774 research outputs found
Deep Multimodal Speaker Naming
Automatic speaker naming is the problem of localizing as well as identifying
each speaking character in a TV/movie/live show video. This is a challenging
problem mainly attributes to its multimodal nature, namely face cue alone is
insufficient to achieve good performance. Previous multimodal approaches to
this problem usually process the data of different modalities individually and
merge them using handcrafted heuristics. Such approaches work well for simple
scenes, but fail to achieve high performance for speakers with large appearance
variations. In this paper, we propose a novel convolutional neural networks
(CNN) based learning framework to automatically learn the fusion function of
both face and audio cues. We show that without using face tracking, facial
landmark localization or subtitle/transcript, our system with robust multimodal
feature extraction is able to achieve state-of-the-art speaker naming
performance evaluated on two diverse TV series. The dataset and implementation
of our algorithm are publicly available online
Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios
Species richness is the core of biodiversity-ecosystem functioning (BEF) research. Nevertheless, it is difficult to accurately predict changes in plant species richness under different climate scenarios, especially in alpine biomes. In this study, we surveyed plant species richness from 2009 to 2017 in 75 alpine meadows (AM), 199 alpine steppes (AS), and 71 desert steppes (DS) in the Tibetan Autonomous Region, China. Along with 20 environmental factors relevant to species settlement, development, and survival, we first simulated the spatial pattern of plant species richness under current climate conditions using random forest modelling. Our results showed that simulated species richness matched well with observed values in the field, showing an evident decrease from meadows to steppes and then to deserts. Summer precipitation, which ranked first among the 20 environmental factors, was further confirmed to be the most critical driver of species richness distribution. Next, we simulated and compared species richness patterns under four different precipitation scenarios, increasing and decreasing summer precipitation by 20% and 10%, relative to the current species richness pattern. Our findings showed that species richness in response to altered precipitation was grassland-type specific, with meadows being sensitive to decreasing precipitation, steppes being sensitive to increasing precipitation, and deserts remaining resistant. In addition, species richness at low elevations was more sensitive to decreasing precipitation than to increasing precipitation, implying that droughts might have stronger influences than wetting on species composition. In contrast, species richness at high elevations (also in deserts) changed slightly under different precipitation scenarios, likely due to harsh physical conditions and small species pools for plant recruitment and survival. Finally, we suggest that policymakers and herdsmen pay more attention to alpine grasslands in central Tibet and at low elevations where species richness is sensitive to precipitation changes
Spectral radius of graphs forbidden or
Let be the graph obtained from a cycle by adding a
new vertex connecting two adjacent vertices in . In this note, we obtain
the graph maximizing the spectral radius among all graphs with size and
containing no subgraph isomorphic to . As a byproduct, we will
show that if the spectral radius , then must
contains all the cycles for unless .Comment: 11 pages, 1 figur
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