High precision physics at future colliders as the International Linear Collider (ILC) require unprecedented high precision in the determination of the energy of final state particles. The needed precision will be achieved thanks to the Particle Flow algorithms (PF) which require highly granular and hermetic calorimeters systems. The physical proof of concept of the PF was performed in the previous campaign of beam tests of physic prototypes within the CALICE collaboration. One of these prototypes was the physics prototype of the Silicon-Tungsten Electromagnetic Calorimeter (SiW-ECAL) for the ILC. In this document we present the latest news on R&D of the next generation prototype, the technological prototype with fully embedded very front-end (VFE) electronics, of the SiW-ECAL. Special emphasis is given to the presentation. 250 GeV - 1 TeV. ILC requires unprecedented precision in the energy determination of final states. These techniques rely on single particle separation in the full detector volume to choose the best information available to measure the energy of the final state objects (i.e. measuring the charged particles momentum at tracking devices better than in the calorimeters). Therefore, PF algorithms require highly granular and compact calorimeter systems featuring minimum dead material (high hermeticity). We evaluate the proposed model in cross-scenario temporal grounding, where the train / test data are heterogeneously sourced. Experiments show large-margin superiority of the proposed method in comparison with state-of-the-art competitors. Given a sentence query and an untrimmed video, the goal of temporal grounding Anne Hendricks et al. 2017); Gao et al. 2017) is to localize video moment described by the sentence query. In recent years, a list of promising models Wang et al. 2020); Ghosh et al. 2019); Rodriguez et al. 2020); Wang et al. Hendricks et al. (2018); Zhang et al. Stroud et al. (2019); Yuan et al. 2019); Zhang et al. 2020) have been designed to tackle this task. Despite remarkable research progress, we empirically find that these models are heavily affected by some superficial bias of the data, leading to inferior generalization performance on cross-scenario testing data. In one of our pilot experiments, we take some well-trained state-of-the-art temporal grounding model and zero the feature vector of all testing queries.
bbc one minute world news
4734969 6251358
4757167 8229834
4472526 3333249
4703000 3491251
2644965 4578117
8071395 2176973
5896348 3032512
6701027 4020293
3116400 1749384
6411902 8395544
30780 1135075
5610643 6708469
8024960 7436534
9604694 3347874
5793378 5343845
5477551 9423505
4893070 165112
4856832 6009269
2819564 7555989
4435637 6096187
9678106 1271459
6575114 6996978
7059696 1888276
4842562 7401655
197760 2000654
1821350 5812151
7016618 5827071
9041948 9571464
163673 3716890
1380916 6211447
597039 4511870
752168 1326407
3857771 1245171
6465662 9703851
2591166 8131263
2071731 8930103
1422428 9333214
1038992 7172694
2782831 2455781
6123483 9382235
4775551 2248004
9758637 8618450
4675652 2484271
5313185 1719221
4158378 6480338
1561594 1034503
4850801 9226048
5214694 6447874
5299319 6595128
8850096 4397024
2946362 9603245
4083951 3110930
7209389 4189697
2329742 5793379
2262729 9474491
7994023 5962232
600357 9984499
1446442 1663538
994054 9789903
1600070 7781571
6923371 9041829
468848 8170006
4082396 5868508
4519797 1805840
8044747 9100295
184056 9699646
5700135 9798102
3352725 1785400
6532234 2043941
5222116 5479591
2353989 5967532
9310512 172317
6575106 8487144
7007150 4323922
6613953 9712521
1484969 4424926
5528069 5497480
2329504 7506663
1473841 9099487
5726019 1445376
6077879 7426591
7543389 9757766
5324535 9822891
9162912 133492
9037108 9881255
site
site
site
site
site
site
site
site
site
site
site
site
