244 research outputs found
The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023
This paper is the system description of the DKU-MSXF System for the track1,
track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023
(VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for
training. By constructing a cross-age QMF training set, we achieve a
substantial improvement in system performance. For Track 2, we inherite the
pre-trained model from Track 1 and conducte mixed training by incorporating the
VoxBlink-clean dataset. In comparison to Track 1, the models incorporating
VoxBlink-clean data exhibit a performance improvement by more than 10%
relatively. For Track3, the semi-supervised domain adaptation task, a novel
pseudo-labeling method based on triple thresholds and sub-center purification
is adopted to make domain adaptation. The final submission achieves mDCF of
0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.Comment: arXiv admin note: text overlap with arXiv:2210.0509
Absence of remote earthquake triggering within the Coso and Salton Sea geothermal production fields
Geothermal areas are long recognized to be susceptible to remote earthquake triggering, probably due to the high seismicity rates and presence of geothermal fluids. However, anthropogenic injection and extraction activity may alter the stress state and fluid flow within the geothermal fields. Here we examine the remote triggering phenomena in the Coso geothermal field and its surrounding areas to assess possible anthropogenic effects. We find that triggered earthquakes are absent within the geothermal field but occur in the surrounding areas. Similar observation is also found in the Salton Sea geothermal field. We hypothesize that continuous geothermal operation has eliminated any significant differential pore pressure between fractures inside the geothermal field through flushing geothermal precipitations and sediments out of clogged fractures. To test this hypothesis, we analyze the pore-pressure-driven earthquake swarms, and they are found to occur outside or on the periphery of the geothermal production field. Therefore, our results suggest that the geothermal operation has changed the subsurface fracture network, and differential pore pressure is the primary controlling factor of remote triggering in geothermal fields
The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023
This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb
Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains
voice activity detection, clustering-based diarization, overlapped speech
detection, and target-speaker voice activity detection, where each procedure
has a fused output from 3 sub-models. Finally, we fuse different
clustering-based and TSVAD-based diarization systems using DOVER-Lap and
achieve the 4.30% diarization error rate (DER), which ranks first place on
track 4 of the challenge leaderboard
Retrosynthetic Planning with Dual Value Networks
Retrosynthesis, which aims to find a route to synthesize a target molecule
from commercially available starting materials, is a critical task in drug
discovery and materials design. Recently, the combination of ML-based
single-step reaction predictors with multi-step planners has led to promising
results. However, the single-step predictors are mostly trained offline to
optimize the single-step accuracy, without considering complete routes. Here,
we leverage reinforcement learning (RL) to improve the single-step predictor,
by using a tree-shaped MDP to optimize complete routes. Specifically, we
propose a novel online training algorithm, called Planning with Dual Value
Networks (PDVN), which alternates between the planning phase and updating
phase. In PDVN, we construct two separate value networks to predict the
synthesizability and cost of molecules, respectively. To maintain the
single-step accuracy, we design a two-branch network structure for the
single-step predictor. On the widely-used USPTO dataset, our PDVN algorithm
improves the search success rate of existing multi-step planners (e.g.,
increasing the success rate from 85.79% to 98.95% for Retro*, and reducing the
number of model calls by half while solving 99.47% molecules for RetroGraph).
Additionally, PDVN helps find shorter synthesis routes (e.g., reducing the
average route length from 5.76 to 4.83 for Retro*, and from 5.63 to 4.78 for
RetroGraph).Comment: Accepted to ICML 202
Theoretical analysis of diffraction grating based on 45°-tilted fiber gratings
We have theoretically analyzed the diffractive characteristics of radiation mode of 45° tilted fiber gratings. The simulated angular dispersion for the 45 ° TFG with 748nm period at 1550nm is around 0.053°/nm, which is quite close to the experimental result
- …