Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Lo, Eric"'
Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challengin
Externí odkaz:
http://arxiv.org/abs/2405.14502
Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature. However, meeting this requirement becomes particularly challenging when certain input fea
Externí odkaz:
http://arxiv.org/abs/2405.11191
Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of re
Externí odkaz:
http://arxiv.org/abs/2305.18729
Two-phase-commit (2PC) has been widely adopted for distributed transaction processing, but it also jeopardizes throughput by introducing two rounds of network communications and two durable log writes to a transaction's critical path. Despite the var
Externí odkaz:
http://arxiv.org/abs/2302.12517
Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and determ
Externí odkaz:
http://arxiv.org/abs/2211.15163
The performance of main memory column stores highly depends on the scan and lookup operations on the base column layouts. Existing column-stores adopt a homogeneous column layout, leading to sub-optimal performance on real workloads since different c
Externí odkaz:
http://arxiv.org/abs/2209.00220
Publikováno v:
PVLDB, 15(11): 3004 - 3017, 2022
Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against th
Externí odkaz:
http://arxiv.org/abs/2207.02900
Deep neural networks perform poorly on heavily class-imbalanced datasets. Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition. Based on the mathematical a
Externí odkaz:
http://arxiv.org/abs/2203.11506
Autor:
Kheshvadjian, Michael, Nazmifar, Michael, Rawal, Rushil, Davood, Joshua, Castaneda, Peris, Dadashian, Eman, Dallmer, Jeremiah, Heard, John, Masterson, John, Lo, Eric, Taich, Lior, Naser-Tavakolian, Aurash, Kokorowski, Paul, Ahdoot, Michael
Publikováno v:
In Urology September 2024 191:185-192
Autor:
Lo, Eric Siu Chung, Wong, Angel Kit Yi, Tang, Sylvia Yee Fan, Li, Dora Dong Yu, Cheng, May May Hung
Publikováno v:
In Teaching and Teacher Education August 2024 146