#6/17/2019 4:25:48 AM#TAB#6/17/2019 4:27:49 AM#TAB#6/18/2019 4:34:05 AM#TAB#6/18/2019 3:50:10 PM#TAB#6/21/2019 4:22:01 AM#TAB#6/21/2019 6:36:01 AM#TAB#6/22/2019 4:16:25 AM#TAB#6/22/2019 4:19:09 AM#TAB#6/25/2019 4:49:16 AM#TAB#6/25/2019 1:14:36 PM
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6/28/2019 4:22:16 AM
6/28/2019 4:36:33 AM
45 102
U138277
N58081 N89953 N98968 N112046 N12609 N65339 N34264 N59628 N118791 N84831 N43827 N76930 N68162 N65560 N57617 N78610 N94166 N58083 N95302 N89219 N121699 N29612 N65586 N114501 N100541 N16150 N26290 N42042 N67166 N59993 N59778 N15022 N94579 N14487 N16538 N17354 N90064 N108433 N101671 N92024 N50461 N39572 N58206 N119531 N84485 N22227
22 9 21 38 53 46 87 147 23 51 70 25 68 5 12 41 10 18 13 48 30 53 12 1 38 249 2 14 38 25 38 129 16 70 12 141 20 34 8 100 16 14 5 15 18 6
6/13/2019 9:03:13 PM#TAB#6/13/2019 9:07:39 PM#TAB#6/13/2019 9:09:05 PM#TAB#6/13/2019 9:09:40 PM#TAB#6/13/2019 9:11:04 PM#TAB#6/13/2019 9:12:02 PM#TAB#6/13/2019 9:12:52 PM#TAB#6/13/2019 9:14:24 PM#TAB#6/14/2019 4:53:19 AM#TAB#6/14/2019 4:23:35 PM#TAB#6/14/2019 4:24:31 PM#TAB#6/14/2019 4:31:08 PM#TAB#6/14/2019 4:31:36 PM#TAB#6/14/2019 4:33:39 PM#TAB#6/14/2019 4:34:26 PM#TAB#6/15/2019 6:56:04 AM#TAB#6/15/2019 6:56:49 AM#TAB#6/15/2019 6:59:05 AM#TAB#6/15/2019 7:00:59 AM#TAB#6/15/2019 7:01:35 AM#TAB#6/15/2019 7:02:50 AM#TAB#6/15/2019 9:05:09 PM#TAB#6/15/2019 9:06:11 PM#TAB#6/15/2019 9:06:51 PM#TAB#6/15/2019 9:08:30 PM#TAB#6/15/2019 9:14:54 PM#TAB#6/15/2019 9:17:51 PM#TAB#6/15/2019 9:17:54 PM#TAB#6/16/2019 6:58:45 AM#TAB#6/16/2019 6:59:32 AM#TAB#6/16/2019 7:00:58 AM#TAB#6/18/2019 3:49:37 AM#TAB#6/18/2019 3:55:11 AM#TAB#6/18/2019 3:55:28 AM#TAB#6/18/2019 3:59:56 AM#TAB#6/18/2019 4:00:33 AM#TAB#6/18/2019 8:43:34 PM#TAB#6/18/2019 8:46:58 PM#TAB#6/18/2019 8:47:54 PM#TAB#6/18/2019 8:48:31 PM#TAB#6/18/2019 8:52:14 PM#TAB#6/18/2019 8:54:07 PM#TAB#6/19/2019 3:37:48 AM#TAB#6/19/2019 3:38:29 AM#TAB#6/19/2019 3:41:57 AM#TAB#6/19/2019 3:44:01 AM
N44139
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6/22/2019 3:10:49 AM
6/22/2019 3:31:33 AM
45
U15020
N121334 N89255 N79392 N70543 N11106 N38780 N44283 N12095 N66771 N99571 N104096 N19024 N95092 N100653 N39221 N88971 N69780 N85810 N114664 N88272 N66396 N70298 N30054
39 1094 45 105 157 476 112 692 29 755 86 177 286 37 14 93 1017 140 409 626 422 156 1493
6/14/2019 7:43:08 AM#TAB#6/14/2019 8:06:25 AM#TAB#6/14/2019 1:37:50 PM#TAB#6/17/2019 9:20:05 AM#TAB#6/18/2019 8:52:33 AM#TAB#6/18/2019 9:07:04 AM#TAB#6/18/2019 10:30:37 AM#TAB#6/18/2019 11:17:04 AM#TAB#6/18/2019 12:26:48 PM#TAB#6/19/2019 6:10:50 AM#TAB#6/19/2019 11:36:25 AM#TAB#6/19/2019 11:38:25 AM#TAB#6/20/2019 6:35:58 AM#TAB#6/20/2019 6:41:52 AM#TAB#6/20/2019 12:20:11 PM#TAB#6/24/2019 8:17:51 AM#TAB#6/24/2019 8:19:34 AM#TAB#6/24/2019 8:55:30 AM#TAB#6/24/2019 9:51:41 AM#TAB#6/24/2019 11:48:05 AM#TAB#6/24/2019 12:17:35 PM#TAB#6/24/2019 1:37:24 PM#TAB#6/24/2019 1:58:36 PM
N107372
N40229 N49558 N76586 N63734 N107048 N36169 N64073 N107780 N67207 N92581 N103573 N29165 N60508 N80983 N86271
6/25/2019 2:23:40 PM
6/25/2019 2:25:09 PM
78
U88198
N108393 N115025 N20132 N121044 N73152 N105712 N54014 N65866 N84809 N27595 N20524 N92415 N41392 N102774 N92626 N26948 N25990 N95521 N84470 N115478 N79890 N28753 N36169 N35271 N107372 N70403 N54725 N115515 N112035 N121939 N25286 N67475 N68462 N109804 N104861 N61309 N64008 N76912 N72751 N117248 N109523 N15608 N67376 N57686 N117497 N43484 N13367 N65174 N115132 N21481 N40182 N16349 N103594 N72885
26 252 87 54 235 150 612 42 132 2 232 39 72 45 69 74 131 278 142 177 275 124 87 33 119 204 75 79 127 59 197 62 43 21 74 160 101 1360 264 142 275 83 83 99 123 131 148 110 130 43 27 71 13 76
6/20/2019 3:36:36 PM#TAB#6/20/2019 5:00:40 PM#TAB#6/21/2019 10:21:17 AM#TAB#6/21/2019 10:22:55 AM#TAB#6/21/2019 10:24:37 AM#TAB#6/21/2019 10:29:25 AM#TAB#6/21/2019 10:31:56 AM#TAB#6/21/2019 12:17:23 PM#TAB#6/21/2019 12:20:58 PM#TAB#6/21/2019 2:12:26 PM#TAB#6/21/2019 6:42:07 PM#TAB#6/21/2019 6:46:05 PM#TAB#6/21/2019 6:48:31 PM#TAB#6/21/2019 7:21:02 PM#TAB#6/22/2019 12:51:08 PM#TAB#6/22/2019 2:57:36 PM#TAB#6/22/2019 2:58:50 PM#TAB#6/22/2019 3:01:07 PM#TAB#6/22/2019 3:06:01 PM#TAB#6/22/2019 3:10:19 PM#TAB#6/24/2019 5:41:07 PM#TAB#6/25/2019 12:06:50 PM#TAB#6/25/2019 12:09:19 PM#TAB#6/25/2019 12:10:59 PM#TAB#6/25/2019 2:06:43 PM#TAB#6/25/2019 2:10:01 PM#TAB#6/25/2019 2:14:08 PM#TAB#6/26/2019 8:38:47 AM#TAB#6/26/2019 8:40:30 AM#TAB#6/26/2019 8:44:04 AM#TAB#6/26/2019 12:35:44 PM#TAB#6/26/2019 12:39:06 PM#TAB#6/26/2019 12:40:31 PM#TAB#6/26/2019 12:43:05 PM#TAB#6/27/2019 9:54:59 AM#TAB#6/27/2019 9:56:34 AM#TAB#6/27/2019 3:50:38 PM#TAB#6/27/2019 3:56:38 PM#TAB#6/28/2019 11:18:43 AM#TAB#6/28/2019 12:00:41 PM#TAB#6/28/2019 12:03:20 PM#TAB#6/28/2019 12:11:55 PM#TAB#6/28/2019 3:52:14 PM#TAB#6/28/2019 3:54:11 PM#TAB#6/29/2019 10:26:01 AM#TAB#6/29/2019 10:29:15 AM#TAB#6/30/2019 9:22:31 AM#TAB#6/30/2019 9:25:07 AM#TAB#6/30/2019 9:27:23 AM#TAB#6/30/2019 9:29:50 AM#TAB#6/30/2019 9:30:46 AM#TAB#6/30/2019 9:31:26 AM#TAB#6/30/2019 9:35:42 AM#TAB#6/30/2019 9:36:27 AM
N23590 N97158 N11517 N22514
N88045 N108379 N12010 N97467 N18908 N53960 N65504 N53116 N64431 N43378 N12913 N53444 N33391 N121240 N16457 N101667 N31561 N57764 N30601 N75223 N79138 N99076 N43104 N92036 N64518 N21874 N67811 N31108 N119568 N89296 N36168 N68345 N122221 N73088 N37590 N52796 N123753 N10785 N80300 N118061 N47217 N82661 N52847 N48980 N75168
7/1/2019 10:15:01 AM
7/1/2019 10:29:22 AM
28 77 91 70
U61431
N109049 N47697 N15107 N11705 N85449 N53716 N120373 N14869 N39555 N75910 N50552 N99270 N51924 N121414 N65327 N90242 N65118 N95121 N60159 N72064 N110099 N15268 N107680
28 15 46 7 143 54 55 246 58 1440 2 327 82 40 7 76 80 929 230 1281 817 128 418
6/14/2019 6:46:20 AM#TAB#6/14/2019 6:47:06 AM#TAB#6/14/2019 6:48:47 AM#TAB#6/14/2019 6:53:58 AM#TAB#6/14/2019 7:37:31 AM#TAB#6/14/2019 8:32:02 AM#TAB#6/14/2019 2:46:57 PM#TAB#6/14/2019 2:47:39 PM#TAB#6/17/2019 6:53:17 AM#TAB#6/17/2019 7:06:30 AM#TAB#6/17/2019 8:15:01 AM#TAB#6/17/2019 8:15:11 AM#TAB#6/17/2019 8:21:05 AM#TAB#6/17/2019 9:03:05 AM#TAB#6/17/2019 9:08:02 AM#TAB#6/17/2019 9:10:57 AM#TAB#6/17/2019 9:18:37 AM#TAB#6/17/2019 12:06:18 PM#TAB#6/17/2019 12:11:27 PM#TAB#6/17/2019 12:12:31 PM#TAB#6/17/2019 1:14:47 PM#TAB#6/17/2019 1:24:45 PM#TAB#6/17/2019 1:27:48 PM
N108433
N121719 N90094 N11341 N51008 N79662 N86089 N122770 N31391 N11804 N112765 N103211 N42583 N101671 N104279 N59188 N105649 N88483 N62585 N96294 N100773 N61448 N15761 N69333 N10028 N12695 N18071 N104663
6/18/2019 2:23:15 PM
6/18/2019 2:30:36 PM
20
U174179
N38780 N30664 N86089 N90425 N59544 N92972 N95092 N121044 N16976 N121414 N82910 N26275 N80822 N105712 N49403 N87370 N66396 N122296 N38987 N63734 N81849 N10686 N41112 N66631 N49558 N116169 N68462 N24617 N53793 N51752 N77320 N58235 N52394
32 240 76 148 81 98 219 163 162 123 164 228 176 114 62 369 91 14 77 87 137 5 64 19 287 238 55 10 16 140 806 190 0
6/18/2019 6:58:32 AM#TAB#6/18/2019 6:59:10 AM#TAB#6/18/2019 12:54:11 PM#TAB#6/18/2019 12:55:28 PM#TAB#6/19/2019 7:03:54 AM#TAB#6/19/2019 10:18:59 AM#TAB#6/20/2019 7:24:33 AM#TAB#6/21/2019 9:22:36 AM#TAB#6/21/2019 9:25:19 AM#TAB#6/21/2019 9:28:02 AM#TAB#6/21/2019 9:36:42 AM#TAB#6/21/2019 9:39:27 AM#TAB#6/21/2019 9:43:15 AM#TAB#6/21/2019 9:46:46 AM#TAB#6/21/2019 9:48:40 AM#TAB#6/21/2019 9:49:43 AM#TAB#6/24/2019 8:09:52 AM#TAB#6/24/2019 8:11:23 AM#TAB#6/25/2019 7:31:24 AM#TAB#6/25/2019 11:57:31 AM#TAB#6/25/2019 11:58:58 AM#TAB#6/25/2019 12:01:16 PM#TAB#6/25/2019 12:01:22 PM#TAB#6/25/2019 12:02:26 PM#TAB#6/25/2019 12:04:30 PM#TAB#6/26/2019 7:11:45 AM#TAB#6/26/2019 7:15:50 AM#TAB#6/26/2019 7:22:01 AM#TAB#6/27/2019 6:00:42 AM#TAB#6/27/2019 6:01:27 AM#TAB#6/27/2019 6:04:03 AM#TAB#6/27/2019 7:23:46 AM#TAB#6/27/2019 9:21:23 AM
N79115
N37853 N68344 N99978 N119481 N86042 N13223 N114030 N54268 N107638 N36828 N89649 N67404 N46272 N49046 N93339 N71947 N98455 N101672 N80304 N49284
7/2/2019 7:43:35 AM
7/2/2019 8:24:12 AM
546
U397787
N102938 N44139 N110993 N84898 N50892 N42970 N120709 N29737 N65329 N105956
49 30 67 4 525 12 80 145 853 50
6/18/2019 7:17:40 PM#TAB#6/22/2019 8:53:16 AM#TAB#6/22/2019 8:54:15 AM#TAB#6/22/2019 8:58:21 AM#TAB#6/22/2019 8:58:24 AM#TAB#6/22/2019 9:08:08 AM#TAB#6/22/2019 9:08:11 AM#TAB#6/23/2019 12:13:30 PM#TAB#6/23/2019 2:07:54 PM#TAB#6/23/2019 3:54:44 PM
N59937 N11182
N24820 N83589 N72885 N58210 N103594 N67842 N48360 N65174 N76977 N18417 N63867 N67971 N76163 N55633 N53116 N24424 N96733 N41216 N21021 N28601
6/30/2019 2:18:19 PM
6/30/2019 2:22:03 PM
62 41
U257982
N50928 N70471 N93998 N35054
602 44 31 18
6/14/2019 8:06:38 AM#TAB#6/18/2019 7:19:24 AM#TAB#6/25/2019 8:08:56 AM#TAB#6/25/2019 8:09:01 AM
N53784
N119254 N18926 N55263 N13688 N105845 N116744 N96955 N102082 N104861 N47094 N122639 N47127 N45571 N122242 N35242 N60588 N54692 N22109 N33322 N84362
6/27/2019 11:12:40 AM
6/27/2019 11:44:38 AM
122
U210765
N95202 N121334 N58113 N84162 N90558 N87652 N17024 N85220 N20045 N112008 N90242 N41168 N65974 N14961 N90215 N62632 N68652 N78202 N94044 N120253 N65118 N92115 N48113 N112765 N103099 N42583 N21782 N104096 N62402 N14466 N48341 N87223 N101051 N111241 N51515
80 92 612 256 116 48 83 53 96 103 29 72 50 19 322 75 45 74 50 51 33 70 66 231 5 130 80 59 94 69 71 34 59 129 177
6/14/2019 12:09:45 PM#TAB#6/14/2019 12:11:13 PM#TAB#6/14/2019 12:12:46 PM#TAB#6/14/2019 1:33:06 PM#TAB#6/14/2019 1:52:54 PM#TAB#6/14/2019 1:55:29 PM#TAB#6/14/2019 1:57:59 PM#TAB#6/14/2019 1:59:31 PM#TAB#6/14/2019 2:00:25 PM#TAB#6/14/2019 2:00:44 PM#TAB#6/17/2019 7:12:32 AM#TAB#6/17/2019 7:13:22 AM#TAB#6/17/2019 7:14:48 AM#TAB#6/17/2019 11:40:23 AM#TAB#6/17/2019 11:40:50 AM#TAB#6/17/2019 11:46:55 AM#TAB#6/17/2019 11:48:57 AM#TAB#6/17/2019 12:29:13 PM#TAB#6/18/2019 12:34:10 PM#TAB#6/18/2019 12:35:30 PM#TAB#6/18/2019 12:36:36 PM#TAB#6/18/2019 12:52:30 PM#TAB#6/18/2019 1:11:07 PM#TAB#6/18/2019 1:12:00 PM#TAB#6/18/2019 1:17:33 PM#TAB#6/18/2019 1:51:08 PM#TAB#6/19/2019 12:35:05 PM#TAB#6/19/2019 12:36:32 PM#TAB#6/19/2019 12:37:51 PM#TAB#6/19/2019 12:39:37 PM#TAB#6/19/2019 12:43:14 PM#TAB#6/19/2019 12:44:39 PM#TAB#6/19/2019 12:45:27 PM#TAB#6/19/2019 12:49:58 PM#TAB#6/19/2019 1:47:11 PM
N80819
N81145 N108781 N78798 N74429 N68456 N89085 N114715 N109436 N18319 N56128 N88313 N101623 N20113 N28953
6/21/2019 6:47:20 AM
6/21/2019 6:48:33 AM
44
U344051
N43178 N55055 N74186
6 89 22
6/15/2019 5:06:19 PM#TAB#6/15/2019 5:07:15 PM#TAB#6/15/2019 5:18:37 PM
N67437
N74784 N66052 N65076 N68856 N17039 N111024 N108946 N73098 N29771 N67259 N97167 N34856 N117525 N27401
6/20/2019 3:36:48 PM
6/20/2019 3:47:43 PM
102
U124839
N105994 N80996 N87523 N17355 N84319 N41288 N121334 N58811 N91273 N62749 N95202 N100509 N43119 N36905 N94432 N26659
124 57 64 54 76 16 86 20 13 438 119 102 36 56 9 983
6/14/2019 4:50:36 AM#TAB#6/14/2019 4:53:45 AM#TAB#6/14/2019 8:53:15 AM#TAB#6/14/2019 8:56:21 AM#TAB#6/14/2019 8:58:01 AM#TAB#6/14/2019 9:00:50 AM#TAB#6/14/2019 9:01:46 AM#TAB#6/14/2019 9:03:30 AM#TAB#6/14/2019 9:12:58 AM#TAB#6/14/2019 9:21:24 AM#TAB#6/14/2019 11:56:36 AM#TAB#6/14/2019 11:58:54 AM#TAB#6/14/2019 12:01:18 PM#TAB#6/14/2019 12:02:21 PM#TAB#6/14/2019 12:05:09 PM#TAB#6/14/2019 12:05:26 PM
N85531
N30687 N51909 N94579 N21590 N57729 N39572 N79595 N41447 N13696 N90376 N93306 N94053 N107590 N12607 N44283 N12470 N25898 N110213 N119877 N102515 N11462 N106552 N37568 N51100 N87223 N20802 N79960 N13725 N23681 N68307 N98363 N32910 N66045 N95501 N17723 N75100 N35080 N92065 N13555 N28113 N104663
6/18/2019 5:03:17 AM
6/18/2019 5:19:19 AM
35
U103175
N38122 N116802 N85471 N94241 N108781 N17686
59 147 91 243 265 1143
6/18/2019 12:38:02 PM#TAB#6/19/2019 2:42:25 PM#TAB#6/19/2019 2:44:52 PM#TAB#6/20/2019 1:39:15 PM#TAB#6/21/2019 10:48:49 AM#TAB#6/24/2019 1:11:29 PM
N87543
N121554 N49680 N40229 N47332 N50061 N49693 N80140 N111084 N98027 N24324 N67814 N84918 N28753 N116670 N76586 N63734 N107048 N101112 N36169 N67207 N92581 N62716 N44266 N60508 N33474 N54725 N122431 N105381
6/25/2019 1:22:57 PM
6/25/2019 1:30:31 PM
180
U299014
N37463 N76869 N47697 N103854 N121699 N32113 N93382 N14376 N78688 N121200 N63789 N123552 N54647 N75009 N89847 N104663 N53158 N97051 N39572 N18385 N59169 N75583 N90147 N79787 N89796 N115110 N41399 N43455 N70298 N50599 N11431 N42359 N111857 N42107
28 15 45 115 177 41 28 45 44 80 4 42 153 137 5 43 145 4 31 267 204 35 80 118 75 124 122 331 170 3 45 82 109 37
6/13/2019 6:09:22 PM#TAB#6/13/2019 6:09:28 PM#TAB#6/14/2019 6:37:43 AM#TAB#6/14/2019 1:21:16 PM#TAB#6/15/2019 6:55:23 AM#TAB#6/15/2019 7:04:52 AM#TAB#6/15/2019 7:08:14 AM#TAB#6/15/2019 7:14:52 AM#TAB#6/15/2019 7:37:43 AM#TAB#6/16/2019 7:06:03 AM#TAB#6/16/2019 7:13:29 AM#TAB#6/16/2019 7:13:33 AM#TAB#6/18/2019 5:52:56 AM#TAB#6/18/2019 6:07:32 AM#TAB#6/18/2019 1:35:09 PM#TAB#6/18/2019 1:35:15 PM#TAB#6/18/2019 1:36:27 PM#TAB#6/18/2019 1:44:03 PM#TAB#6/18/2019 1:44:08 PM#TAB#6/20/2019 6:08:59 AM#TAB#6/21/2019 12:21:42 PM#TAB#6/21/2019 1:36:43 PM#TAB#6/22/2019 6:16:06 PM#TAB#6/23/2019 4:57:30 AM#TAB#6/23/2019 8:56:16 AM#TAB#6/24/2019 6:15:02 AM#TAB#6/24/2019 11:15:57 AM#TAB#6/24/2019 11:18:19 AM#TAB#6/24/2019 11:24:02 AM#TAB#6/24/2019 11:34:15 AM#TAB#6/24/2019 11:34:18 AM#TAB#6/24/2019 4:09:32 PM#TAB#6/25/2019 6:50:12 AM#TAB#6/26/2019 7:10:16 AM
N105772 N100589
N120447 N24244 N105855 N79041 N86318 N75652 N14882 N77334 N24335 N55895 N62314 N115498 N14678 N81496 N120909 N107573 N51773 N55076 N43695 N63481 N50689 N121294 N94628 N50435 N87898 N96567 N56474 N54940
6/28/2019 1:17:05 PM
6/28/2019 1:22:07 PM
95 106
U350237
N18363 N43119 N122382 N79391 N86113 N63636 N44108 N118320 N112032 N81726 N85531 N94044 N100679 N105649 N120253 N95251 N78424 N82032 N99572 N27093 N89400 N42592 N48858 N101051 N61629 N69542 N68999 N83241 N75943 N104003 N110194 N23948 N103617 N26402 N110319
103 30 11 932 29 23 10 27 655 26 9 67 1517 24 131 107 617 27 151 12 260 21 2378 22 46 240 357 249 17 135 186 698 31 1091 561
6/14/2019 6:13:32 AM#TAB#6/14/2019 9:38:16 AM#TAB#6/17/2019 6:15:53 AM#TAB#6/17/2019 6:28:44 AM#TAB#6/17/2019 6:44:47 AM#TAB#6/17/2019 6:45:53 AM#TAB#6/17/2019 9:40:52 AM#TAB#6/17/2019 10:11:30 AM#TAB#6/17/2019 11:39:21 AM#TAB#6/17/2019 11:52:24 AM#TAB#6/18/2019 5:16:12 AM#TAB#6/18/2019 7:00:15 AM#TAB#6/18/2019 7:34:07 AM#TAB#6/18/2019 12:30:07 PM#TAB#6/18/2019 1:09:49 PM#TAB#6/19/2019 7:20:04 AM#TAB#6/19/2019 7:43:05 AM#TAB#6/19/2019 10:51:12 AM#TAB#6/19/2019 11:10:52 AM#TAB#6/19/2019 11:51:45 AM#TAB#6/19/2019 12:15:35 PM#TAB#6/19/2019 12:20:08 PM#TAB#6/19/2019 12:26:58 PM#TAB#6/19/2019 3:01:57 PM#TAB#6/20/2019 5:08:35 AM#TAB#6/20/2019 7:18:12 AM#TAB#6/20/2019 7:22:27 AM#TAB#6/20/2019 8:22:39 AM#TAB#6/20/2019 2:23:14 PM#TAB#6/21/2019 5:53:55 AM#TAB#6/21/2019 8:53:25 AM#TAB#6/21/2019 8:57:17 AM#TAB#6/24/2019 5:28:53 AM#TAB#6/24/2019 5:29:25 AM#TAB#6/24/2019 5:47:58 AM
N93998
N63458 N68125 N49680 N116989 N47590 N81302 N95546 N71031 N69937 N74276 N25251 N24100 N78447 N52760 N122091 N111857 N80140 N111084 N79855 N95738 N84101 N119720 N74036 N86656 N68188 N38774 N26047 N48809 N93164 N72237 N107372 N24324 N102626 N112738 N87272 N36468 N38862 N116770 N52378 N51376 N107357 N69103 N91931 N107048 N72238 N83507
6/25/2019 7:01:20 AM
6/25/2019 8:48:34 AM
831
U214322
N89255 N95202
1328 436
6/14/2019 6:51:52 AM#TAB#6/14/2019 10:24:20 AM
N119833
N61154 N15124 N114938 N115495 N65974 N51924 N78202 N59685 N14961 N81783 N42055 N109041 N17790 N103844 N50223 N118951 N25370 N88470 N65118 N75333 N81726 N90215 N118320 N87417 N47696 N115534 N98288
6/17/2019 11:34:29 AM
6/17/2019 11:41:29 AM
261
U290629
N72931 N78965 N90242 N79842 N42055 N75910 N63119 N26275 N88841 N103887 N13203 N104304 N67688 N36730 N68298 N79391 N62746 N93019 N57235 N90825 N25370 N70426 N116839 N115311 N44987 N29409 N89663 N105905 N13879 N36897 N34916 N14452 N65974 N37252 N81726 N84050 N102498 N104892 N62632 N10083 N115495 N31693 N65623 N54627 N38318 N102284 N85806 N109222 N90215 N38517 N88490 N11637 N100569 N108135 N36166 N13696 N119877 N56372 N30664 N44283 N40222 N26279 N94400 N18402 N31687 N80907 N17723 N62593 N70471 N46755 N53943 N48582 N56855 N90425 N95941 N63185 N92024 N23681 N49054 N35069 N90431 N101671 N88483 N123665 N51008 N88434 N59544 N60476 N56109 N19160 N95829 N88929 N104096 N109636 N55139 N57109 N95120 N95772 N42583 N10856 N52234 N120253 N90094 N43420 N115129 N92972 N67163 N56386 N98456 N106098 N95007 N79600 N101051 N80645 N48341 N18457 N44759 N74743 N105611 N20031 N31778 N47528 N83046 N77212 N97851 N22108 N39306 N47779 N100653 N91454 N29105 N56664 N31487 N73098 N84255 N14480 N41179 N53158 N96234 N61223 N50023 N75943 N44812 N19230 N65076 N81145 N37742 N72126 N114715 N84809 N105712 N80819 N18319 N108781 N28149 N33346 N82663 N46170 N62799 N16262 N25043 N86648 N11595 N70031 N86048 N22083 N30996 N61495 N123294 N105279 N79757 N77463 N72108 N61808 N71569 N51557 N17361 N72057 N51832 N71207 N41397 N13892 N88971 N77291 N90169 N88888 N30442 N71915 N70388 N20440 N24548 N121202 N20329 N56158 N16357 N72375 N75503 N111506 N17979 N11209 N88272 N66396 N90517 N53291 N43384 N93719 N68265 N70298 N38133 N114664 N46197 N17686 N30054 N100613 N43455 N63041 N76208 N97870 N64326 N41621 N42449 N58061 N115883 N57305 N112727 N48002 N76598 N55766 N21685 N100777 N78899 N59938 N108973 N14080 N28235 N78447 N69103 N49558 N88231 N35210 N75354 N18133 N58447 N111084 N52760 N119787 N76217 N26774 N76253 N111918 N41158 N38420 N44063 N11139 N116731 N107890 N66946 N107048 N22924 N76586 N70300 N116670 N38720 N86664 N87272 N47735 N63734 N90005 N50061 N69094 N117416 N101112 N93635 N55003 N104446 N115929 N12827 N31968 N86549 N110656 N115163 N25904 N76179 N60508 N18710 N67814 N38356 N23572 N42107 N50439 N21504 N31018 N116036 N66423 N85960 N44348 N27612 N70871 N109804 N64266 N23441 N62755 N18804 N19831 N34982 N20389
61 18 59 1239 188 209 194 345 416 155 60 236 154 192 101 685 222 14 297 69 385 221 217 235 119 656 173 325 556 1276 0 82 97 233 269 64 72 345 125 21 221 466 104 344 145 683 63 219 447 378 240 73 115 4 31 330 719 165 114 253 29 237 6 186 7 116 159 547 98 203 187 385 315 501 115 34 191 195 1022 167 25 14 133 256 16 403 111 228 268 138 136 103 100 265 302 435 187 8 688 116 5 73 29 62 197 196 66 10 71 103 111 272 136 22 111 129 242 42 140 25 1779 1041 9 118 1385 223 156 683 260 661 179 168 162 36 27 5 159 1698 858 1475 86 1060 680 1083 967 81 9 39 202 49 1420 227 209 407 297 69 239 450 53 415 109 480 78 178 152 347 104 68 0 53 391 200 841 39 40 130 1089 68 283 38 377 176 123 382 158 32 87 155 82 89 5 37 186 169 69 744 148 9 21 31 285 842 116 126 191 46 153 210 75 82 111 65 112 169 52 182 161 23 134 215 169 38 103 96 72 66 107 152 88 15 72 46 5 143 270 175 177 368 214 185 99 184 110 11 82 14 543 163 30 8 11 6 448 34 389 913 91 460 16 346 194 235 960 120 198 291 646 450 625 241 256 148 49 66 69 32 18 371 42 63 100 974 283 391 71 178 13 98 54 210 72 66 164 101 7 294 72 7 210 324 11 135 170 51 31 9
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Dataset Card for PENS: PErsonalized News headlineS

PENS is an English dataset for Personalized News Headline Generation Research. It contains two parts for training and test individually. The training set was collected from anonymized user impressions logs of Microsoft News website, and the test set is manually-created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode.

PENS contains about 113k English news articles whose topics are distributed into 15 categories and 500k impression logs generated by over 445k users for training. In detail, every news article contains rich textual content including title, body, category and corresponding entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To provide an offline testbed, we invited 103 English native speakers to manually create a test set by two stages. In detail, there are over 100k personalized news headlines generated.

Notice that each user was de-linked from the production system when securely hashed into an anonymized ID to protect user privacy. For more detailed information about the PENS dataset, you can refer to the following paper: PENS: A Dataset and Generic Framework for Personalized News Headline Generation.

Dataset Details

Dataset Description

PENS (PErsonalized News headlineS) is an English dataset tailored for Personalized News Headline Generation research. The dataset is divided into training and test sets to support both model development and evaluation.

  • Training Set: Collected from anonymized user impression logs of the Microsoft News website, containing approximately 113k English news articles across 15 categories and 500k impression logs from over 445k users. Each news article includes a title, body, category, and associated entities. Impression logs detail click events, non-click events, and historical news click behaviors of users prior to each impression.

  • Test Set: Manually created by 103 native English speakers to provide a fair and reliable benchmark for offline model evaluation. This set includes over 100k personalized news headlines generated through a two-stage annotation process.

PENS ensures user privacy by de-linking user data from the production system and anonymizing user IDs through secure hashing.

The statistics of news corpus and training set
of the PENS dataset.

Dataset Sources [optional]

Dataset Structure

PENS consists of two main components: the training set and the test set.

  • Training Set:

    • News Articles: ~113,762 entries
      • Fields:
        • News ID: Unique identifier for each news article (e.g., N10000).
        • Category: One of 15 predefined categories (e.g., sports).
        • Topic: Specific topic within the category (e.g., soccer).
        • Headline: The title of the news article.
        • News body: The full content of the news article.
        • Title entity: JSON mapping between phrases in the title and their corresponding entities in WikiData.
        • Entity content: Detailed information about each entity from WikiData.
    • Impression Logs: 500,000 entries
      • Fields:
        • UserID: Anonymous identifier for the user (e.g., U335175).
        • ClicknewsID: Space-separated list of historically clicked news IDs.
        • Dwelltime: Space-separated list of browsing durations for each clicked news.
        • Exposure_time: Space-separated list of exposure times for historical clicked news (separated by #TAB#).
        • Pos: Space-separated list of clicked news IDs in the current impression.
        • Neg: Space-separated list of unclicked news IDs in the current impression.
        • Start: Start time of the impression (e.g., 7/3/2019 6:43:49 AM).
        • End: End time of the impression (e.g., 7/3/2019 7:06:06 AM).
        • Dwelltime_pos: Space-separated list of browsing durations for clicked news in the current impression.
  • Validation Set:

    • Structured similarly to the training set, used for validating model performance during training.
  • Test Set:

    • Personalized Headlines: >100,000 entries
      • Fields:
        • userid: Unique identifier for each of the 103 test users (e.g., NT1).
        • clicknewsID: Comma-separated list of historically clicked news IDs from the first stage.
        • posnewID: Comma-separated list of exhibited news IDs in the second stage.
        • rewrite_titles: Space-separated list of manually written personalized headlines (separated by #TAB#).

The dataset is structured to facilitate the mapping between user behaviors and personalized headline generation.

Uses

Direct Use

PENS is intended for research in personalized news headline generation. Suitable use cases include:

  • Developing models that generate personalized news headlines based on user interaction data.
  • Evaluating the effectiveness of personalization algorithms in news dissemination.
  • Analyzing user behavior and preferences in the context of news consumption.

Out-of-Scope Use

PENS is not suitable for:

  • Commercial use without explicit permission from Microsoft Research.
  • Applications requiring real-time data updates, as the dataset is static.
  • Tasks unrelated to text generation or summarization, such as image processing.

Dataset Structure

PENS consists of two main components: the training set and the test set.

  • Training Set:

    • News Articles: ~113k entries
      • Fields: title, body, category, entities
    • Impression Logs: ~500k entries
      • Fields: click_events, non_click_events, historical_click_behaviors
  • Test Set:

    • Personalized Headlines: >100k entries
      • Fields: original_article, personalized_headline, user_id

The dataset is structured to facilitate the mapping between user behaviors and personalized headline generation.

Dataset Creation

Curation Rationale

The PENS dataset was created to address the need for comprehensive resources in the domain of personalized news headline generation. By leveraging real user interaction data and manually curated headlines, PENS provides a robust foundation for developing models that can tailor news content to individual preferences.

Source Data

Data Collection and Processing

  • Training Data: Extracted from anonymized user impression logs of the Microsoft News website. The data includes detailed user interactions with news articles, ensuring a rich source of information for personalization.

  • Test Data: Generated through a two-stage manual annotation process involving 103 native English speakers. This ensures high-quality, personalized headlines for reliable evaluation.

Who are the source data producers?

  • Training Data Producers: Users of the Microsoft News website whose interactions were anonymized and aggregated.

  • Test Data Producers: 103 native English speakers recruited to generate personalized headlines.

Annotations [optional]

Annotation process

The test set annotations were created in two stages:

  1. Initial Generation: Annotators generated personalized headlines based on provided news articles and user interaction histories.
  2. Validation: A secondary review ensured consistency and quality of the generated headlines.

Who are the annotators?

The annotators are 103 native English speakers with expertise in journalism and content creation, ensuring that the personalized headlines are both accurate and engaging.

Personal and Sensitive Information

PENS contains anonymized user interaction data. User IDs are securely hashed to prevent re-identification, and no personal, sensitive, or private information is included in the dataset.

Bias, Risks, and Limitations

PENS may contain inherent biases present in user interaction data, such as demographic or preference-based biases. Additionally, the dataset is limited to English-language news and may not generalize to other languages or cultural contexts.

Recommendations

Users should critically assess the dataset for potential biases and ensure that models trained on PENS are evaluated for fairness and representativeness. It's recommended to complement PENS with additional data sources to mitigate identified biases.

Citation

BibTeX:

@inproceedings{pens2024,
  title={PENS: PErsonalized News headlineS for Personalized News Headline Generation},
  author={Author Names},
  booktitle={Proceedings of the Conference on Natural Language Processing},
  year={2024},
  organization={Microsoft Research}
}

APA: Ao, X., Wang, X., Luo, L., Qiao, Y., He, Q., & Xie, X. (2021, August). PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 82-92).

Glossary

  • Impression Log: A record of a user's interaction with news articles, including clicks and non-clicks.
  • Personalized Headline: A news headline tailored to an individual user's preferences and interaction history.

More Information

Put Your Voice on Stage: Personalized Headline Generation for News Articles

Fact-Preserved Personalized News Headline Generation

Dataset Card Contact

For any inquiries or feedback regarding the PENS dataset, please contact [email protected].

Download

The PENS dataset is available for free download for research purposes under the Microsoft Research License Terms. Please ensure you have read and agree to the license terms before downloading.

License

The PENS dataset is distributed under the Microsoft Research License Terms. Please review the license agreement before using the dataset.

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