Search for: All records

Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics. 

Dual-plane stereoscopic particle image velocimetry (PIV) is capable of quantifying the flow field in terms of three-component (3C) flow vectors and 3C vorticity vectors simultaneously. Here, we present a test rig to carry out the 20 kHz dual-plane stereo PIV measurements on a premixed swirling flame by using a two-legged burst-mode laser. Other than the traditional methods employing the laser polarization direction and the two-color separation methods, two same-color laser sheets with a 100 ns delay were adopted to separate the imaging processes for the two pairs of cameras using the image straddling method. Each laser sheet with the same wavelength of 532 nm has a pulse cyclic frequency of 20 kHz within each burst generated by the high-repetition-rate burst-mode laser. 3C velocity vectors of a swirling flame were obtained based on the sequential particle images for each laser sheet. In spite of non-perfect simultaneous flow measurements on the two spatially separated laser sheets, the velocity error caused by the 100 ns delay on top of a 50 μs duration, which was used for the velocity vector calculation, is negligible. This short-delay separation method significantly simplifies the experimental setup for dual-plane stereo PIV measurements, especially for low-speed flows.

 

Amazonian peatlands store a large amount of soil organic carbon (SOC), and its fate under a future changing climate is unknown. Here, we use a process-based peatland biogeochemistry model to quantify the carbon accumulation for peatland and nonpeatland ecosystems in the Pastaza-Marañon foreland basin (PMFB) in the Peruvian Amazon from 12,000 y before present to AD 2100. Model simulations indicate that warming accelerates peat SOC loss, while increasing precipitation accelerates peat SOC accumulation at millennial time scales. The uncertain parameters and spatial variation of climate are significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, SOC accumulation rate in the PMFB slows down to 7.9 (4.3–12.2) g⋅C⋅m⁻²⋅y⁻¹from the current rate of 16.1 (9.1–23.7) g⋅C⋅m⁻²⋅y⁻¹, and the region may turn into a carbon source to the atmosphere at −53.3 (−66.8 to −41.2) g⋅C⋅m⁻²⋅y⁻¹(negative indicates source), depending on the level of warming. Peatland ecosystems show a higher vulnerability than nonpeatland ecosystems, as indicated by the ratio of their soil carbon density changes (ranging from 3.9 to 5.8). This is primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with nonpeatland ecosystems under future climate conditions. Peatland and nonpeatland soils in the PMFB may lose up to 0.4 (0.32–0.52) Pg⋅C by AD 2100 with the largest loss from palm swamp. The carbon-dense Amazonian peatland may switch from a current carbon sink into a source in the 21st century.