1,974 research outputs found
Approximately Hadamard matrices and Riesz bases in random frames
An matrix with entries which acts on as a
scaled isometry is called Hadamard. Such matrices exist in some, but not all
dimensions. Combining number-theoretic and probabilistic tools we construct
matrices with entries which act as approximate scaled isometries in
for all . More precisely, the matrices we construct have
condition numbers bounded by a constant independent of .
Using this construction, we establish a phase transition for the probability
that a random frame contains a Riesz basis. Namely, we show that a random frame
in formed by vectors with independent identically
distributed coordinates having a non-degenerate symmetric distribution contains
many Riesz bases with high probability provided that . On the
other hand, we prove that if the entries are subgaussian, then a random frame
fails to contain a Riesz basis with probability close to whenever , where are constants depending on the distribution of the
entries
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Understanding dark scenes based on multi-modal image data is challenging, as
both the visible and auxiliary modalities provide limited semantic information
for the task. Previous methods focus on fusing the two modalities but neglect
the correlations among semantic classes when minimizing losses to align pixels
with labels, resulting in inaccurate class predictions. To address these
issues, we introduce a supervised multi-modal contrastive learning approach to
increase the semantic discriminability of the learned multi-modal feature
spaces by jointly performing cross-modal and intra-modal contrast under the
supervision of the class correlations. The cross-modal contrast encourages
same-class embeddings from across the two modalities to be closer and pushes
different-class ones apart. The intra-modal contrast forces same-class or
different-class embeddings within each modality to be together or apart. We
validate our approach on a variety of tasks that cover diverse light conditions
and image modalities. Experiments show that our approach can effectively
enhance dark scene understanding based on multi-modal images with limited
semantics by shaping semantic-discriminative feature spaces. Comparisons with
previous methods demonstrate our state-of-the-art performance. Code and
pretrained models are available at https://github.com/palmdong/SMMCL
The Discussion of aoInfanta Problem: The Situation and Trends of Chinese Childrens Animation
The problem Infant in Chinese animation will be analyzed the differences between Chilren s Animation and Infant will be declaired then it will be point out that Childish view in Chinese public opinion is wrong There are shown that the Chinese animation s experiences and trend
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
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